problem solving abilities can be facilitated among students by

Sign up for our free weekly newsletter and receive

top education news, lesson ideas, teaching tips and more!

No thanks, I don't need to stay current on what works in education!

COPYRIGHT 1996-2016 BY EDUCATION WORLD, INC. ALL RIGHTS RESERVED.

COPYRIGHT 1996 - 2024 BY EDUCATION WORLD, INC. ALL RIGHTS RESERVED.

• SchoolNotes.com
• The Educator's Network

The Edvocate

• Lynch Educational Consulting
• Dr. Lynch’s Personal Website
• Write For Us
• The Tech Edvocate Product Guide
• The Edvocate Podcast
• Terms and Conditions
• Privacy Policy
• Assistive Technology
• Best PreK-12 Schools in America
• Child Development
• Classroom Management
• Early Childhood
• EdTech & Innovation
• Education Leadership
• First Year Teachers
• Gifted and Talented Education
• Special Education
• Parental Involvement
• Policy & Reform
• Best Colleges and Universities
• Best College and University Programs
• HBCU’s
• Higher Education EdTech
• Higher Education
• International Education
• The Awards Process
• Finalists and Winners of The 2023 Tech Edvocate Awards
• Award Seals
• GPA Calculator for College
• GPA Calculator for High School
• Cumulative GPA Calculator
• Grade Calculator
• Weighted Grade Calculator
• Final Grade Calculator
• The Tech Edvocate
• AI Powered Personal Tutor

Teaching Students About Organic Form

Teaching students about justinian and theodora, michael williams, u.n.c. reports declines in black and hispanic enrollment, educationusa higher education fair 2024, internationalisation experts debate approaches to war in gaza, campus sustainability, research and teaching excellence, smart space optimization, these are the 2 students and 2 teachers killed at apalachee high school in georgia, strategies and methods to teach students problem solving and critical thinking skills.

The ability to problem solve and think critically are two of the most important skills that PreK-12 students can learn. Why? Because students need these skills to succeed in their academics and in life in general. It allows them to find a solution to issues and complex situations that are thrown there way, even if this is the first time they are faced with the predicament.

Okay, we know that these are essential skills that are also difficult to master. So how can we teach our students problem solve and think critically? I am glad you asked. In this piece will list and discuss strategies and methods that you can use to teach your students to do just that.

• Direct Analogy Method

A method of problem-solving in which a problem is compared to similar problems in nature or other settings, providing solutions that could potentially be applied.

• Attribute Listing

A technique used to encourage creative thinking in which the parts of a subject, problem, or task are listed, and then ways to change those component parts are examined.

• Attribute Modifying

A technique used to encourage creative thinking in which the parts of a subject, problem, or task are listed, and then options for changing or improving each part are considered.

• Attribute Transferring

A technique used to encourage creative thinking in which the parts of a subject, problem or task listed and then the problem solver uses analogies to other contexts to generate and consider potential solutions.

• Morphological Synthesis

A technique used to encourage creative problem solving which extends on attribute transferring. A matrix is created, listing concrete attributes along the x-axis, and the ideas from a second attribute along with the y-axis, yielding a long list of idea combinations.

SCAMPER stands for Substitute, Combine, Adapt, Modify-Magnify-Minify, Put to other uses, and Reverse or Rearrange. It is an idea checklist for solving design problems.

• Direct Analogy

A problem-solving technique in which an individual is asked to consider the ways problems of this type are solved in nature.

• Personal Analogy

A problem-solving technique in which an individual is challenged to become part of the problem to view it from a new perspective and identify possible solutions.

• Fantasy Analogy

A problem-solving process in which participants are asked to consider outlandish, fantastic or bizarre solutions which may lead to original and ground-breaking ideas.

• Symbolic Analogy

A problem-solving technique in which participants are challenged to generate a two-word phrase related to the design problem being considered and that appears self-contradictory. The process of brainstorming this phrase can stimulate design ideas.

• Implementation Charting

An activity in which problem solvers are asked to identify the next steps to implement their creative ideas. This step follows the idea generation stage and the narrowing of ideas to one or more feasible solutions. The process helps participants to view implementation as a viable next step.

• Thinking Skills

Skills aimed at aiding students to be critical, logical, and evaluative thinkers. They include analysis, comparison, classification, synthesis, generalization, discrimination, inference, planning, predicting, and identifying cause-effect relationships.

Can you think of any additional problems solving techniques that teachers use to improve their student’s problem-solving skills?

The 4 Types of Brainstorming

Feeling lethargic it may all that screen ....

Matthew Lynch

Related articles more from author.

4 Factors to Consider about Teaching Jobs and School Reform

Teachers: How to Use Technology to Spruce Up Your Lesson Plans

19 Strategies to Help Students Who Cannot Follow a Routine

Learning How to Teach

Educational Reform: Finding a Solution for a Nation at Risk

Getting the Most Out of Student Teaching Mentorship

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• Front Psychol

Effects of Online Cooperative Learning on Students’ Problem-Solving Ability and Learning Satisfaction

Yi-ping wang.

1 College of International Relations, Huaqiao University, Xiamen, China

2 School of Management, Harbin Institute of Technology (HIT), Harbin, China

Associated Data

The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author.

As technology changes, it is becoming more common in education for students to acquire knowledge from sources other than just their teachers. In the face of a diverse student background, teachers have to make adjustments in their instruction so that students do not simply listen. Student-based educational philosophy aims to combine instructional methods with cooperative learning to allow students to change from passive learning to active knowledge construction, reinduce students’ learning motivation and passion, and enhance students’ self-learning effectiveness. Focusing on college students in Fujian Province as the research sample, 360 copies of a questionnaire were distributed for this study. After deducting invalid and incomplete ones, 298 copies remained, with a retrieval rate 83%. The research results showed significantly positive correlations between online cooperative learning and problem-solving ability, problem-solving ability and learning satisfaction, and online cooperative learning and learning satisfaction. According to the results, it is expected, in the digital era, to integrate information technology into the teaching environment and focus on learning objectives to create teaching software with a user-friendly interface, simple operation, learning process recording, and an interactive learning community in the teaching-learning process to develop the characteristics and effectiveness of digital teaching and learning.

Introduction

As times progress and technology improves, teachers are no longer the only channel for students acquiring knowledge. Students in this generation are stimulated by distinct and diverse cultures to show more active and flexible characters or responses than students before them, and are even brave enough to challenge existing values. Students in a traditional learning model with passive lectures will not concentrate in the classroom. Examinations have been a core part of education for a long time. It is the best time to practice cooperative learning. The curricula show that the ideas such as taking the initiative, engaging in the public, and seeking the common good are important. Engaging in the public and seeking the common good is a result of the characters of positive independence and face-to-face fostering of interactive and interpersonal skills mentioned in cooperative learning. In this respect, it can be stated that cooperative learning guides students to be well and develops various interactive abilities with ego, others, society, and nature. It also helps students in applying and practicing their knowledge, experiencing the meaning of life, being willing to devote to the sustainable development of society, nature, and culture, and seeking reciprocity of each other and common good. Information technologies are material tools that learners should actively and broadly apply to a the positive interaction channel between oneself and the environment to effectively engage the public with others and the environment ( Li et al., 2021 ).

In the face of diverse student background, teachers have to make adjustments in their instruction to stop students from simply listening. Educational philosophy should be student-based to promote each student’s thinking. In this case, cooperative learning allows students to change from passive learning into active knowledge construction, could reinduce students’ learning motivation and passion, and enhance students’ self-learning effectiveness. Most students are digital natives born after 1980, while most of their teachers are digital immigrants and even “digital refugees” escaping from technologies and being afraid of new knowledge. The overlap between such two generations is limited, meaning that their values and morality are distinct. Modern students are digital natives able to use mobile phones, televisions, computers, laptops, and tablets since childhood, and highly dependent on new technologies. Information-technology-integrated instruction with multimedia equipment and materials means teaching and learning is no longer restricted to dictation and paper-and-pencil ( Vaz et al., 2021 ); the class climate has changed to cooperative learning. The operation of cooperative learning is smoother through information technology, and a communication and interaction bridge can be built through information technology so that cooperative learning could cultivate students’ problem-solving ability to further promote learning satisfaction. As a result, the effects of online cooperative learning on students’ problem-solving ability and learning satisfaction are discussed in this study, expecting to integrate information technology into the teaching environment in the digital era, focus on learning objectives based on learning theory, have teaching software with a user-friendly interface, simple operation, learning process recording, and an interactive learning community in the teaching-learning process to develop the characteristics and effectiveness of digital teaching and learning.

Literature Review and Hypothesis

Constructivists regard gaining knowledge as a comprehensive and reflective thinking activity through students’ independent exploration and observation and highly praise learner-centered learning environments. Teachers’ roles of propagating the doctrine, imparting professional knowledge, and resolving doubts change into knowledge building facilitators. The superordinate-subordinate relationship of “Learning from Teacher” is changed into the equal relationship of “Learning with Teacher.” The learning perspective of constructivism facilitates the development of current learning technology ( Cortez et al., 2021 ).

Dozens of instructional strategies are developed for cooperative learning, and each grouping method presents the characteristics and applicable teaching situation. Teachers could flexibly apply the difference according to instructional objectives, student characteristics, and course attributes. Researchers, in the interview with collaborative teachers, also reveal not being restricted into a grouping method, but extracting the advantages of various methods, and making flexible adjustments in consideration of teachers’ personality traits and class attributes and characteristics ( Akdemir et al., 2020 ). Major cooperative learning strategies are classified into three types, including one suitable for leading sharing and discussion among students, another for assisting students in mastering learning content, and the last for leading teams for theme-based inquiry. Each type shows various strategies to cope with different teaching styles, or more than two strategies could be changed and applied depending on the demands ( Hafeez, 2021 ).

Li and Keller (2018) mentioned the significant effects of using web problem-based cooperative learning and on the problem-solving skills of the children. The results revealed the better performance of students compared to traditional problem-based learning. Del Gaudio et al. (2021) used online cooperative learning to discover the advantages and strengths, solve problems according to collaborative interaction, comprehend the roles, integrate the discussed ideas, clearly master the tasks, coordinate the allocation of team members’ reports, complete reports according to previous discussion results, discuss and modify successive measures together, inspect cooperation results, track back problem-solving processes, and reflect team organization and roles, problem-solving ability as to independently complete tasks with high-level thinking, and cooperative problem-solving ability as to create the value of synergy, solve problems and complete tasks together, and create good performance beyond the expectation ( Wu et al., 2019 , 2022 ). Ingrid (2019) explained that independent thinking and analysis ability allowed dealing with daily life and even life problems. Teachers applying information technology to cooperative learning to enrich students’ life experience, being good at asking questions, creating problem-solving teaching situations, applying technological tools to speculate and deduce problems, effectively solving problems with cooperative discussions, and enhancing adaptability to life could help students become problem-solving experts. For this reason, the following hypothesis is established in this study.

H1 : Online cooperative learning presents significantly positive correlations with problem-solving ability. H1-1 : Online cooperative learning shows significantly positive correlations with problem-solving ability. H1-2 : Online cooperative learning reveals remarkably negative correlations with problems-solving ability.

Oates and Ritók (2018) explained that learners being able to effectively enhance their problem-solving ability after going through the curriculum arranged by the school, course content of teachers, and effective promotion of knowledge acquisition in the learning process, with consistent expectation and anticipation, would appear satisfactory; on the contrary, dissatisfaction would be delivered. Metin-Orta and Demirtepe-Saygılı (2021) stated that education aimed to help individuals live their life; in real situations, an individual using critical thinking to solve complicated and messy dilemmas and problems was the core task of modern education. Teachers in the teaching process did not simply transmit knowledge, provide guidance for study, and dispel confusion, but had to help students associate old experience with new knowledge to further solve problems through tight cognition structure to form meaningful learning in order to effectively enhance learning satisfaction. Wu et al. (2021) regarded cooperative problem-solving ability as an individual with sufficient ability communicating and dialoging with more than two companions to share knowledge and skills, collaboratively and effectively participate in an activity, and develop teamwork ability to solve problems. Collaborative problem solving referred to several partners collaboratively completing a task where each partner had to positively participate ( Chiao and MacVaugh, 2021 ; Min et al., 2021 ), mutually coordinate, and pull together to solve problems in the task with teamwork so as to effectively enhance learning satisfaction. Accordingly, the following hypothesis is establishment in this study.

H2 : Problem-solving ability shows remarkably positive correlations with learning satisfaction. H2-1 : Problem-solving ability appears to have notably positive correlations with learning satisfaction. H2-2 : Problem-solving ability presents significantly negative correlations with learning satisfaction.

Wu et al. (2020) applied interactive APP to analyze learning satisfaction with idiom teaching; the students, regardless of gender and learning achievement, were satisfied with the use of interactive APP for idiom learning. The use of information-technology-integrated cooperative learning for the learning achievement of students in the experimental group did not outperform students in the control group, but the learning satisfaction was better than those in the control group. Kurilovas and Kubilinskiene (2020) mentioned that students in the experimental group with cooperative learning outperformed students with general cooperative learning on learning achievement and learning attitude and presented positive learning satisfaction. Haidar and Fang (2019) explained cooperative learning as teachers effectively applying information technology to smooth cooperative learning; for instance, dynamic information materials and real-time team performance could assist in students’ learning motivation, learning ambition, learning satisfaction, and learning effectiveness and create a quality learning environment with peer teamwork and teacher-student interaction. The following hypothesis is therefore established in this study.

H3 : online cooperative learning reveals notably positive correlations with learning satisfaction. H3-1 : Online cooperative learning shows remarkably positive correlations with learning satisfaction. H3-2 : Online cooperative learning reveals notably negative correlations with learning satisfaction.

Methodology

Operational definition, online cooperative learning.

Online cooperative learning, as the independent variable in this study, is measured with positive interdependence, promotive interaction, social skills, and group processing, according to the blended learning model proposed by Liao et al. (2019) .

• Positive interdependence: mutual dependence, mutual responsibility, mutual help, acceptance of assistance, and cheering up team members.
• Promotive interaction: mutual assistance, sharing information, and providing clear explanation in the team.
• Social skills: leadership and communication.
• Group processing: evaluating the cooperation effectiveness of each other.

Problem-Solving Ability

Problem-solving ability, as the dependent variable in this study, is measured with exploration and comprehension, planning and execution, and monitoring and reflection, according to the problem-solving ability model proposed by Lin et al. (2018) .

Learning Satisfaction

Learning satisfaction, as the dependent variable in this study, is measured with student aspects, teacher aspects, and school aspect, according to the blended learning model proposed by Travis and Bunde (2020) .

• Student aspects: including students’ interests, learning motivation, learning attitude, personality traits, gender, needs, experience, learning ability, learning effectiveness, and peer interpersonal relationship.
• Teacher aspects: covering teachers’ professional ability, traits, teaching methods, curriculum arrangement, teaching content, difficulty in material design, attitude towards students, and teacher-student interaction model.
• School aspects: containing school equipment, learning environment, environmental safety and health, teaching resources, and transportation.

Research Object and Analysis Method

College students in Fujian Province, as the research sample, were distributed 360 copies of a questionnaire for this study. After deducting invalid and incomplete ones, 298 copies were valid, with a retrieval rate 83%. After confirming the applicable online cooperative learning strategy, the actual teaching activity is practiced as planned. Four teachers practicing cooperative learning in the school were invited as the collaborative teachers to deliver the 10-week (total 50 sessions) teaching activity to 500 students in 10 classes of a university in Fujian Province. The questionnaire data collection is preceded after the end of the course.

Two-stage analysis in Structural Equation Modeling (SEM) is applied to analyze goodness-of-fit and test the model in this study. Confirmatory Factor Analysis (CFA) is first used, aiming to test the existence of independent variables in the model in order to delete dependent variables with bad effects on causal analysis. Path analysis is then preceded after the modification. Path analysis aims to estimate the relationship of model paths among variables. Without Confirmatory Factor Analysis to test independent variables, the use of path analysis might be affected by independent variables to result in bad goodness-of-fit or insignificant model paths. Goodness-of-fit test in Amos18.0 is utilized in this study. CMIN/DF of the measurement result being smaller than 5 is acceptable and being smaller than 3 is excellent; GFI, AGFI, NFI, IFI, TLI, and CFI are better higher than 0.9; and RMR, RMSEA, and SRMR are better when smaller and ideally smaller than 0.05.

Factor Analysis

The online cooperative learning scale in this study, with factor analysis, extracted four factors of “positive interdependence” (eigenvalue = 2.633, α  = 0.84), “promotive interaction” (eigenvalue = 1.875, α  = 0.86), “social skills” (eigenvalue = 2.236, α  = 0.81), and “group processing” (eigenvalue = 1.633, α  = 0.87). The cumulative covariance explained achieves 75.923%. The problem-solving ability scale, after factor analysis, extracted three factors of “exploration and comprehension” (eigenvalue = 3.251, α  = 0.86), “planning and execution” (eigenvalue = 2.407, α  = 0.88), and “monitoring and reflection” (eigenvalue = 2.716, α  = 0.83). The cumulative covariance explained reaches 77.493%. The learning satisfaction scale, with factor analysis, extracted three factors of “student aspects” (eigenvalue = 1.577, α  = 0.80), “teacher aspects” (eigenvalue = 2.281, α  = 0.85), and “school aspects” (eigenvalue = 2.388, α  = 0.90). The cumulative covariance explained achieves 80.762%.

Empirical Analysis Model of Structural Equation

Regarding the Confirmatory Factor Analysis (CFA) results, the convergent validity of the observation model could observe the reliability of individual observed variable, construct reliability (CR), and average variance extracted (AVE); the reliability of individual observed variable is better higher than 0.5. The factor loadings of observed items in this study are higher than the suggested value. The construct reliability is better higher than 0.6, while other researchers suggest higher than 0.5 being acceptable. The model calibration results reveal the construct reliability higher than 0.5. Average variance extracted is suggested higher than 0.5; the average variance extracted of the dimensions in this study is higher than 0.5, conforming to the suggested value.

In terms of the structural formula calibration results, χ 2 / df , RMSEA, GFI, AGFI, RMR, and NFI are suggested to be ≦5, ≦0.08, ≧0.9, ≧0.9, ≦0.05, and ≧0.9, respectively. This study shows χ 2 / df  = 3.142≦5, RMSEA = 0.032≦0.08, GFI = 0.967≧0.9, AGFI = 0.934≧0.9, RMR = 0.031≦0.05, and NFI = 0.918≧0.9, revealing good overall model fit. Under good overall model fit, the structural formula parameter calibration results are shown in Table 1 and Figure 1 . The research results present online cooperative learning → problem-solving ability 0.327 *** that H1 is supported, problem-solving ability → learning satisfaction 0.423 *** that H2 is supported, and online cooperative learning → learning satisfaction 0.386 *** that H3 is supported.

Structural equation modeling result.

Model path diagram. *** p  < 0.001.

The research results prove that, in the practice of online cooperative learning, information technology makes up for the insufficiency of cooperative learning, enriches courses, promotes students’ learning motivation, and drives learning effectiveness to form a positive cycle. Students’ learning motivation comes from the advancement of performance and the learning confidence comes from the ideal performance. Teachers use online cooperative learning to facilitate group discussion skills and the understanding of students. They also use Google Forms to conduct digitalized tests, and mind maps and tables to improve students’ problem-solving skills ( Simamora, 2017 ). In the teaching-learning process, instructional objectives are inspected to return the teaching profession. Teachers are good at asking questions to enhance students’ cooperation and encourage thinking. Especially in comprehension and analysis, the top-down relationship should be broken and the subjective consideration of teachers’ cognition, ideas, and interpretation as being better than students should be avoided so that it would not come out with teachers’ expected answers ( Phillips et al., 2014 ). Students’ answers could be typed with computers to respect the answers, enhance the confidence without losing students’ creativity, and present brainstorming; teachers ensure the focus and integration at the end. The application of online cooperative learning could reconstruct teachers’ teaching profession, and the experience and constant rolling correction could improve teaching skills to face changeable students and present the value of online cooperative learning. The intervention of information technology could change the resistance to the online cooperative learning process into assistance, helping it to become a powerful backup force of online cooperative learning, induce learning motivation, and promote problem-solving ability and learning satisfaction as the final instructional objectives.

Alves et al. (2019) explained collaborative problem solving as an individual or more than two companions with sufficient capability sharing knowledge and skills through communication and dialogue, collaboratively and effectively participating in activities, and developing teamwork to solve encountered problems. Collaborative problem solving referred to a task being collaboratively completed by several partners. Each partner had to positively participate, mutually coordinate, and help each other in the same situation to solve problems with teamwork so as to effectively enhance learning satisfaction. The intervention of information technology could make the best out of a bad situation in the online cooperative learning process to support online cooperative learning, induce learning motivation, and promote problem solving capability and learning satisfaction as the ultimate instructional objectives. The research result conforms to the points of view proposed by Munawar and Chaudhary (2019) and Haidar and Fang (2019) .

Teachers need full training to guide students with “stretching and jumping” opportunities in the “interactive relationship.” Meanwhile, teachers need full wisdom to help students move from conflict compromise to positive trust ( Ramdani et al., 2019 ). What is more, multiple evaluations outside the classroom, such as completion of team assignments, quiz performances, and sectional examination performance, help teams not to slack. Besides, each member is important that no-one is confident of the winning ( Hafeez, 2021 ). Students would search network data, discuss grounded arguments, focus on discussion through information technology, and save a lot of time for groupwork. Teachers, with statistics, would announce team performance with data at any time to induce competition and crisis awareness of teams. There might be conflict in a team, but a contest with multiple evaluations allows individuals to give up personal prejudice and unite to make effort for the team. It naturally reinforces the group process of cooperative learning ( Akdemir et al., 2020 ).

The research results show that the item of “ Teachers currently use the instructional method of online cooperative learning to make courses interesting and active ” receives the highest score in online cooperative learning strategies, revealing the acquisition of student identity. The item of “ I think the use of platform[s] for Internet communication media could help the communication and teamwork between team members and I in the cooperative learning course ” receives the highest score in problem solving capability, revealing the acquisition of student identity. The item of “ I think the application of online cooperative learning could enhance learning ability and confidence ” receives the highest score in learning satisfaction, revealing the acquisition of student identity.

The research results prove that students’ responses in class are a mirror reminding teachers of the need to adjust the instructional methods. In traditional didactic instruction, students’ academic achievement decides teachers’ success. In the use of online cooperative learning, students’ learning motivation awakes teachers’ passion. Teachers could continuously retain the original instructional methods; nevertheless, modern students are active and there are special students who are extroverts or introverts. These students may challenge teachers’ authority. Teachers can easily get tired if they do not adapt their instructional methods according to the diverse needs of students. The assistance of information technology in the practice allows seeking consensus from online resources in the team discussion. Under the situation with a well-grounded argument, students are convinced by each other to contribute to the successive discussions. The research result conforms to the points of view proposed by Weaver et al. (2019) and Ingrid (2019) . With online cooperative learning, teachers simply combine the original computer software with cooperative learning courses through the Internet, rather than re-learning brand new and strange computer software. Teachers who enjoy learning and self-growth could challenge themselves and activate teaching with advanced functions. However, it should be kept in mind that information technologies are only tools; using media can attract students’ attention in a short period, but having students internalize knowledge is the goal. Karakus Taysi (2019) mentioned the aims of education as helping individuals live their life. The development of individual critical thinking and problem-solving skills are the main aims of contemporary education. Teachers did not simply propagate the doctrine, impart professional knowledge, and resolve doubts in the teaching process, but had to help students link old experience with new knowledge, make tight cognitive structures for meaningful learning, and further solve problems to effectively promote learning satisfaction.

Online cooperation learning method is important for cultivating students’ independent thinking, interpersonal communication, competition awareness, and teamwork ( Cortez et al., 2021 ). Teachers and students are good at utilizing information technology to have students focus on discussion content and direction, instantaneously acquire the answers and feedback and correction, and improve team performance with data ( Mutua and Ong'ong'a, 2020 ). When making effort in the learning process, the learning result would not be lower than the expected performance and students would reflect this with their learning satisfaction.

Data Availability Statement

Ethics statement.

This study was reviewed and approved by the ethics committee of the Huaqiao University. Written informed consent was obtained from all participants for their participation in this study.

Author Contributions

Y-PW performed the initial analyses and wrote the manuscript. T-JW assisted in the data collection and data analysis. All authors revised and approved the submitted version of the manuscript.

This research was supported by the National Natural Science Foundation of China (71702059).

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as potential conflicts of interest.

Publisher’s Note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

• Akdemir Ö., Biçer D., Parmaksız R. Ş. (2020). Information and communications technology metaphors . Mediterr J. Soc. Behav Res 4 , 11–18. doi: 10.30935/mjosbr/9596, PMID: [ CrossRef ] [ Google Scholar ]
• Alves N. D. C., Von Wangenheim C. G., Hauck J. C. (2019). Approaches to Assess Computational Thinking Competences Based on Code Analysis in K-12 Education: A Systematic Mapping Study . Inform Educ. 18 , 17–39. doi: 10.15388/infedu.2019.02, PMID: [ CrossRef ] [ Google Scholar ]
• Chiao B., MacVaugh J. (2021). Open innovation and organizational features: An experimental investigation . Eur. Manag. J. 39 , 376–389. doi: 10.1016/j.emj.2020.12.004 [ CrossRef ] [ Google Scholar ]
• Cortez C. P., Patricio C. S., Uriarte W. N., Jr. (2021). Teachers at lock-Down: generalized anxiety disorder and attitude towards E-learning amidst COVID-19 era . Eur J interact multi Educ 2 :e02113. doi: 10.30935/ejimed/11288 [ CrossRef ] [ Google Scholar ]
• Del Gaudio B. L., Porzio C., Sampagnaro G., Verdoliva V. (2021). How do mobile, internet and ICT diffusion affect the banking industry? An empirical analysis. Eur. Manag. J. 39 , 327–332. doi: 10.1016/j.emj.2020.07.003 [ CrossRef ] [ Google Scholar ]
• Hafeez M. (2021). Impact of Teacher’s training on interest and academic achievements of students by multiple teaching methods . Pedagogical Res 6 :em0102. doi: 10.29333/pr/11088 [ CrossRef ] [ Google Scholar ]
• Haidar S., Fang F. (2019). English language in education and globalization: A comparative analysis of the role of English in Pakistan and China . Asia Pacific J. Educ. 39 , 165–176. doi: 10.1080/02188791.2019.1569892 [ CrossRef ] [ Google Scholar ]
• Ingrid I. (2019). The effect of peer collaboration-based learning on enhancing English oral communication proficiency in MICE . J. Hosp. Leis. Sport Tour. Educ. 24 , 38–49. doi: 10.1016/j.jhlste.2018.10.006 [ CrossRef ] [ Google Scholar ]
• Karakus Taysi E. (2019). The effect of listening attitude and listening anxiety on listening comprehension: A regression model . Univ. J. Educ. Res. 7 , 356–364. doi: 10.13189/ujer.2019.070207 [ CrossRef ] [ Google Scholar ]
• Kurilovas E., Kubilinskiene S. (2020). Lithuanian case study on evaluating suitability, acceptance and use of IT tools by students–An example of applying technology enhanced learning research methods in higher education . Comput. Hum. Behav. 107 :106274. doi: 10.1016/j.chb.2020.106274 [ CrossRef ] [ Google Scholar ]
• Li K., Keller J. M. (2018). Use of the ARCS model in education: A literature review . Comput. Educ. 122 , 54–62. doi: 10.1016/j.compedu.2018.03.019, PMID: [ CrossRef ] [ Google Scholar ]
• Li M., Yin D., Qiu H., Bai B. (2021). A systematic review of AI technology-based service encounters: implications for hospitality and tourism operations . Int. J. Hosp. Manag. 95 :102930. doi: 10.1016/j.ijhm.2021.102930 [ CrossRef ] [ Google Scholar ]
• Liao H. C., Li Y. C., Wang Y. H. (2019). Optimal cooperative learning grouping to improve medical university students’ English competencies . SAGE Open 9 :2158244019861454. doi: 10.1177/2158244019861454 [ CrossRef ] [ Google Scholar ]
• Lin Y. H., Hsu Y. L., Lin Y. L., Hsiao W. L. (2018). An action research of nature science education in junior high schools focusing on influence of play-oriented cooperative learning . Int J Organ Innov 10 , 226–244. [ Google Scholar ]
• Metin-Orta I., Demirtepe-Saygılı D. (2021). Cyberloafing behaviors among university students: their relationships with positive and negative affect . Curr. Psychol. 1–14. doi: 10.1007/s12144-021-02374-3, PMID: [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Min J., Kim H., Yoo J. (2021). Effects of self-esteem, problem-solving ability, and professional nursing values on the career identity of nursing college students in South Korea: A cross-sectional study . Int. J. Environ. Res. Public Health 18 :8531. doi: 10.3390/ijerph18168531, PMID: [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Munawar S., Chaudhary A. H. (2019). Effect of cooperative learning on the writing skill at elementary level in the subject of English . Bull. Educ. Res. 41 , 35–44. [ Google Scholar ]
• Mutua S. N., Ong’ong’a D. O. (2020). Online news media framing of COVID-19 pandemic: probing the initial phases of the disease outbreak in international media . Eur J Interact Multi Educ 1 :e02006. doi: 10.30935/ejimed/8402 [ CrossRef ] [ Google Scholar ]
• Oates J., Ritók N. I. (2018). The Role of Art Education in Children’s Development . NY, USA: Young Children’s Play and Creativity. [ Google Scholar ]
• Phillips B. N., Fleming A. R., Kaseroff A., Huck G. E. (2014). Work-related social skills: definitions and interventions in public vocational rehabilitation . Rehabil. Psychol. 59 , 386–398. doi: 10.1037/rep0000011, PMID: [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Ramdani R., Syamsuddin A., Sirajuddin S. (2019). Development of mathematical module-problem solving approach to train Student’s reflective thinking . Pedagogical Res 4 :em0040. doi: 10.29333/pr/5861 [ CrossRef ] [ Google Scholar ]
• Simamora E. W. (2017). The effect of student team achievement division cooperative learning on The concept understanding ability of mathematic advances in social science . Educ Humanit Res 104 , 407–411. doi: 10.2991/aisteel-17.2017.87 [ CrossRef ] [ Google Scholar ]
• Travis J., Bunde J. (2020). Self-regulation in college: The influence of self-efficacy, need satisfaction, and stress on GPA, persistence, and satisfaction . Curr. Psychol. 1–11. doi: 10.1007/s12144-020-01091-7 [ CrossRef ] [ Google Scholar ]
• Vaz A. L. A., Ferreira F. A., Pereira L. F., Correia R. J., Banaitis A. (2021). Strategic visualization: the (real) usefulness of cognitive mapping in smart city conceptualization . Manag. Decis. 60 , 916–939. doi: 10.1108/MD-11-2020-1512 [ CrossRef ] [ Google Scholar ]
• Weaver R. H., Naar J. J., Jarrott S. E. (2019). Using contact theory to assess staff perspectives on training initiatives of an intergenerational programming intervention . The Gerontologist 59 , 770–779. doi: 10.1093/geront/gnx194, PMID: [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Wu T. J., Xu T., Li L. Q., Yuan K. S. (2020). “Touching with heart, reasoning by truth!” The impact of brand cues on mini-film advertising effect . Int. J. Advert. 39 , 1322–1350. doi: 10.1080/02650487.2020.1755184 [ CrossRef ] [ Google Scholar ]
• Wu T. J., Yuan K. S., Yen D. C. (2021). Leader-member exchange, turnover intention and presenteeism–the moderated mediating effect of perceived organizational support . Curr. Psychol. 1–12. doi: 10.1007/s12144-021-01825-1, PMID: [ CrossRef ] [ Google Scholar ]
• Wu T. J., Yuan K. S., Yen D. C., Xu T. (2019). Building up resources in the relationship between work–family conflict and burnout among firefighters: moderators of guanxi and emotion regulation strategies . Eur. J. Work. Organ. Psychol. 28 , 430–441. doi: 10.1080/1359432X.2019.1596081 [ CrossRef ] [ Google Scholar ]
• Wu T. J., Yuan K. S., Yen D. C., Yeh C. F. (2022). The effects of JDC model on burnout and work engagement: A multiple interaction analysis . Eur. Manag. J. doi: 10.1016/j.emj.2022.02.001 [ CrossRef ] [ Google Scholar ]

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

• View all journals
• Explore content
• About the journal
• Publish with us
• Sign up for alerts
• Review Article
• Open access
• Published: 24 November 2023

Exploring learning outcomes, communication, anxiety, and motivation in learning communities: a systematic review

• Wenwen Cao 1 &
• Zhonggen Yu   ORCID: orcid.org/0000-0002-3873-980X 2  

Humanities and Social Sciences Communications volume  10 , Article number:  866 ( 2023 ) Cite this article

2569 Accesses

3 Citations

Metrics details

• Language and linguistics

Learning communities have become a focal point of research due to their potential impact on learning outcomes, motivation, and communication. These factors are recognized as crucial determinants of the effectiveness of learning communities. To guide this study, a thorough review of 35 relevant studies was conducted, employing rigorous inclusion and exclusion criteria based on the PRISMA framework to ensure a systematic and robust approach. The findings of this study indicated that learning communities possess the capacity to enhance communication, motivation, and learning outcomes, while simultaneously alleviating learner anxiety. Specifically, it was observed that well-designed online learning communities can significantly improve learning outcomes. Furthermore, the utilization of online technologies within these communities can facilitate enhanced communication, leading to improved learning outcomes. Moreover, this study offers a range of recommendations for optimizing learning outcomes through the implementation of learning communities. These recommendations serve as valuable guidance for harnessing the full potential of learning communities to achieve educational goals. In conclusion, this study underscores the importance of learning communities in enhancing learning outcomes, motivation, and communication. It highlights the efficacy of appropriately designed online communities and the integration of technology in fostering effective communication and improving learning outcomes. The study contributes important insights into ways of maximizing the benefits of learning communities in promoting educational success.

Similar content being viewed by others

Exploring students’ beliefs about web-based collaborative learning and their practices: a qualitative case study of university English-as-a-foreign-language readers

The science of effective learning with spacing and retrieval practice

Real-world effectiveness of a social-psychological intervention translated from controlled trials to classrooms

Introduction.

In recent years, there has been a growing interest in both offline and online learning communities, which consist of professionals, shared goals, facilitators, and mechanisms, as well as the interconnectedness among these elements. These learning communities have shown potential in enhancing leadership, organization, and the ability to tackle various challenges (Wen and Zhang, 2020 ). Consequently, scholars have increasingly focused on investigating the impacts of learning communities on learning outcomes, motivation, and communication (Magana et al., 2021 ). These factors are considered important indicators of the effectiveness of learning communities. Notably, motivation and learning outcomes can be positively influenced through communication within learning communities. This is because strong motivation, coupled with frequent communication, facilitates intensive engagement with new knowledge and innovative information, consequently enhancing knowledge acquisition.

Anxiety plays a crucial role in learning communities and can impede effective communication and learning outcomes. Within a learning community, learners often face challenges related to imbalances in communication abilities and anxiety levels between experts and novices (Young et al., 2018 ). Novice learners may experience apprehension and reluctance to ask questions, leading to their withdrawal from active participation in learning activities within the community. Additionally, the dominance of experts within the learning community may exert pressure on other community members, hindering effective communication between teachers and learners. Particularly, anxiety, primarily experienced by novice learners, can have a detrimental impact on learning outcomes. The presence of anxiety can significantly influence learning outcomes, communication, and motivation within learning communities. Accordingly, this study aims to examine the role of anxiety and propose strategies to alleviate anxiety levels within learning communities.

This study complements the missing links in the scientific literature in the field of learning communities. Several academic studies have examined the efficacy of learning communities in the field of physical education (Parker et al., 2022 ), analyzed the impact of learning communities on online learning outcomes, and investigated the integration of learning communities with social networks in educational settings. Blayone et al. ( 2017 ) conducted research specifically on the influence of online learning communities on learning outcomes, while Schechter ( 2010 ) explored the role of social networks in educational contexts. Scanty review studies have synthesized the effects of learning communities on learning outcomes, communication, anxiety, and motivation. It aims to understand how communication, anxiety levels, and motivation impact students’ learning outcomes in a community-based educational setting. The objective is to gather data on these variables and analyze the findings to provide insights on how to enhance learning experiences and outcomes within these communities. This systematic review study is meaningful and necessary since it aims to fill the research gap by identifying community-based learning outcomes, communication, anxiety, and motivation. The specific research questions are: (1) Can learning communities improve learners’ communication? (2) Can learning communities improve learners’ motivation? (3) Can learning communities mitigate learners’ anxiety? and (4) How to improve learning outcomes through learning communities?

Theoretical framework

Activity Theory is a theoretical framework that originated in the field of psychology and has gained prominence in various disciplines such as education, sociology, and human–computer interaction (Sukirman and Kabilan, 2023 ). It provides a lens to analyze and understand human actions within a social context. According to Activity Theory, human activities are not isolated events but are influenced by, and also influence, the social, cultural, and historical factors in which they occur. This theoretical perspective emphasizes that humans are active agents who engage in purposeful activities to achieve specific goals. Activities are seen as complex systems comprising multiple interconnected elements, including the subject (the individual or group engaged in the activity), the object (the goal or purpose of the activity), the tools or artifacts used, the rules and norms governing the activity, the community or social setting in which the activity takes place, and the division of labor among participants.

Based on the activity theory, learning communities were conducive to language learning outcomes. Activity theory attempted to explore human–computer interactions based on the conception that a specific Activity could exert an influence on thinking, learning goals, reasons for doing, ways of doing, and learning methods. Activity theory provides a foundation for learning communities (Engeström, 2001 ). In a learning community, an individual activity could influence aspects of others. The positive or negative learning activity could exert a positive or negative influence on others’ learning behaviors. It is thus important for community members to establish a model of positive activities to positively influence other language learners in the community.

Leading activities, community guidelines, and organized divisions of work could improve language learning effectiveness and inspire language learners and teachers (Isbell, 2018 ). In a learning community, teachers and designers could select learners who were actively engaged in learning activities and set them up as examples to be followed by other learners. Teachers and designers could also specify community guidelines to direct community members to appropriate learning directions and guide them to achieve success in language learning. Teachers could also organize learning activities and divide members into different teams where individuals assumed different responsibilities. In this way, teachers could improve members’ language learning effectiveness and stimulate other members’ learning enthusiasm.

The interplay between anxiety, communication, motivation, and learning outcomes within learning communities is a complex and dynamic process that can significantly impact the effectiveness of the educational experience. Anxiety can hinder effective communication and dampen motivation, ultimately impacting learning outcomes. On the other hand, positive communication can enhance motivation and learning outcomes, and intrinsic motivation supports effective communication and improved learning outcomes. Understanding these intricate dynamics can inform educators and policymakers in creating supportive learning environments that foster effective communication, reduce anxiety, and enhance motivation, leading to improved learning outcomes in learning communities.

Literature review

Definition of learning community.

To define learning community, it is valuable to refer to the works of Wenger-Trayner and Wenger-Trayner ( 2015 ) and Wenger ( 1998 ). These studies provide insights into the concept of learning communities. According to Wenger-Trayner and Wenger-Trayner ( 2015 ) and Wenger ( 1998 ), a learning community can be understood as a collective of individuals who share a common interest, engage in joint activities, and collaborate in a meaningful manner to enhance their learning and knowledge. It is characterized by mutual engagement, shared values, and a sense of belonging.

In a learning community, individuals come together to pursue their common goals, exchange ideas, and challenge one another intellectually. They often engage in regular interactions, such as discussions, collaborative projects, and sharing resources. Through these interactions, members of the learning community develop relationships, build trust, and establish a supportive environment that fosters continuous learning and development. The learning community is not restricted to a formal educational setting but can be found in various contexts, including workplaces, online platforms, or other social spaces. It transcends traditional hierarchical structures and encourages participation from individuals at different levels of expertise. Within a learning community, newcomers are welcomed and supported in their learning journey, while experienced members serve as mentors or facilitators.

Central to the concept of a learning community is the notion of a “community of practice” as described by Wenger ( 1998 ). A community of practice refers to a group of individuals who share a domain of knowledge or field of practice and jointly learn through their interactions. Members of a community of practice engage in collective learning, negotiation of meaning, and the development of shared resources and practices. Drawing from Wenger-Trayner and Wenger-Trayner ( 2015 ) and Wenger ( 1998 ), a learning community can be defined as a social group of individuals who come together to pursue a common interest, engage in joint activities, and collaborate in a meaningful manner to enhance their learning and knowledge. It is characterized by mutual engagement, shared values, and a supportive environment that fosters continuous learning and development.

Communication

Learning communities have the potential to enhance learning outcomes through improved communication. Online learning communities offer teachers the chance to engage in activities related to English language teaching, enabling students to acquire language knowledge and engage in meaningful communication with their instructors (Pagan et al., 2020 ). Moreover, these communities provide teachers with a wealth of resources to adequately prepare for their teaching responsibilities. By connecting students and teachers from diverse social, cultural, and educational backgrounds, learning communities facilitate the exchange of suggestions, feedback, and mutual learning (Pagan et al., 2020 ). Consequently, online learning communities offer students living in isolated areas the opportunity to communicate with their teachers and interact with their peers, while teachers can employ flexible instructional approaches through online communicative technologies (Salazar, 2011 ).

Drawing upon the constructive attributes of learning communities, they serve as significant platforms for effective communication between school management, English language learners, and other stakeholders involved. These collaborative communities foster communication to redress inequities encountered by English language learners and also shed light on the dynamic interplay among schools, teachers, and students (Brooks et al., 2010 ). Therefore, by leveraging the benefits of learning communities, such as enhanced communication channels and the exchange of ideas, feedback, and resources, there is potential for improved learning outcomes for both teachers and students. Researchers thus propose the following research question:

RQ1. Can learning communities improve learners’ communication?

Learning communities play a crucial role in improving learning outcomes by enhancing motivation. By creating a supportive and engaging environment, learning communities can motivate and activate students, while also fostering teachers’ professional development and shaping students’ perceptions within meaningful contexts (Pagan et al., 2020 ). In the context of learning Chinese as a foreign language, online learning communities have been found to effectively motivate students to engage in language learning (Cai and Zhu, 2012 ). In the case of Vietnamese students, who have limited opportunities to practice English oral skills, their motivation and interest in oral practice tend to be low. However, the use of social media within learning communities can bridge the gap between text-based learning and oral skills practice. Through the assistance of learning communities facilitated by social media platforms, students can engage in socio-cultural interactions and actively practice their oral English skills. This is made possible due to the easy accessibility, flexible schedules shared resources, and collaborative attributes of learning communities (Duong and Pham, 2022 ). In general, learning communities hold the potential to foster desire and motivation within online or distance learning contexts. By providing a supportive and interactive environment, these communities play a vital role in enhancing engagement and motivation among learners.

Although learning communities for English teachers have shown potential in enhancing language learners’ communication and motivation, there are still discrepancies and contradictions between researchers and teachers, theoretical frameworks and practical implementation, and the integration of innovative designs and pedagogical practices. Within learning communities, teachers have the opportunity to raise pertinent questions, observe learners’ behaviors, analyze academic issues, propose inquiries, implement teaching strategies, reflect on their instructional practices, and address challenging problems (Yan and Yang, 2019 ). However, the extent to which teachers can effectively improve students’ communication skills and motivation within a learning community remains uncertain. Consequently, researchers have put forth the following research inquiries to explore this matter:

RQ2. Can learning communities improve learners’ motivation?

Learning communities have the potential to enhance learners’ interactions and alleviate their feelings of anxiety. Specifically, it has been observed that learning communities can improve the interactions among learners in a relaxed and informal setting, resulting in a reduction in learning anxiety among international students who are married to individuals residing in the United States (Grimm et al., 2019 ). By engaging in cooperative and interactive activities within a learning community, members are able to effectively address misunderstandings and misconceptions commonly encountered in foreign language education (Zhang, 2016 ). These close interactions redirect learners’ focus toward learning activities, thereby reducing psychological stress and increasing overall satisfaction. Furthermore, participation in a learning community allows learners and teachers to share a wealth of learning resources, which facilitates easy accessibility to materials and diminishes anxiety arising from concerns about making trivial mistakes or experiencing a lack of proficiency. Interactions with peers and instructors also aid in rectifying any misconceptions regarding key concepts. However, the effects of learning communities on interactions and anxiety have not been thoroughly explored through systematic review studies. Consequently, the following research questions have been proposed by the researchers undertaking this study:

RQ3. Can learning communities mitigate learners’ anxiety?

Learning outcomes.

The establishment of virtual learning communities through the development of online learning platforms presents a promising approach to enhancing learning outcomes. One such example is the virtual intercultural avenues (VIA) program, which leverages social media, serious games, and other educational technologies to facilitate both online and physical learning and teaching within a learning community (Ren et al., 2016 ). Furthermore, the Discussion Forum of the GRE Analytical Writing Section, an online learning platform, has proven to be an effective tool for guiding students in practicing their writing skills within a learning community. Leveraging the theory of Community of Inquiry, the platform fosters social presence, teacher presence, and cognitive presence, ultimately leading to an improvement in students’ analytical writing skills (Sun et al., 2017 ).

The Theory of Community of Inquiry is a theoretical framework that focuses on the process of creating meaningful and transformative learning experiences in online and blended learning environments. According to Yu and Li ( 2022 ), this theory emphasizes the importance of social presence, cognitive presence, and teaching presence in fostering a deep and engaged learning community. According to this theory, social presence refers to the extent to which participants in an online community perceive each other as real and as connected individuals. It involves establishing trust, building relationships, and engaging in open communication to create a sense of belonging and connectedness. Cognitive presence refers to the depth of critical thinking, reflection, and inquiry that occurs within the learning community. It involves the exploration of complex problems, the application of higher-order thinking skills, and the construction of new knowledge and understanding. Teaching presence encompasses the design, facilitation, and direction of the learning experience by the instructor or facilitator. It includes instructional design, facilitating discourse, and providing direct instruction as necessary to guide and support learners’ engagement and achievement of learning goals.

The Theory of Community of Inquiry suggests that all three elements (social presence, cognitive presence, and teaching presence) are interconnected and essential for the creation of a rich and meaningful learning experience. By fostering a sense of community, promoting active and reflective learning, and providing effective teaching, this theory aims to optimize the online and blended learning environment to support deep and transformative learning outcomes. The online learning platform such as Gather.Town could enhance students’ engagement and interactions in foreign language learning by establishing a learning community (Zhao and McClure). To explore how to improve learning outcomes through learning communities, researchers proposed the research question as follows:

RQ4. How to improve learning outcomes through learning communities?

Research methods.

The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework is a widely recognized and utilized tool for conducting and reporting systematic reviews and meta-analyses in academic research. The PRISMA framework offers a comprehensive set of guidelines to ensure transparency and rigor in the review process, enhancing the credibility and reproducibility of the study findings. The PRISMA framework comprises a 27-item checklist and a four-phase flow diagram, which serve as valuable resources to guide researchers through each stage of the review process. These stages include the identification and selection of relevant studies, the extraction and synthesis of data, the assessment of study quality and bias, and the reporting of the results. The checklist addresses key components such as the design and objectives of the review, the search strategy and inclusion criteria, the data extraction process, and the assessment of the risk of bias in included studies.

By adhering to these guidelines, researchers can ensure a thorough and systematic approach to their review, minimizing the likelihood of bias and enhancing the reliability of the study findings. Furthermore, the PRISMA flow diagram visually depicts the flow of information throughout the review process, from the initial identification of studies to the final inclusion or exclusion of articles. This diagram allows readers to understand the selection process and identify any potential biases or gaps in the review. The PRISMA framework serves as a valuable tool for researchers undertaking systematic reviews and meta-analyses. Its comprehensive checklist and flow diagram promote transparency, rigor, and consistency in the review process, ultimately enhancing the validity and reliability of the study findings.

This systematic review study was implemented based on the protocol of PRISMA (Page et al., 2021 ). The review study was not registered since it did not involve any human or animal participants and was approved by the Academic Board of the University. Researchers recruited three raters to include and exclude the studies obtained from various online databases. Two raters independently included and excluded the studies based on both inclusion and exclusion criteria. The inter-rater reliability was measured to ensure both raters reached a satisfactory degree of agreement on their decisions.

Raters independently extracted data from the included studies using the finalized data extraction form. Three reviewers performed the extraction to minimize errors and biases. Any discrepancies between the reviewers were resolved through discussion or consultation with another reviewer. They assessed the quality and risk of bias of each included study using established tools, such as the Cochrane Collaboration’s risk of bias tool or the Newcastle-Ottawa Scale. This step helps inform the interpretation of the results and enhances the robustness of the systematic review.

To assess the quality and risk of bias of each included study, raters understood the specific criteria and domains assessed and then obtained all relevant information from the included studies, such as study protocols, methods, data, and results. They identified the key domains or criteria used in the assessment tool to evaluate the quality and risk of bias in the studies and evaluated each included study individually based on the identified domains and criteria. After carefully reviewing the information provided in the publication(s) of the study, including methods sections, tables, figures, and supplementary materials, they used the assessment tool to assign ratings or scores for each domain or criterion being evaluated. They justified the ratings for each domain or criterion, summarized the overall risk of bias for each included study, highlighted specific areas where bias might be present, and considered the implications of the assessed risk of bias on the study findings and the strength of evidence.

Raters included the studies based on the following inclusion criteria. Firstly, they should belong to the scope of learning outcomes, communication, anxiety, and motivation in learning communities. Secondly, they should be of higher quality based on the assessment of a systematic review, i.e. Step 6: Assess Quality of Included Studies ( https://guides.lib.unc.edu/systematic-reviews/assess-quality ) detailed in University Guidelines in the University of North Carolina at Chapel Hill. Two raters scored each included scientific literature based on a 5-point system. The final score of each was calculated as the mean of the two raters’ scores. They scored the included studies according to the questions proposed to evaluate their relevance, reliability, validity, and applicability (Appendix A ). Thirdly, the included studies should be able to provide enough data for a systematic review. For instance, they should provide convincing results and evidence to support their findings.

Researchers also established exclusion criteria to exclude the literature. The literature will be excluded if they are poorly scored or designed. They will exclude editorials, notes, short surveys, reference work entries, news, datasets, duplicated documents, withdrawn works, corrections, and those out of the scope of the learning community. They also excluded those without abstracts, rigid design, a proper sample size, or adequate data, as well as those failing to provide enough convincing results and evidence. Two raters will exclude the literature based on the criteria with the measurements of inter-rater reliability. A third rater will also decide the results if both raters cannot reach an agreement on any decision.

Researchers obtained scientific literature from multiple online databases according to their specific syntactic rules. Specifically, they retrieved 2065 results on August 16, 2022 by keying “learn* outcome*“ OR communicat* OR anxiety OR motivat* (topic) and “learn* communit*“ (topic) in the search column in Web of Science including article ( n  = 1433), conference paper ( n  = 656), others ( n  = 63), online first ( n  = 37), reviews ( n  = 34), abstracts ( n  = 16), books ( n  = 3), etc. This online database includes the Core Collection of Web of Science, China Sciences Citation Index, Derwent Innovations Index, KCI-Korean Journal Database, MEDLINE®, and SciELO Citation Index.

They obtained 2236 results by keying (TITLE-ABS-KEY (“learn* outcome*“ OR communicat* OR anxiety OR motivat*) AND TITLE-ABS-KEY (“learn* communit*“)) in the search column of Scopus, including article ( n  = 1269), conference paper ( n  = 644), and book chapter ( n  = 177), review ( n  = 87), book ( n  = 24), conference review ( n  = 24), note ( n  = 5), editorial ( n  = 3), and short survey ( n  = 1). The discipline included Social Sciences ( n  = 1485), computer science ( n  = 892), engineering ( n  = 336), arts and humanities ( n  = 141), mathematics ( n  = 125), psychology ( n  = 107), business, management and accounting ( n  = 100), medicine ( n  = 90), decision sciences ( n  = 51), and physics and astronomy ( n  = 38). The literature search was carried out on August 16, 2022.

They obtained 46 result(s) for ‘(communication OR anxiety OR motivation OR learning OR community)’ in Springer by entering terms, i.e. “with at least one of the words: communication anxiety motivation learning community” and “where the title contains: learning outcome”. The content type included article ( n  = 26), chapter ( n  = 16), conference paper ( n  = 13), and reference work entry ( n  = 4). The discipline included education ( n  = 20), computer science ( n  = 18), engineering ( n  = 2), psychology ( n  = 2), and biomedicine ( n  = 1). The obtained results were all written in English and the search was implemented on August 16, 2022.

They obtained 227 results for [Keywords: communication or anxiety or motivation or learning outcome] and [Title: learning community] in Sage. The article type included article-commentary ( n  = 1), research-article ( n  = 191), review-article ( n  = 8), case-report ( n  = 3), and others ( n  = 24), ranging from 1981 to 2022. The discipline included geography ( n  = 2), public health ( n  = 23), engineering & computing ( n  = 3), marketing & hospitality ( n  = 1), and economics & development ( n  = 5). Researchers carried out the search on August 16, 2022.

They obtained 18 results by keying in “Find articles with these terms: communication or anxiety or motivation or learning outcome, and “Title: learning community” in Elsevier ScienceDirect. The article type included review article ( n  = 1), research article ( n  = 15), encyclopedia ( n  = 1), and book chapter ( n  = 1). The publication titles included Computers & Education ( n  = 2), The Internet and Higher Education ( n  = 2), and Nurse Education Today ( n  = 2). Subject areas included social sciences ( n  = 13), nursing and health professions ( n  = 4), psychology ( n  = 4), business, management and accounting ( n  = 2), arts and humanities ( n  = 1), earth and planetary sciences ( n  = 1), and medicine and dentistry ( n  = 1). After the inclusion and exclusion, researchers included a total of 35 studies in this systematic review study (Fig. 1 ).

This is a diagram that visually displays the process of selecting and filtering relevant scientific literature.

The included studies ( n  = 35) guided the study. They underwent inter-rater selection after the inclusion and exclusion process based on the criteria. Two raters extracted necessary information and data from included studies using content analysis methods (Hsu et al., 2013 ). They adopted Cohen’s kappa statistics to evaluate the inter-rater reliability coefficient (Cohen, 1968 ). The inter-rater reliability reached a satisfactory level ( k  = 0.92). Raters extracted data such as authors, publication years, names of sources, and major findings that might guide this systematic review study (Table 1 ).

The selected studies for this systematic review were chosen following the PRISMA framework, which ensures a comprehensive and transparent selection process. To ensure completeness, a thorough search of relevant databases was conducted, capturing a wide range of studies related to learning communities and their effects on communication, motivation, and learning outcomes. The inclusion criteria encompassed studies from various contexts, such as different educational levels, institutions, and countries.

To address the representativeness of the selected studies, efforts were made to include studies with diverse socio-demographics. This was achieved by including studies conducted in various socio-economic settings, encompassing different geographical regions, cultural backgrounds, and geographic locations. Additionally, studies were included that involved learners from different age groups, ethnicities, and educational backgrounds, ensuring a comprehensive representation of socio-demographic diversity. By incorporating studies from diverse socio-demographic backgrounds, this systematic review aims to provide a more comprehensive and holistic understanding of the effects of learning communities on communication, motivation, and learning outcomes.

Results and discussion

Most studies reported that learning communities could improve learners’ communication. Communication was a fundamental ability that could reflect learners’ academic achievements in online learning communities. Virtual communities could provide private and social media-based platforms for students to communicate with peers or teachers to share their opinions, propose questions, and obtain timely feedback from teachers (Corbo et al., 2016 ). Various roles of students may greatly facilitate communication in learning communities. Different roles of students and teaching in learning communities could exert a great influence on the communicative pedagogical approach and learning experiences (Puigdellivol et al., 2017 ). Teachers could integrate the roles and cater different learning tasks to different individuals. Various kinds of learning communities, assisted with mobile technologies, could enhance communicative skills, improve self-directed learning management, and reduce addictions to social media and cyber-bullying behaviors (Furdu et al., 2015 ). In this way, learners could improve communication through digital technologies (de Witt, 2011 ).

Various factors in learning communities could improve learners’ communicative ability. Virtual and physical learning communities could both improve communicative skills via organized learning activities (Young, 2002 ). School leadership could activate teachers’ learning communities and establish organized interactions to improve cultural knowledge acquisition, teaching skills, and communicative ability (Shin and Choi, 2018 ). Both students and teachers with video annotation tools could improve their communicative skills and reflective thinking ability by reducing communicative hindrance, avoiding the revelation of students’ weaknesses, and contextualizing the written notes in videos (Shek et al., 2021 ). Based on computer-assisted communication, teachers could dominate learning activities and promote cooperation and interactions between students and teachers in learning communities (Zhao et al., 2019 ).

Communication in learning communication is conducive to learning outcomes. Communicative ability, an important factor that could influence learning communities, could in turn influence students’ self-regulation, collaborative learning ability, problem-solving skills, and learning outcomes (Park and Hee, 2022 ). The activity level and communicative skills in learning communities were positively related to learning outcomes (Seo and Eun-Young, 2018 ). Frequent communication, a strong sense of presence, and favorable relationships could greatly improve learning outcomes based on learning communities (Seckman, 2018 ). Social communication occurred frequently in virtual learning communities, where the forum provided opportunities for members to post opinions and answer questions conveniently and concisely (Reyes and Tchounikine, 2004 ). Frequent communication could increase the contacts of knowledge, and thus improve learning outcomes.

Learning communities have the potential to enhance learners’ communication skills. Studies have shown that by participating in learning communities, students are provided with opportunities for collaborative learning, active engagement, and communication with peers and instructors. These interactions facilitate the exchange of ideas, discussions, feedback, and constructive criticism, contributing to the development of effective communication skills. Additionally, the integration of social networks within learning communities can further promote communication by providing an online platform for interaction and collaboration. Therefore, it can be argued that learning communities have a positive impact on learners’ communication abilities.

The majority of studies revealed that virtual learning communities could enhance learners’ motivation. Virtual learning communities could have a positive impact on learners’ motivation for Chinese language education (Cai and Zhu, 2012 ). Living-learning communities could improve learners’ motivation and enhance their skills in adopting motivational strategies. The honors community could more significantly motivate students to learn than science and engineering communities (Faber et al., 2014 ). The features of teachers in learning communities, e.g. shared vision and contextual sustainability, could exert a great influence on students’ motivation in learning activities (Kim and Jung, 2018 ). Interpersonal connections and a sense of belonging could motivate students to engage in learning activities in virtual learning communities (Lopez de la Serna et al., 2021 ). Learning communities could enhance the sense of communities, improve learning quality, enhance learning engagement, increase course satisfaction, and foster learning motivation (Lee, 2021 ).

Learning communities could foster learners’ motivation via the improvement in collaboration, interactions, satisfaction, and self-efficacy. Collaborative and social interactive models based on self-determination theory could cultivate a learning climate motivating students to engage in listening practice in learning communities (Ng and Latife, 2022 ). Students who joined the learning communities tended to possess higher levels of satisfaction, self-efficacy, and motivation than those who did not (Park et al., 2019 ). Learners’ self-efficacy, learning strategies, and intrinsic motivation played important roles in the persistence of learning behaviors in online learning communities (Park and Bong, 2022 ). Teachers’ self-efficacy could exert a great influence on their motivational regulation, perceived teaching values, and engagement in online professional learning communities (Zhang and Liu, 2019 ).

Learning communities can have a positive effect on learners’ motivation. Engaging in a learning community provides a sense of belongingness and support, which can increase learners’ motivation to actively participate in their learning process. Being part of a community creates a social connection that fosters intrinsic motivation and a desire to achieve goals. Learning communities often emphasize collaboration and peer support, which can enhance motivation through the encouragement and inspiration provided by peers. In a community setting, learners can share their successes, challenges, and progress, creating a positive and motivating environment. Furthermore, learning communities can offer additional resources, such as access to mentors or experts, which can increase learners’ motivation by providing them with guidance and support. The availability of these resources and the opportunity for meaningful interactions within a learning community can inspire learners to persist in their learning journey and achieve their goals. Generally, learning communities create a supportive and collaborative environment that promotes motivation and engagement, leading to improved learning outcomes.

Numerous studies demonstrated that both offline and online learning communities could reduce learners’ anxiety. A year-long learning community could facilitate collaboration and reduce the anxiety of university lecturers in the UK (MacKenzie et al., 2010 ), leading to lower levels of anxiety among learners. Virtual learning communities could improve learning environments for dental school students and enhance their engagement in dental education by reducing anxiety and stress (Karpenko et al., 2021 ). In addition, professional learning communities could reduce teachers’ anxiety via training and online courses (Intasingh, 2019 ). Teachers with less anxiety could transfer the relaxing atmosphere to learners, which might result in reduced learner anxiety in learning communities.

Learner anxiety could be mitigated through collaboration in learning communities. Collaborative learning in learning communities could also cause learner anxiety, especially when learners are aware that their learning achievements would be evaluated and compared with their peers. The learner’s anxiety could, in turn, negatively influence their participation and motivation in learning through learning communities. Computer anxiety could negatively influence learning outcomes in computer-supported learning communities (Celik and Yesilyurt, 2013 ). However, frequent interactions could acquaint learners with their environments. With the learning process through learning communities, learners might be increasingly familiar with their peers and competitive environments. Their anxiety might thus be reduced and learning outcomes and coping strategies might be enhanced in learning communities (Hilliard et al., 2020 ).

Learning communities can help mitigate learners’ anxiety. Learning can often be challenging and overwhelming, leading to feelings of anxiety and stress. However, being part of a learning community can alleviate these negative emotions by providing a supportive and collaborative environment. By interacting with peers who share similar learning experiences and challenges, learners realize they are not alone in their struggles. This sense of shared experience and commonality can help reduce anxiety by providing reassurance and support. Learning communities can also offer opportunities for collaboration and peer learning, which can help alleviate anxiety by distributing the workload and fostering a sense of shared responsibility. When learners work together and support one another, the burden of learning may seem less daunting, reducing anxiety levels. Additionally, learning communities often promote a growth mindset, emphasizing the idea that intelligence and skills can be developed over time with effort and practice. This mindset can help alleviate anxiety by reducing the fear of failure and fostering a more positive perception of learning. Consequently, learning communities can provide a nurturing and supportive environment that helps mitigate learners’ anxiety by promoting shared experiences, collaboration, and a growth mindset.

Properly designed online learning communities could improve learning outcomes in various aspects. Online learning platforms could establish learning communities through advanced communicative technologies. Online learning platforms, e.g. IRC Francais, could improve foreign language learning effectiveness through learning communities, improve digital literacy, enhance self-efficacy, facilitate knowledge acquisition, and foster learning motivation (Insaard and Netwong, 2015 ). Online learning platforms such as the Hellenic American Union in Greece could improve second language learning skills through learning communities (Halkias and Mills, 2008 ). Online learning platforms such as UNIV-RCT could provide plentiful learning resources through learning communities to improve problem-solving skills, enhance collaborative learning ability, and maintain French language proficiency (Stoytcheva, 2017 ). Communication in learning communities could enhance individual awareness, team collaborative skills, and learning outcomes via online interactions (Chou et al., 2014 ).

Communication, enhanced through online technologies, could increase learning outcomes. The online platform could improve communication through cloud learning communities and the online teaching was effective through professional learning communities (Karo and Petsangsri, 2021 ). Bilateral communication through learning communities could improve learning outcomes. Communication through learning communities could improve cross-cultural communication and learning experiences (Kamihira et al., 2011 ). The computer-assisted communication through learning communities could increase virtual engagement and social and cognitive presence. Communication is an indispensable factor that may facilitate learning community-assisted learning and teaching. Teachers, developers, and course designers could pay special attention to the ways to enhance communication through online communicative technologies.

Improving learning outcomes through learning communities involves creating a supportive and collaborative environment that fosters engagement, participation, and active learning. Educators can encourage learners to interact with each other through group discussions, collaborative projects, or online forums. This interaction allows for the exchange of ideas, diverse perspectives, and constructive feedback, which can deepen understanding and enhance learning. They can encourage learners to actively engage with course materials and concepts through problem-solving activities, case studies, hands-on experiments, or simulations. This active learning approach promotes critical thinking, application of knowledge, and a deeper understanding of the subject matter. They can create opportunities for learners to connect with each other, such as icebreaker activities, regular check-ins, or social events. Foster a culture of inclusivity, respect, and support to create a safe space for learners to express their ideas, ask questions, and seek help when needed. They can offer clear learning objectives, guidelines, and resources to support learners’ progress. They can promote self-reflection and self-assessment practices to help learners monitor their progress, identify areas where they need improvement, and set goals for growth.

In addition, a well-designed online learning community can significantly improve learning outcomes through collaboration and interaction, peer-to-peer learning, timely feedback and support, a sense of belonging and motivation, personalized learning opportunities, access to diverse perspectives and resources, and flexibility and convenience.

Online learning communities facilitate collaboration and interaction among learners. By incorporating discussion forums, group projects, and virtual classrooms, learners can engage with and learn from one another in a collaborative manner. This active involvement promotes a deeper understanding of the subject matter.

Online learning communities facilitate peer-to-peer learning where learners can share their knowledge, experiences, and perspectives with their peers. Engaging in meaningful discussions and exchanging insights can enhance understanding and promote critical thinking among learners.

A well-designed online learning community provides timely feedback and support mechanisms, such as instructor feedback, peer assessment, and virtual office hours. These elements enhance comprehension, allow for clarification of doubts, and improve overall engagement with the learning materials.

By creating a supportive and inclusive environment, a well-designed online learning community fosters a sense of belonging among learners. This feeling of community helps motivate learners to actively participate, persist in their studies, and strive for better learning outcomes.

Online learning communities can offer personalized learning opportunities through adaptive learning technologies, individualized assignments, and tailored resources. Such customization allows learners to focus on their specific learning needs and preferences, leading to better comprehension and retention of the material.

Online learning communities often bring together learners from different regions, cultures, and backgrounds. This diversity provides learners with exposure to different perspectives and ideas, broadening their understanding and enriching their learning experience.

Online learning communities offer the flexibility to access learning materials and engage with fellow learners at any time and from anywhere. This convenience allows learners to adapt their learning to their individual schedules and preferences, resulting in enhanced engagement and better learning outcomes.

To sum up, a well-designed online learning community enables collaboration, promotes peer-to-peer learning, provides timely feedback and support, fosters a sense of belonging, offers personalization, exposes learners to diverse perspectives, and provides flexibility. These elements collectively contribute to significant improvements in learning outcomes.

Deeper insights into learning communities and related factors

Learning communities are social environments where individuals come together to learn, share knowledge, and support each other’s learning journeys. Online learning communities specifically refer to these communities facilitated through digital platforms, enabling learners from different locations to connect and collaborate virtually. Deep insights into learning communities and related factors can be further explored.

Social constructivism is an important element to be included in learning communities. Learning communities are based on the principle of social constructivism, which suggests that knowledge is actively constructed through social interactions and collaboration. In a learning community, learners engage in discussions, share ideas, and collectively build knowledge through their interactions.

Sense of community plays an important role in community-based learning. A crucial aspect of learning communities is the development of a sense of community among members. The feeling of belonging, shared goals, and support within the community fosters a positive learning environment. The sense of community encourages active participation, cooperation, and a sense of accountability among learners.

Active learning is facilitated in communities. Learning communities promote active learning rather than passive consumption of information. Learners are encouraged to contribute, ask questions, and critically engage with the learning content. This active participation enhances comprehension, retention, and application of knowledge.

Roles of facilitators are essential in learning communities. Facilitators play a significant role in online learning communities by guiding and supporting learners. They create a structured framework, facilitate discussions, provide feedback, and encourage participation. Skilled facilitators can effectively nurture a collaborative learning environment and address individual learning needs.

Peer learning and support are considered important factors in learning communities. Peer learning is an essential component of learning communities. Learners can benefit from the diverse knowledge, experiences, and perspectives of their peers. Peer feedback, collaboration on projects, and collective problem-solving contribute to deeper learning and skill development.

Reflection and metacognition are considered important elements in learning communities. Learning communities encourage learners to reflect on their learning experiences and engage in metacognition, which involves thinking about their thinking. Reflection helps learners consolidate their understanding, identify areas for improvement, and set goals for further learning.

Technology and digital tools can be used in learning communities. Online learning communities heavily rely on technology and digital tools to facilitate communication, collaboration, and access to resources. Learning management systems, communication platforms, multimedia resources, and online forums support and enhance the learning experience within the community.

Lifelong learning and professional development cannot be sustained without learning communities. Learning communities provide opportunities for lifelong learning and continuous professional development. Learners can stay updated with the latest knowledge and trends in their field, acquire new skills, and build professional networks within the community.

Motivation and engagement are important factors influencing the effect of learning communities. Engaging and motivating learners is crucial for the success of learning communities. Incorporating gamification elements, interactive activities, and recognition of achievements can enhance learner motivation and sustain engagement over time.

Assessment and evaluation are important measurements to secure the development of learning communities. Learning communities employ various methods of assessment and evaluation to measure learning outcomes. These may include quizzes, assignments, peer evaluations, and self-assessments. The feedback received through assessments helps learners identify areas for improvement and guide future learning efforts.

In conclusion, learning communities foster active learning, collaboration, peer support, and reflection. Skilled facilitators, technology, and an effective sense of community contribute to creating an engaging and supportive learning environment. By focusing on these factors, learning communities can significantly enhance the learning outcomes and overall learning experience for individuals.

Recommendations for optimizing community-based learning outcomes

Building a strong sense of community, fostering active collaboration, and facilitating meaningful connections are key to optimizing community-based learning outcomes. A sense of belonging can facilitate community-based learning. Create an inclusive and welcoming learning community that values and respects the contributions of all members. Encourage learners to actively participate and engage in discussions, promoting a sense of belonging and ownership within the community. This can enhance motivation and commitment to learning.

Active collaboration is an important factor in the success of community-based learning. Design learning activities that promote active collaboration among community members. Assign group projects, discussion forums, and peer-to-peer mentoring programs to encourage learners to work together, share knowledge, and learn from each other. This collaborative approach enhances critical thinking, problem-solving skills, and a deeper understanding of the subject matter.

It is important to cultivate meaningful connections between community members. To promote mentorship, professional development, and access to a broader range of knowledge and resources, learners should be encouraged to establish meaningful connections with their peers, facilitators, mentors, and industry professionals within the community. This can be facilitated through various opportunities such as networking events, guest lectures, and virtual meet-ups.

Major findings

This study presents a systematic review based on the PRISMA framework, finding that the utilization of learning communities can yield enhancements in communication, motivation, and learning outcomes, along with a reduction in learners’ anxiety. It is suggested that well-designed online learning communities have the potential to improve learning outcomes, while the integration of online technologies can further augment communication and subsequently enhance learning outcomes within learning communities. Additionally, the researchers put forward several suggestions aimed at enhancing learning outcomes through the implementation of learning communities.

Limitations

Although this study is rigidly designed, this study is limited to several aspects. Firstly, this study could not leverage all the publications due to the limitation of library resources. Secondly, this study undertakes a systematic review without sufficient quantitative data support. The number of included studies is limited to 35, which is insufficient to underpin the conclusion in the absence of quantitative data. Lastly, there may be other factors excluded from this study that may need further investigation in the future.

Implications for future research

Future research could integrate entertainment elements into learning communities. Serious games could stimulate learners’ interest and promote their learning motivation by integrating entertainment into learning communities (Tam, 2022 ). Learners could play serious games with team members and subconsciously acquire knowledge embedded in the games and plots. Teachers could guide students to focus on how to achieve goals in the games and students struggled with fun in the gameplay. The difficulty in entertainment-based learning communities may lie in the development and design of serious games for adult learners. Future researchers could be devoted to the creation of serious games with interdisciplinary efforts.

Educational administrators could consider including the element of learning communities-based learning and teaching in the future. Some countries and areas have implemented this educational policy. For instance, learning communities were included as an important educational policy in Scotland (Hancock and Hancock, 2021 ). The learning community organization may need administrators to coordinate between different individuals and institutions. Individuals may possess different personality traits and preferences. Coordinators need to meet different demands and establish a harmonious learning community. Teachers could account for the goals and process of learning community-based learning and encourage learners to participate in the learning activities. The formation of learning communities may be confronted with unexpected challenges.

Future teachers could combine traditional pedagogy with learning communities, especially in language education. For instance, the combination of a popular teaching model with learning communities could leverage educational technologies and improve community-assisted Spanish language learning outcomes. The learning community could cultivate a Spanish language learning space for students and teachers to interact with each other and solve difficult problems (Overfield, 2003 ). In a learning community, students could enhance their interactions and communication with peers and teachers to improve their language practice skills. They could also foster their critical thinking ability through the learning community.

Advanced technologies and digital literacy could better the learning community-based learning outcomes in the future. Learning technologies and innovative pedagogies could improve language learning by establishing a cyber-learning community via a flipped pedagogical approach. The online technologies could make learning communities easily established, together with smooth communication. The learning communities could improve meaningful and collaborative learning, increase the opportunities for oral skill practice, and enhance language learning engagement through various learning activities such as role play, storytelling, discussion, and presentation (Wu et al., 2017 ). Future development of learning community-based learning may largely depend on the development of information technologies and digital literacy of learners and teachers.

Future research could highlight how to improve task distribution and collaborative teaching in learning communities. For instance, in learning communities of English teachers, teachers played different roles and presented different identities, and they engaged in a higher proportion of reasoning teaching with lower distributed participation and less collaborative teaching (Cheng and Pan, 2019 ). It may be the important task of teachers to allot appropriate assignments to different team members in the learning community. Teachers could also encourage members’ collaboration in learning and improve the learning process. They could also provide timely feedback on students’ complaints or suggestions. Students should hold a positive attitude towards collaborative learning in a community.

Future research could focus on how to improve students’ metacognition in a learning community. Community metacognition could improve communication among tertiary learners in Chungbuk University in learning communities. Students with higher levels of metacognition could adopt a cooperative strategy to learn in a community because they might be aware of the importance and benefits of community-based learning. They would collaborate with peers and teachers by raising questions and solving problems. On the contrary, those with lower meta-cognition could not perceive the benefits of learning communities and could thus refuse to collaborate with members or teachers. They would likely prefer individual learning, which might not benefit the learning outcomes.

Future research could also focus on gender differences in the sense of learning and attitudes toward privacy in learning communities. Females held a significantly stronger sense of learning and felt more comfortable with personal information revelation than their counterparts (Ozturk and Deryakulu, 2011 ). Learning attitudes could exert a great influence on learning outcomes in the context of learning communities (Eftimie, 2013 ). Positive attitudes could increase learning outcomes in the context of learning communities. Future researchers could make every effort to cater to different preferences in learning communities and improve community-based learning outcomes by adopting appropriate teaching strategies. This might pose great challenges to teachers and designers in the future.

Data availability

The datasets generated during and/or analyzed during the current study are openly available in the [OSF] repository, [ https://osf.io/m697t/?view_only=a3c261407d83424f93ddcfefcee607a3 ].

Blayone TJB, Vanoostveen R, Barber W, DiGiuseppe M, Childs E (2017) Democratizing digital learning: theorizing the fully online learning community model. Int J Educ Technol High Educ 14. https://doi.org/10.1186/s41239-017-0051-4

Brooks K, Adams SR, Morita-Mullaney T (2010) Creating inclusive learning communities for ELL students: transforming school principals’ perspectives. Theory Into Pract 49(2):145–151. https://doi.org/10.1080/00405841003641501

Article   Google Scholar  

Cai SR, Zhu W (2012) The impact of an online learning community project on University Chinese as a foreign language students’ motivation. Foreign Lang Ann 45(3):307–329. https://doi.org/10.1111/j.1944-9720.2012.01204.x

Celik V, Yesilyurt E (2013) Attitudes to technology, perceived computer self-efficacy and computer anxiety as predictors of computer supported education. Comput Educ 60(1):148–158. https://doi.org/10.1016/j.compedu.2012.06.008

Cheng X, Pan XY (2019) English language teacher learning in professional learning communities: a case study of a Chinese secondary school. Prof Dev Educ 45(4):698–712. https://doi.org/10.1080/19415257.2019.1579109

Chou CL, Hirschmann K, Fortin AH, Lichstein PR (2014) The impact of a faculty learning community on professional and personal development: the facilitator training program of the American Academy on Communication in Healthcare. Acad Med 89(7):1051–1056. https://doi.org/10.1097/ACM.0000000000000268

Article   PubMed   Google Scholar  

Cohen J (1968) Weighted kappa: nominal scale agreement provision for scaled disagreement or partial credit. Psychol Bull 70(4):213

Article   CAS   PubMed   Google Scholar  

Corbo JC, Rundquist A, Henderson C, Dancy MH (2016) Using asynchronous communication to support virtual faculty learning communities. In: Jones DL, Ding L, Traxler A (eds) Physics Education Research Conference (PERC), Sacramento, CA, July 20–21, 2016

De Witt C (2011) Communication in online learning communities: digital teaching in higher education as reflected by pragmatism. Z Padagogik 57(3):312–325

Google Scholar  

Duong QPT, Pham TN (2022) Moving beyond four walls and forming a learning community for speaking practice under the auspices of Facebook. E-learn Digit Media 19(1):1–18. https://doi.org/10.1177/20427530211028067

Eftimie R (2013) The role of pupil–teacher communication within E-learning communities. Is E-learning a good tool in improving educational performance? In: Giannakopoulos G, Sakas DP, Vlachos DS, KyriakiManessi D (eds) Proceedings of the 2nd International Conference on Integrated Information (IC-ININFO 2012), procedia social and behavioral sciences, vol. 73, Budapest, Hungary, August 30–September 3, 2012, pp. 196–204

Engeström Y (2001) Expansive learning at work: toward an activity theoretical reconceptualization. J Educ Work 14(1):133–156. https://doi.org/10.1080/13639080020028747

Faber CJ, Grigg S, Kirn A, Chasmar J, Benson LC (2014) Engineering student motivation and perceived metacognition in learning communities. In: ASEE (ed) 2014 ASEE annual conference & exposition, Indianapolis, IN, June 15–18, 2014

Furdu I, Patrut B, Varlan S (2015) Mobile notifier tool to enhance communication within learning communities. In: Patrut B, Andone D, Holotescu C, Grosseck G (eds) SMART 2014—International conference on Social Media in Academia—Research and Teaching (SMART), Timisoara, Romania, September 18–21, 2014

Grimm AT, Kanhai D, Landgraf J (2019) International student spouses and the English language: co-creating a low-stakes language learning community. J Int Stud 9(4):1172–1190. https://doi.org/10.32674/jis.v9i4.583

Halkias D, Mills GT (2008) Distance education in support of lifelong learning: the case of the Hellas Alive Web Platform in building Greek language learning communities. In: Mastorakis NE, Mladenov V, Bojkovic Z, Simian D, Kartalopoulos S, Varonides A (eds) Proceedings of the 12th WSEAS international conference on computers, PTS 1–3: new aspects of computers, Heraklion, Greece, July 23–25, 2008

Hancock A, Hancock J (2021) On the outside, looking in: learning community languages and Scotland’s 1. Curr Issues Language Plan 22(3):328–347. https://doi.org/10.1080/14664208.2020.1867415

Hilliard J, Kear K, Donelan H, Heaney C (2020) Students’ experiences of anxiety in an assessed, online, collaborative project. Comput Educ 143:103675. https://doi.org/10.1016/j.compedu.2019.103675

Hsu YC, Hung JL, Ching YH (2013) Trends of educational technology research: more than a decade of international research in six SSCI-indexed refereed journals. Educ Technol Res Dev 61(4):685–705. https://doi.org/10.1007/s11423-013-9290-9

Insaard S, Netwong T (2015) Development of professional learning community of practice to enhance practical community using ICT for instruction of basic education teachers. In: Chova LG, Martinez AL, Torres IC (eds) INTED2015: 9th International Technology, Education and Development Conference. INTED Proceedings, Madrid, Spain, March 2–4, 2015, pp. 1968–1976

Intasingh S (2019) Non-science teacher’s teaching science: problems, adaptation, and anxiety. In: Yuenyong C, Sangpradit T (eds) Proceedings of the 6th International Conference of Science Educators and Teachers (ISET) 2018, AIP Conference Proceedings, 2081, 030014

Isbell DR (2018) Online informal language learning: insights from a Korean learning community. Language Learn Technol 22(3):82–102

Kamihira T, Aoki M, Nakano T (2011) Building a shared cross-cultural learning community for visual communication design education. In: Kurosu M (ed) 2nd International conference on Human Centered Design (HCD)/14th International Conference on Human–Computer Interaction (HCI), Orlando, FL, July 9–14, 2011

Karo D, Petsangsri S (2021) The effect of online mentoring system through professional learning community with information and communication technology via cloud computing for pre-service teachers in Thailand. Educ Inf Technol 26(1):1133–1142. https://doi.org/10.1007/s10639-020-10304-2

Karpenko AE, Sterlitz SJ, Garcia-Hammaker SS (2021) Virtual online learning communities reducing dental student stress and anxiety. J Dent Educ 85:1195–1196. https://doi.org/10.1002/jdd.12430

Kim J, Jung PS (2018) Teachers’ motivation for voluntary participation and the characteristics of teacher learning community impact on transfer of learning. J Learn-Cent Curric Instr 18(10):953–975. https://doi.org/10.22251/jlcci.2018.18.10.953

Lee YS (2021) Successful learning communities during times of disruption: developing a community of inquiry in business communication. Bus Commun Res Pract 4(1):57–64. https://doi.org/10.22682/bcrp.2021.4.1.57

Article   MathSciNet   Google Scholar  

Lopez de la Serna A, Bilbao Quintana N, Romero Andonegui A (2021) Motivation and sense of belonging in the Virtual Learning Communities at the university. Comparative study. EDMETIC 10(2):227–249. https://doi.org/10.21071/edmetic.v10i2.12988

MacKenzie J, Bell S, Bohan J, Brown A, Burke J, Cogdell B, Jamieson S, McAdam J, McKerlie R, Morrow L, Paschke B, Rea P, Tierney A (2010) From anxiety to empowerment: a Learning Community of University Teachers. Teach High Educ 15(3):273–284. https://doi.org/10.1080/13562511003740825 . Article Pii 923048457

Magana AJ, Jaiswal A, Madamanchi A, Parker LC, Gundlach E, Ward MD (2021) Characterizing the psychosocial effects of participating in a year-long residential research-oriented learning community. Current Psychol. https://doi.org/10.1007/s12144-021-01612-y

Ng B, Latife A (2022). Exploring students’ learning and motivation in a lesson study for learning community (LSLC) environment: a new perspective. Int J Lesson Learn Stud. https://doi.org/10.1108/IJLLS-01-2022-0007

Overfield DM (2003) Creating a language learning community within and beyond the classroom. In: Cherry CM (ed) Models for excellence in second language education: dimension 2003, joint conference of the Southern Conference on Language Teaching (SCOLT)/Foreign-Language-Association-of-Georgia (FLAG), Atlanta, GA, February 27–March 1, 2003

Ozturk E, Deryakulu D (2011) The effect of type of computer mediated communication tools on social and cognitive presence in online learning community. Hacet Univ Egitim Fak Derg-Hacet Univ J Educ 41:349–359

Pagan FJB, Ventura RC, Gomez-Garre LC (2020) The participation of Primary Education teachers in the virtual learning community English Teachers Exchange Network (ETEN). Rev Fuentes 22(2):178–189. https://doi.org/10.12795/revistafuentes.2020.v22.i2.10

Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD et al. (2021) The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ 372:71. https://doi.org/10.1136/bmj.n71

Park, Dong S, Mee-Soon C, Hye-Young J (2019) The structural relationship among learning satisfaction, learning motivation, and academic self-efficacy in physical education students: multi-group analysis according to participating in learning community activity of center for teaching and learning. J Learn-Centered Curric Instr 19(11):551–571. https://doi.org/10.22251/jlcci.2019.19.11.551

Park H, Bong CHAS (2022) The effect of self-efficacy for group work, learning strategy, intrinsic motivation on task persistence in the online learning community. Res Humanit Soc Sci 30(2):186–209

Park MJ, Hee PS (2022) The mediating effect of communication skills on the relationship between collaborative self-regulation, collaborative self-efficacy, team efficacy, and problem-solving skills in university students learning community activities. J Learn-Centered Curric Instr 22(11):57–75. https://doi.org/10.22251/jlcci.2022.22.11.57

Parker M, Patton K, Goncalves L, Luguetti C, Lee O (2022) Learning communities and physical education professional development: a scoping review. Eur Phys Educ Rev 28(2):500–518. https://doi.org/10.1177/1356336X211055584

Puigdellivol I, Molina S, Sabando D, Gomez G, Petrenas C (2017) When community becomes an agent of educational support: communicative research on Learning Communities in Catalonia. Disabil Soc 32(7):1065–1084. https://doi.org/10.1080/09687599.2017.1331835

Ren HH, Ma C, Atlantis P (2016) Study on improvement and development of college English autonomous learning field within ubiquitous learning community. In: Atlantis Press (ed) Proceedings of the 2016 International conference on Computer Engineering and Information System (CEIS), ACSR—Advances in Comptuer Science Research, vol. 52, Shanghai, China, November 12–13, 2016, pp. 174–177

Reyes P, Tchounikine P (2004) Redefining the turn-taking notion in mediated communication of virtual learning communities. In: Lester JC, Vicari RM, Paraguacu F (eds) 7th International conference on intelligent tutoring systems, proceedings. Lecture notes in computer science, vol. 3220, Maceio, Brazil, August 30–September 3, pp. 295–304

Salazar D (2011) E-learning communities: meeting the instructional challenges of a diverse educational environment. In: Callaos N, Carrasquero JV, Oropeza A, Tremante A, Welsch F (eds) 5th International Multi-Conference on Society, Cybernetics and Information (IMSCI’11), Orlando, FL, July 19–22, 2011

Schechter C (2010) Learning from success as leverage for a professional learning community: exploring an alternative perspective of school improvement process. Teachers College Record 112(1):182–224

Seckman C (2018) Impact of interactive video communication versus text-based feedback on teaching, social, and cognitive presence in online learning communities. Nurse Educ 43(1):18–22. https://doi.org/10.1097/NNE.0000000000000448

Seo E, Eun-Young K (2018) The effects of participation and activity levels in learning community of college students: focusing on academic achievement and communication skills. J Learn-Centered Curric Instr 18(21):929–948. https://doi.org/10.22251/jlcci.2018.18.21.929

Shek MM-P, Leung K-C, To PY-L (2021) Using a video annotation tool to enhance student–teachers’ reflective practices and communication competence in consultation practices through a collaborative learning community. Educ Inf Technol 26(4):4329–4352. https://doi.org/10.1007/s10639-021-10480-9

Shin SI, Choi E-S (2018) Structural relationships among secondary school principals’ compassionate rationalism leadership, communication in organization, learning organization culture, teaching competency and active participation degree in teacher learning communities. J Korean Teacher Educ 35(4):41–70. https://doi.org/10.24211/tjkte.2018.35.4.41

Stoytcheva M (2017) Collaborative distance learning: developing an online learning community. In: Pasheva V, Popivanov N, Venkov G (eds) Proceedings of the 43rd international conference Applications of Mathematics in Engineering and Economics (AMEE’17), AIP conference proceedings, 1910, 060009, Sozopol, Bulgaria, June 8–13, 2017

Sukirman MK, Kabilan MK (2023) Indonesian researchers’ scholarly publishing: an activity theory perspective. High Educ Res Dev. https://doi.org/10.1080/07294360.2023.2209522

Sun YY, Franklin T, Gao F (2017) Learning outside of classroom: exploring the active part of an informal online English learning community in China. Br J Educ Technol 48(1):57–70. https://doi.org/10.1111/bjet.12340

Tam ACF (2022) Transforming preschool language teachers’ beliefs of implementing play-based learning in a professional learning community. Int J Early Years Educ. https://doi.org/10.1080/09669760.2022.2065247

Wen QF, Zhang H (2020) Building professional learning communities of foreign language teachers in higher education. Circulo De Linguistica Aplicada A La Comunicacion 84:1–12. https://doi.org/10.5209/clac.72815

Wenger E (1998) Communities of practice: learning, meaning, and identity. Cambridge University Press

Wenger-Trayner E, Wenger-Trayner B (2015) An introduction to communities of practice: a brief overview of the concept and its uses. https://www.wenger-trayner.com/introduction-to-communities-of-practice . Accessed 3 Jul 2023

Wu WCV, Hsieh JSC, Yang JC (2017) Creating an online learning community in a flipped classroom to enhance EFL learners’ oral proficiency. Educ Technol Soc 20(2):142–157

Yan Y, Yang LX (2019) Exploring contradictions in an EFL teacher professional learning community. J Teacher Educ 70(5):498–511. https://doi.org/10.1177/0022487118801343

Young A, Cavanagh M, Moloney R (2018) Building a whole school approach to professional experience: collaboration and community. Asia-Pac J Teacher Educ 46(3):279–291. https://doi.org/10.1080/1359866X.2018.1436689

Young C (2002) Task design principles of a campus-based communication skills course in a natural-virtual learning community. In: Kinshuk X, Lewis R, Akahori K, Kemp R, Okamoto T, Henderson L, Lee CH (eds) International conference on computers in education, Auckland, New Zealand, December 3–6, 2002, pp. 1305–1306

Yu ZG, Li M (2022) A bibliometric analysis of Community of Inquiry in online learning contexts over twenty-five years. Educ Inf Technol. https://doi.org/10.1007/s10639-022-11081-w

Zhang S, Liu Q (2019) Investigating the relationships among teachers’ motivational beliefs, motivational regulation, and their learning engagement in online professional learning communities. Comput Educ 134:145–155. https://doi.org/10.1016/j.compedu.2019.02.013

Zhang YH (2016) Learning community in foreign language learning. In: Chang L, Guiran C, Zhen L (eds) Proceedings of the 6th international conference on Electronics, Mechanics, Culture and Medicine (EMCM), ACSR—advances in computer science research, vol. 45, Shenyang, China, December 29–31, 2015, pp. 394–397

Zhao J, Hua X, Li Z (2019) Empowering learning community of teachers in computer mediated communication. In: Wang S, Lin L, Hartsell T, Zhan H, Beedle J (eds) 2019 Proceedings of the eighth International Conference on Educational Innovation through Technology (EITT). University of Southern Mississippi, Biloxi, MS, pp. 27–31

Zhao X, McClure CD (2022) Gather.Town: a gamification tool to promote engagement and establish online learning communities for language learners. RELC J https://doi.org/10.1177/00336882221097216

Download references

Acknowledgements

Author would like to extend sincere gratitude to anonymous reviewers and editors. This work is supported by the Key Research and Application Project of the Key Laboratory of Key Technologies for Localization Language Services of the State Administration of Press and Publication, “Research on Localization and Intelligent Language Education Technology for the ‘Belt and Road Initiative” (Project Number: CSLS 20230012), and Special fund of Beijing Co-construction Project-Research and reform of the “Undergraduate Teaching Reform and Innovation Project” of Beijing higher education in 2020-innovative “multilingual+” excellent talent training system (202010032003); Research on the Development of Leadership Skills for Foreign Language Instructors in Selected Chinese Universities (a sub-project by NSF, Grant No. 2106571).

Author information

Authors and affiliations.

Foreign Languages Teaching Department, Qufu Normal University (Rizhao District), 276826, Rizhao, Shandong, China

Faculty of Foreign Studies, Beijing Language and Culture University, Beijing, China

• Zhonggen Yu

You can also search for this author in PubMed   Google Scholar

Contributions

WC collected data, analyzed data, revised and confirmed the paper. ZY acquired the funding and conceptualized the study.

Corresponding author

Correspondence to Zhonggen Yu .

Ethics declarations

Competing interests.

The authors declare no competing interests.

Ethical approval

The research was approved by the academic committee of the Faculty of Foreign Studies of Beijing Language and Culture University (Grant No. 20231037). The research was performed where no human participants were involved.

Informed consent

This article does not contain any studies with human participants performed by any of the authors.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Rights and permissions.

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Cite this article.

Cao, W., Yu, Z. Exploring learning outcomes, communication, anxiety, and motivation in learning communities: a systematic review. Humanit Soc Sci Commun 10 , 866 (2023). https://doi.org/10.1057/s41599-023-02325-2

Download citation

Received : 20 February 2023

Accepted : 27 October 2023

Published : 24 November 2023

DOI : https://doi.org/10.1057/s41599-023-02325-2

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

This article is cited by

A meta-analysis of effects of automated writing evaluation on anxiety, motivation, and second language writing skills.

• Xiaoli Huang

The Asia-Pacific Education Researcher (2024)

Quick links

• Explore articles by subject
• Guide to authors
• Editorial policies

Advertisement

Learning design to support student-AI collaboration: perspectives of leading teachers for AI in education

• Open access
• Published: 28 January 2022
• Volume 27 , pages 6069–6104, ( 2022 )

Cite this article

You have full access to this open access article

• Jinhee Kim 1 ,
• Hyunkyung Lee 1 &
• Young Hoan Cho   ORCID: orcid.org/0000-0002-1775-7953 1  

38k Accesses

85 Citations

4 Altmetric

Explore all metrics

Preparing students to collaborate with AI remains a challenging goal. As AI technologies are new to K-12 schools, there is a lack of studies that inform how to design learning when AI is introduced as a collaborative learning agent to classrooms. The present study, therefore, aimed to explore teachers’ perspectives on what (1) curriculum design, (2) student-AI interaction, and (3) learning environments are required to design student-AI collaboration (SAC) in learning and (4) how SAC would evolve. Through in-depth interviews with 10 Korean leading teachers in AI in Education (AIED), the study found that teachers perceived capacity and subject-matter knowledge building as the optimal learning goals for SAC. SAC can be facilitated through interdisciplinary learning, authentic problem solving, and creative tasks in tandem with process-oriented assessment and collaboration performance assessment. While teachers expressed instruction on AI principles, data literacy, error analysis, AI ethics, and AI experiences in daily life were crucial support, AI needs to offer an instructional scaffolding and possess attributes as a learning mate to enhance student-AI interaction. In addition, teachers highlighted systematic AIED policy, flexible school system, the culture of collaborative learning, and a safe to fail environment are significant. Teachers further anticipated students would develop collaboration with AI through three stages: (1) learn about AI, (2) learn from AI, and (3) learn together. These findings can provide a more holistic understanding of the AIED and implications for the educational policies, educational AI design as well as instructional design that are aimed at enhancing SAC in learning.

Similar content being viewed by others

Leading teachers' perspective on teacher-AI collaboration in education

Types of teacher-AI collaboration in K-12 classroom instruction: Chinese teachers’ perspective

Designing for Complementarity: Teacher and Student Needs for Orchestration Support in AI-Enhanced Classrooms

Explore related subjects.

• Artificial Intelligence
• Digital Education and Educational Technology

Avoid common mistakes on your manuscript.

1 Introduction

One of the most profound areas of technological progress within the past decade has been in the development of artificial intelligence (AI) and its increased integration across multiple industries. The field of education is among those adopting and adapting to the opportunities and challenges of AI-enabled technologies, amid the broader incorporation of data, algorithms, and automation (Luckin et al., 2016 ). For instance, devices and programs that utilize AI can capture, aggregate, and analyze students’ learning performance data in real-time from different sources to develop a student learning profile and automatically provide customized content, feedback, and learning parameters. These, in turn, provide more tailored and relevant learning opportunities and experiences that support students as they progress through the learning material (Peng et al., 2019 ; Cho et al., 2019 ). On the other hand, communicative AI such as conversational agents and embodied social robots interact with students, not limited to supporting students’ cognitive development, serve as an empathic peer/tutor to support affective development such as improving learning interest, motivation, self-regulation, and sense of empathy and collaboration (Chin et al., 2014 ). In sum, AI is increasingly permeating the education ecosystem by increasingly interacting and collaborating with students, building and maintaining social relationships, and offering personalized instruction. This indicates that the educational field has integrated nonhuman agents as collaborative agents serving roles of tutors/teachers, assistants, advisors, and even learning peers (Lee & Kim, 2020 ; Kim et al., 2020 ).

Although interests and demands for students-AI collaboration (SAC) in learning, in general, are increasing, the integration of AI in classroom activities and the AI-related school curricula are complex and challenging in K-12 schools. In the absence of a specific roadmap for AI in Education (AIED), teachers face challenges in that they are not formally trained for AIED but have to teach about it in a jam-packed curriculum, without adequate and convenient infrastructure. Moreover, while AI applications differ from other technologies in that they explicitly aim to act as agents in the classroom environment by adaptively tapping into students’ learning process, teachers face substantial pedagogical challenges in designing and facilitating how students interact, collaborate, and learn with AI in the classroom previously ruled by human-human (students and teachers) interaction only (Gunkel, 2012 ).

In recognition of teachers’ beliefs and views will decide the actual curriculum at the ground level and are critical in the planning of educational practice for sustainability (Chiu, 2017 ), this study aims to examine the views of leading teachers in AIED on key considerations for the design and implementation of SAC in learning for K-12 schools. The findings of this study can develop a holistic understanding of learning design in the classroom where AI serves as a student’s collaborator on a learning task.

2 Literature review

2.1 student-ai collaboration.

The ways that the role of AI in learning has been positioned as another emerging educational tool are mainly two-fold: (1) ‘Learning with AI’ and (2) ‘Learning about AI’ (Holmes et al., 2019 ). The former means the use of AI as  a direct teaching and learning tool (e.g., adaptive or personalized learning management system, intelligent tutoring system (ITS), and AI speaker), while the latter refers to an approach that teaches AI as a learning content to develop the ability to design, develop, and utilize AI algorithms based on the understanding of AI (Baker & Smith, 2019 ). Although these studies offer a comprehensive understanding of the advancement of AI technology and the development of AI-related school curricula, it is claimed that such tool-centric conception of AI cannot fully discuss the potential of AI functionality, intended educational purpose of AI, as well as the potential risks of AIED in the educational system (Big Innovation Centre, 2020). In contrast to existing technologies, AI engages in more autonomous, personalized, and active interaction with students. Interactions with AI are dynamic rather than static, reflecting upon the communication and interactions being exchanged within a learning process and context. AI being autonomous, social, and reactive (Wooldridge & Jennings, 1995 ) makes the study of AI intriguing for AIED scholars and employs AI to serve roles as learning peers, tutors, and assistants that have been restricted to human students and teachers (Simmler & Frischknecht, 2020). This study, therefore, envisions AI as a subject in interaction on students’ learning and shifts the attention that was previously focused on the advancement of AI technology and the development of AI-related school curricula to the nature and quality of social interaction between students and AI as collaborative learning partners/peers and its implications for the learning environment that can support the SAC for learning.

The current study adopts the theory of distributed cognition (DC) that has been found as an effective framework for a holistic understanding of the complex relations and interactions among heterogeneous agents (i.e., students, AI, and teachers), the multiple existing digital technologies, and practices in the classroom environment (Hutchins, 1995 ; Perkins, 1993 ; Nardi & O’Day, 1999 ). The notion of DC provides two important methodological insights. First, the unit of analysis should be expanded from the individual to the wider system. For instance, cognitive processes may be distributed across the members of a team; cognitive processes may involve coordination between internal and external (material or environmental) structure; processes may be distributed through time in such a way that the products of earlier events can transform the nature of later events (Hutchins, 1995 ). Second, the analysis looks for a range of mechanisms that can partake in the cognitive system rather than restricting itself to symbol manipulation and computation – e.g. the interplay between human memory, external representations, and the manipulation of objects (Hollan et al., 2000 , p. 176). This gives some indication of the sorts of observations and phenomena that a DC analysis might highlight; this study has expanded on these by reference to the broader DC literature to present a set of components for SAC on learning. The present study developed the SAC model composed of the four components that have been the focus of the work to date: (1) curriculum, (2) student-AI interaction, (3) environment, and (4) evolution over time (See Fig.  1 ).

Student-AI Collaboration Model

The SAC model consists of three participants: Student(S), AI, and Teacher(T). First, an individual student is considered an active learning agent in the SAC model. In fact, students in the early AIED literature, particularly in learning with ITS, were portrayed as passive recipients along with a specified learning path that AI guides. However, the recent study anticipates shifts in students’ agency and roles over their learning through AIED whereby students learn as collaborators actively interacting with AI to achieve more optimized learning, or as leaders participating in learning and rethinking their growth within complex learning systems (Ouyang & Jiao, 2021 ).

Next, the model identifies AI as another learning agent, shifting its nature and role from a mere learning tool. Previous studies take note of AI’s human-resemblance characteristics as a unique feature that distinguishes it from traditional educational tools (Huang, 2018 ), and expand the role of AI in learning (Simmler & Frischknecht 2020). For example, AI could be a teacher to properly diagnose learning processes and outcomes, provide personalized feedback and evaluate achievement (Chaudhry & Kazim, 2021 ). On the other hand, Fryer et al. ( 2017 ) demonstrate that the AI chatbot and students learn foreign languages together through peer relationships. Instead of fixing a conceptualization of interaction as a human-only process and the role of technology as a mediating tool, the model opens the theoretical possibility of AI as an interaction subject directly exchanging information with students in a learning process (Guzman & Lewis, 2020 ).

Alongside students and AI, teachers play a critical role in shaping and facilitating SAC and must be held accountable in classroom learning (Chaudhry & Kazim, 2021 ). In this regard, Utterberg and colleagues (2021) describe teachers as gatekeepers for AI adoption in the classroom, arguing that if teachers do not allow students to interact with AI in the classroom learning activity, AI is not likely to be embedded into the teaching curricula of school. In addition, although some anticipate that AI-assisted data-driven, evidence-informed decisions based on the collection and analysis of students’ real-time learning data may diminish teachers’ leadership, most literature argues that human teachers will remain the masterminds behind AI algorithms, considering the drawbacks of AI-assisted data-driven decision-making in intuition and value consideration (Wang, 2021 ). Taken together, teachers’ pedagogical decision-making is best managed by reviewing and embracing a blend of data-driven, evidence-informed decision-making by AI and value-based moral decision-making by teachers to provide more effective instructional strategies (Zheng, 2020 ).

2.2 Curriculum: Learning goal, content and assessment

The curriculum, one of the core elements in the model, consists of learning goals, content, and assessment. While the acquisition of knowledge and skills in a specific domain has been the prime focus in the earlier literature (Ouyang & Jiao, 2021 ), the recent studies highlight the goal of AIED to cultivate high-level thinking such as problem-solving and creativity through collaboration with AI, rather than simply acquiring knowledge in the specific domain (Kafai & Burke, 2014 ). Particularly, improving computational thinking (CT), a set of skills including decomposition, abstraction, algorithm design, debugging, testing/simulation, heuristic reasoning, and generalization, is being accentuated to understand and use AI effectively to solve problems (Shute et al., 2017 ; NRC, 2010 ). Rodrigues et al. ( 2016 ) highlighted that CT can facilitate and support the mental processes that support the activity of learning in the school by demonstrating quantitative evidence on the correlation between primary school students’ CT and academic performance in the school.

Although AIED calls for a multidisciplinary approach, STEM-related learning contents were widely implemented for several technical and practical reasons (Zawacki-Rtichter et al., 2019 ). Because AI’s understanding of meaning and context through natural language processing is yet well advanced and much more difficult and expensive than interpreting mathematical expressions, humanities-related (e.g., arts, social sciences, etc.) learning contents have less been stressed in the AIED field (Olmos-Peñuela et al., 2015).

The assessment in AIED provides more formative feedback based on a sophisticated diagnosis of student understanding, engagement, and academic integrity (Zawacki-Richter et al., 2019 ). In contrast to the traditional assessment centered on formalized tests, the assessment in AIED analyzes and evaluates information from various pathways about students through speech recognition, language analysis, and behavioral pattern analysis (Vincent-Lancrin & van der Vlies, 2020 ).

2.3 Student-AI interaction: Cognitive, socio-emotional, and artifact-mediated interaction

Student-AI Interactions are divided into three types: cognitive, socio-emotional, and artifact-mediated interaction. First of all, cognitive interaction refers to task-focused interaction about the content or their learning process (Dillenbourg et al., 1995 ). This includes interactions about domain-focused content to be learned, such as the sharing, elaborating, and processing of knowledge (Hmelo-Silver & Barrows, 2008 ). For instance, a student talks with a chatbot about the characteristics of rocks suggested by a teacher, infers the types of rocks, and learns about different criteria for classifying rocks in a geology class.

Second, socio-emotional interaction involves “purposeful interchanges among group members that shape perceptions of emotions and socio-emotional climate” (Bakhtiar et al. 2017 , p. 62). A range of studies investigated the effects of socio-emotional interaction between a student and AI on learning performance. For example, polite web-based tutors induce more learning than regular web-based tutors (McLaren et al., 2011 ). In addition, Hwang et al. ( 2020 ) found that an adaptive learning model using emotional and cognitive performance analysis was effective in elementary school students’ mathematical learning outcomes by reducing their math anxiety.

Lastly, it should be noted that the core of AI is algorithms and engines. AI, therefore, interacts with students through artifacts such as interfaces. The characteristics of an AI system’s interface and how students interact with AI through the interface are found to have a significant impact on learning with AI. For instance, Fu et al. ( 2020 ) presented that the AI’s social presence, accurate speech recognition, and peer influence affect language learners’ continuous interaction with AI-enabled automatic scoring applications.

2.4 Environment: Learning space, institution, and culture

The environments including learning space, institutional rules, and school culture play a crucial role in implementing SAC effectively. They are macro-level background elements for SAC. First, for AI to successfully embed into the classroom, an appropriate learning space has to be preemptively built. In this regard, the Korean government highlights distributing smart devices and establishing a wireless network environment for K-12 classrooms as a part of building an adequate learning space for AIED (MOE, 2021). In addition, digital infrastructure (i.e., learning platform) is considered to be essential for SAC (Wang & Cheng, 2021 ).

Second, institutional support is necessary for the budget/funding provision, the curriculum/pedagogy development, and legal/ethical guidelines in AIED. In particular, the time and cost of developing and introducing an appropriate methodology for implementing AIED pose a major challenge in public educational institutions (Zawacki-Richter et al., 2019 ). Without an upper-level institution’s explicit directions and guidelines on curriculum and pedagogy for AIED, it would be challenging for schools to adopt AI, the new technology (Wang & Cheng, 2021 ). Furthermore, the introduction of AI in classrooms can cause several legal and ethical issues related to personal information and privacy, thus, institutional safeguards are needed to protect students from damage and disputes (Okoye et al., 2020 ).

Last but not least, school culture is a crucial environmental factor in SAC. The AIED has to be achieved through the collective will of the diverse stakeholders in the educational system. For instance, teachers are resistant to adopting new technology after they receive negative feedback from colleagues, students, and parents in school as well as due to their demanding schedules meeting various roles in schools. Therefore, it is crucial to build a collaborative school culture that supports professional dialogue on the need and importance of AIED and the utilization of AI for learning among varied stakeholders (Kim et al., 2021a ).

2.5 Evolution over time

AI is not a static tool that does not change. Just as students learn and improve learning by interacting with AI, AI also learns and improves over time through interaction with students (Self, 1998 ). While a student learns effectively through interaction with the personalized AI, AI optimizes the student model by collecting the student’s information and response to the AI’s feedback and reflecting on them to derive more optimized analysis results (Tan & Cheah, 2021 ). In short, student learning growth and development go hand in hand with AI development, and vice-versa, which indicates that AI is not a mere tool.

After reviewing existing literature, the present study acknowledges the unique potential of AI and seeks a better understanding of the key considerations for the design and implementation of SAC in learning for K-12 schools toward a new AI-mediated educational environment. More precisely, the study aims to disclose and examine teachers’ views on what (1) curriculum design (learning goals, contents, and assessment), (2) student-AI interaction during learning activity, and (3) learning environments (learning space, culture, and institution) are required and (4) how students would develop collaboration with AI over time based on the proposed SAC model (see Fig. 1 ). The research questions (RQs) set for the study are as follows:

(1) What curriculums are required in the SAC?

(2) What supports are needed in student-AI interactions?

(3) What learning environments should be established?

(4) How would SAC evolve over time?

3.1 Participant and context

In accordance with the 2015 revised national curriculum which reinforced software (SW) education as a mandatory subject, the Korean Ministry of Education and Ministry of Science and ICT have designated and operated 2011 SW leading schools via 17 metropolitan and provincial education offices as of December 2020. Among them, 247 schools are now selected as AI pilot schools, and 34 additional schools are designated as AI convergence curriculum-oriented high schools (KERIS, 2020 ).

A combination of a purposeful and snowball sampling strategy was employed to explore diverse views of AIED leading teachers about what should be supported to design and implement SAC in learning for the needs of different contexts of schools and students. Every participant works in either SW leading schools, AI pilot schools, or AI convergence curriculum-oriented high schools. This study initially conducted interviews with four leading teachers (P1, P3, S1, S5) from different regions/schools, years of teaching experience, and AIED experiences. Table 1 summarizes the information of 10 Korean teachers (5 primary and 5 secondary schools Footnote 1 ) who participated in the study. This study received ethical approval from the university’s Institutional Review Board and informed consent from all participants.

3.2 Data collection

The findings of this study are based on semi-structured interviews conducted with 10 teachers presented in Table 1 for approximately between 90 and 120 minutes. We first developed a semi-structured interview guide based on the SAC model proposed (see Fig. 1 ) with 15 questions related to the main components of the model. Due to the COIVID-19 pandemic situation, interviews were mostly conducted via videoconferences using the ZOOM, except face-to-face interviews with S1 and S2 because of their preference. The interviews were audio-recorded and then transcribed.

3.3 Data analysis

A hybrid inductive and deductive thematic analysis (Braun & Clarke, 2006 ) was undertaken to identify themes related to the proposed framework. The first author generated initial codes through a repetitive reading of transcripts and conducted a deductive thematic analysis to develop initial themes based on the proposed framework. Then, a corresponding author reviewed all annotated transcripts to thoroughly examine codes and to identify any differences in interpretations. The analysis continued with an inductive approach to search for new emerging codes and themes not previously identified. The team reviewed codes and generated themes, combining existing themes or splitting some themes into subthemes. This process was repeated until the researchers reached an agreement on every theme. The team finally defined and named each theme that provided a full sense of the theme and its importance and translated the interview extracts regarding the themes from Korean to English.

We critically reflected on the translations to ensure that the ‘voice’ of the participants was maintained, so that those possible misunderstandings were avoided. To ensure the reliability of the data, we first confirmed the interview transcripts with every participant and they revised them when necessary. The analysis process and findings were discussed among the authors, and any disagreement was clarified.

4 Findings and discussion

Twenty-three themes were generated to address the four research questions. To be specific, a final set of 7 themes under RQ1, 6 themes under RQ2, 7 themes under RQ3, and 3 themes under RQ4 were determined (see Appendix 1 ).

4.1 Curriculum: Learning goal

4.1.1 capacity building.

Teachers considered developing capacities that help students to be future-proof citizens in the fast-changing society driven by digital and AI technologies as the prime learning goals in SAC on learning. First of all, teachers expected SAC could augment students’ cognitive capacity that includes higher-order thinking (e.g., CT, critical thinking, creativity and imagination, and analytical thinking). To be specific, teachers considered that students should (1) engage in high-level cognitive processes involving problem-solving, divergent thinking, and reflection, (2) express ideas on learning tasks, and (3) solve problems in systematic ways (Kafai & Burke, 2014 ; Resnick, 2006 ) through interaction with AI. These findings reflect the experimental study by Lin et al. ( 2021 ), which provided quantitative evidence of the SAC’s positive impacts on students’ creativity, logical thinking, and problem-solving skills. They further highlighted that AI allowed students to better comprehend the problems within actual scenarios and help them plan and arrange a way to solve problems through various functions AI offers, such as analysis of AR sensors. The current study’s findings shift the use of AI in students’ learning from simply solving a problem for students or providing them the right answer (Skinner, 1958 ; Afzal et al., 2019 ) to engaging them in problem-solving, providing a variety of problem-solving experiences to propel them into new ways of thinking (Ouyang & Jiao, 2021 ). In addition, educating students in higher-order thinking means instilling the ability to collaborate effectively with AI as they conduct in-depth analysis to make decisions, evaluate the information received and processed by AI critically, and generate a broader range of solutions using AI.

Second, teachers aimed to facilitate the development of students’ social capacity for collaboration with peers, communication (e.g., storytelling and public speaking, asking the right questions, and synthesizing messages), leadership (e.g., achievement orientation, grit, and persistence, and coping with uncertainty) through SAC. For instance, S3 commented:

It has proven very difficult to program machines to emulate our innate ability to manage and utilize emotions such as negotiation, conflict resolution, and having empathy for others. Students are not simply learning how to interact with AI but they are also learning things that AI will not be doing through AI-involved learning tasks with peers .

Teachers consider social skills as highly human abilities that may not be replaced by automation or AI. Meanwhile, they find the opportunity to utilize AI and learning activities/experiences with AI to help students possess AI-proof skills that add values beyond what can be done by automated systems and AI.

Parallel to these capacities, digital capacities that embrace the skills such as software use and development (e.g., programming literacy, computational and algorithmic thinking, data analysis, and statistics) and understanding digital systems (e.g., data literacy, tech translation, and enablement) as main learning objectives to be achieved through SAC.

4.1.2 Subject-matter knowledge building

Another learning goal that teachers sought to achieve through SAC was to guide students toward a better, more robust understanding of the subject-matter knowledge. However, teachers do not intend to solely transfer one specific subject knowledge, but also assist students in transforming/applying knowledge and skills into tangible products, feasible solutions, and new information. As P1’s quotation illustrates in Appendix 1 , SAC facilitates students to understand subject-matter knowledge by helping them to organize new information, link it to their existing knowledge, and retrieve information (Yeo & Lee, 2012 ).

4.2 Curriculum: Content

4.2.1 interdisciplinary learning.

Most teachers argued that it is essential to teach students algorithms, mathematical and statistical backgrounds within informatics/computer science subjects to build a strong foundation of knowledge in AI. However, they also highlighted that it is significant to make connections between SAC and cross-curricular subjects to better achieve the aforementioned learning goals. Teachers explained that an interdisciplinary learning approach can support students (1) to build a holistic understanding of AI itself (“ Students can better understand AI and technology itself by connecting it to its roots in linguistics, social science, economics, neuroscience, etc.”, S2) and (2) to improve their entire task performance with AI in various subjects (“ A more integrated curriculum that enhances students’ consistent use of AI and diversifies experiences in AI for solving complex problems within a specific subject and across subjects” , S3).

4.2.2 Authentic problems and tasks

Teachers highlighted the use of authentic tasks that allow students to construct and apply standard-driven knowledge to solve a real-world problem need to be foregrounded. Teachers developed various learning activities that make connections between subject-area knowledge and real-life problems through the  student-AI team’s task-focused interactions. For instance, students addressed their own classroom problems (e.g., a face detection of students running with outdoor shoes inside the classroom in home-economics, P4), daily life (e.g., a weekly meal plan for the family in home-economics; illustrated plant book making in science, P1), and global challenges (e.g., predicting the future of Antarctica’s melting glaciers in social science and science, S4). Teachers support the notion of Cho et al. ( 2015 ) findings that an authentic task makes classroom work more relevant by serving as a bridge among the content learned in the classroom, why this knowledge is important in the world outside of it, and how real-world AI technologies work.

In line with this, it was worth noting that teachers implement such authentic tasks with AI by engaging students in the research process with both teachers and AI supporting them. Teachers highlight that SAC should build students’ ability to (1) inquire (i.e., ask good questions, discuss and reformulate the problems), (2) research and reflect (i.e., identify and determine what needs to be learned and what resources are required to answer those questions), (3) evaluate (i.e., information gathering, filtering, and integration) and (4) communicate ideas and learning (Ai et al., 2008 ).

The class sought to analyze the cause of the increasing suicide rate in the country. Students first analyzed public open data through data mining techniques. They then captured one particular suicide risk group and reasoned why. We had an open discussion about the characteristics of this group and the possible suicidal impulse experience this group may hav e (S1).

4.2.3 Creative tasks

Teachers identified creative tasks (e.g., creating writing, drawing, and music composition) that can develop students’ capabilities to develop ideas, make connections, create and make, and communicate and evaluate the creative outcomes, as another meaningful learning content and activities that SAC should be engaged (See P5’s quotation in Appendix 1 ).

4.3 Curriculum: Assessment

4.3.1 process-oriented assessment.

Teachers explained that various learning contents and activities are designed for students to have an opportunity to achieve the desired learning goals. Along with this, teachers highlighted that the assessment of SAC on the learning task performance should continually be conducted, while both teachers and students are actively involved in the assessment. Teachers, in particular, highlight the assessment aims to understand the process students undergo when given a task, rather than the outcome or product of a learning activity. In this regard, most teachers in the study conduct the assessment on two aspects: conceptual and procedural knowledge on both the subject-matter area and AI technology. For instance, S1 said:

In case when students developed prediction models for identifying areas at risk of earthquakes, the depth of students’ understanding of subject knowledge and ability to locate it to define and construct variables that affect data collection, selection, and analysis and the adjustment of the model weight is central areas for assessment. So in this case, both I and the physics teacher examined students’ performance .

It is interesting to capture that teachers perform co-assessment, whereby co-teachers discuss assessment, grading practice, share assessment responsibilities, and collaborate on ways to differentiate assessments based on learner needs (Conderman & Hedin, 2012 ). In doing so, teachers determine how well students have performed and plan the necessary language and content learning targets to help all students meet grade-level expectations (Dove & Honigsfeld, 2017 ).

4.3.2 Collaboration performance

Another significant assessment approach in SAC was related to the assessment of team learning performance. Teachers expressed that although some SAC-related learning activities and tasks are performed at an individual level, most work is accomplished by teams of individuals, either be small (i.e., a group of three) or large (i.e., a whole class). Teachers then recognize the importance of leveraging the collective knowledge and distributed resources (Johnson & Johnson, 2006 ) in achieving shared task goals not solely between an individual student and an AI but among students. Therefore, a scheme that is commonly used for assessing individual learning should be applied with caution. Team level learning is assessed both at the lower level through the acquisition of knowledge and member satisfaction, and at the higher level, such as enhanced work processes and level of behavioral changes. Along with this, team learning assessment is not limited to the performance among students, but includes interaction between students and AI, which is illuminated by S3:

Collaboration with peers is a key part to be assessed by criteria such as how they interact with other group members; contribute knowledge and resources to group discussion; provide constructive feedback to others and facilitate the group processes by follow-up, extension, and reframing. However, students’ collaboration with AI is an equally important area to be assessed by examining how much and various data they exchanged with AI, how many new models they have tried, and how they reduced the model’s error.

4.4 Student-AI interaction: Cognitive interaction

4.4.1 teacher support for students.

To enhance student-AI cognitive interaction during SAC, teachers expressed a range of supports and improvements needed both for students and AI. First of all, instruction on AI principles for students is found to be critical. Students need to develop a deep understanding of the core concepts of AI (i.e., definitions and types of AI and the knowledge of algorithms; Kim et al., 2021b ) and establish a sensibility of AI’s limitations, an understanding of what AI can and cannot do, the benefits and potential problems that the deployment of AI might entail to effectively regulate and orchestrate their learning task operation process. In particular, primary school teachers expressed a pressing need to guide students to explicitly identify what is and what is not AI, since many students conflate AI technologies with other non-AI technologies, and identify the unique characteristics of AI that may benefit or hinder their learning and action.

Second, given that data fuel AI supporting students’ data literacy which can collect, process, analyze, evaluate and manage data to make data-based decisions (UNESCO, 2019a ) is essential support alongside the instruction of AI principles. S1 well presents this view:

Students wondered why AutoDraw only recommends a series of western style hot dogs, sausages served on a toasted roll or a bun, not Korean-style hot dog on a stick! The class analyzed data AutoDraw learned from and found out that not sufficient image data of Korean hot dogs have been collected compared to the western hot dog. Groups of students then further discuss how to promote Korean culture and food.

Through her quotes, it can be seen that data literacy allows students to better understand AI’s suggestions/recommendations particularly when there is uncertainty about AI’s suggestion. Through reasoning and examining data, students actively exchange and reflect on knowledge and perspectives shaped in society with data that represent digital images of real phenomena, objects, and social processes. This guides students to be actively involved in meaning-making by contextualizing AI’s suggestion into the learning task context and further developing solutions to address the task. In doing so, students become active agents from passive consumers of AI.

Third, teachers need to prompt students to reflect on SAC and enhance their skills for handling failure and their confidence during SAC through debugging AI models and error analysis whereby students interpret the significance of observed outcomes of the AI; analyze the logic of the model and test data, model prediction performance, and model features; evaluate where the logic of model data, prediction, and features break down; develop alternative ways to fix the breakdown; justify resolution for the breakdown; and examine and test their assumptions in iterative cycles of attempting a fix (DeLiema et al., 2020 ). Teachers mentioned a range of classroom activities and teaching strategies that surround debugging such as (1) comic-strip-like storyboard creation (students create their SAC experiences over time), (2) data visualization (students create a visual representation of how data was generated, when it was generated, who generated it and how it was stored) and (3) writing a journal specifically in response to debug and error analysis strategies performed in one-on-one or a small group.

4.4.2 AI offering an instructional scaffolding

Teachers expected AI could offer students scaffolding-driven interaction that provides them with detailed instructional support during learning task operation. Particularly, teachers highlighted that AI should take a proactive approach by anticipating students’ learning difficulties and presenting a series of step-by-step questions that enhance students’ understanding of subject knowledge (Albacete et al., 2018 ).

Students were given the assignment to research the moon using an AI speaker. But young students sometimes don’t know what to ask and where to begin when they search for information. AI should more proactively interact with students by asking specific questions like “Do you know how crater looks like?” to scaffold the research process, instead of simply answering questions asked by students (P3) .

4.5 Student-AI interaction: Socio-emotional interaction

4.5.1 teacher support for building students-ai relationship.

Instruction on AI ethics and AI experiences in daily life were found to be two crucial supports needed to enhance student-AI socio-emotional interaction. Teachers described that the AI ethics education aims to establish students’ moral sensitivity toward AI in which students’ ethical grounding can be embedded in the selection, design, deployment, and use of AI as well as decision-making driven by AI. Teachers particularly highlight that it is crucial to educate students not only about the possible ethical and emotional harm caused by AI or misuse of AI but also the importance of humans’ ethical values on shaping technology, which in turn shapes individual lives and society.

Students should be fully aware of ethical challenges when AI is misused. At the same time, they should be mindful that they are the ones who shape and develop AI. AI will learn what they speak to AI and how they behave to AI and that learning results will come back to them (P5).

Teachers further pointed out that it is vital to provide students with AI experiences in daily life to enhance their awareness of AI, sensitivity to its applications, and become familiar with AI. In addition, teachers find the opportunity to build authentic connections between students’ AI experiences and AI ethics instruction.

Students often imagine that AI exists only in the movie and assume that AI has nothing to do with them. So I often share examples of how AI is already used in our everyday lives, including Google search, smart home devices like smart refrigerators, Netflix and Youtube recommendation engine, and even robot barista! We then further discuss how AI might impact their parents and their jobs in the future. Students actively say their opinions about how technology should be used and even talk about ethical and legal impacts (P4).

4.5.2 AI attributes as a learning mate

First, teachers perceived that the element of gamification could positively enhance students’ participation, engagement, and continuity in SAC and the SAC performance. This view is in line with earlier research which found gamification is an integral part of students-technology interaction to improve engagement, participation, and continuity of individuals to support learning processes and improve learning outcomes (Caporarello et al. 2019 ). In this regard, Dalmazzo and Ramirez ( 2017 ) utilized gamified interactions between students and an automatic tutoring system to provide students with adaptive learning guidance.

Second, teachers suggested that AI should be engaged in educationally meaningful socio-emotional interaction with students; AI should be designed with an understanding of students’ affective domain in mind. For instance, P1 expressed:

Teaching is not simply about building students’ knowledge. AI should interact educationally meaningfully with students, encourage them to overcome their difficulties and achieve the task, and motivate students to try once again when they insist that they would not be able to solve the problems. In this regard, educational AI engineers need a deep understanding of students’ affective and psychology domains.

Their views are corroborated by existing studies suggesting that AI needs to be equipped with a theory of mind which would make it possible to recognize and understand emotions, infer intentions and predict behavior to build and maintain relationships, communicate effectively, and work collaboratively with humans to achieve common goals (Cuzzolin et al., 2020 ; Riedl, 2019 ).

4.6 Student-AI interaction: Artifact-mediated interaction

4.6.1 intuitive interface of ai.

Intuitive AI interface/hardware can even be suitable for students with no prior experience. In particular, primary school teachers expressed that the interface itself needs to be a powerful medium for expression and support students in working on the task without requiring additional manual books to figure out how the AI system works out.

In addition, teachers highlight that AI interface/hardware design should make the task execution process both by students and AI intuitive, particularly through interactive visualization. For instance, P1 said:

Synchronization is needed between students and the machine learning algorithms to create a framework for accessing knowledge and teaming up to direct the search for knowledge and eventually act for the shared goals. AI interface should integrate visual information production or processing panel to visualize the most information-intensive pathway for exchanging information between students and artificial agents.

4.6.2 Availability of diverse digital tools

Teachers expressed the need for an AI interface that is rich in the pool of digital tools to make the SAC process more interactive and learners more active and engaged in executing the task. Especially, teachers associate this need to create a classroom for accommodating a diverse range of skills, needs, and interests of students. Accordingly, students work and collaborate with AI in varied methods and strategies to execute the task. For instance, P3 shared students’ use of AutoDraw in different cases as follows:

Students used AutoDraw’s iconic images, screen-captured them, and worked on Powerpoint to further edit them with texts and other images to make a poster for nature protection in a science class. In Korean class, students downloaded their works on AutoDraw and worked on Word to write a story to make a book. In times of a whole-class discussion, students captured individual work and shared it on Miro, an online whiteboard and visual collaboration platform. Can’t all of these works be done on Autodraw?

4.7 Environment: Learning space

4.7.1 flexible classroom design.

Teachers expressed that SAC can take place not only in the digital learning environment but also in the actual classroom. To support new ways of learning that may occur in SAC, classroom spaces should embrace adaptability (students-adaptable space) and convertibility (repurposing space like a classroom becoming a computer lab, art studio, or gym) which promotes effective collaboration amongst students as well as SAC.

SAC-related learning activities can take place in different subjects. The classroom should be flexible enough to turn to a science lab where students can work on simulation with AI on a laptop from a music studio where students collaboratively work on song-making with AI and their peers (P1).

4.7.2 Digital learning environment

Adequate digital infrastructure should be equipped to facilitate SAC. To do so, teachers first mentioned that the school should be equipped with secured wireless networks to connect and facilitate real-time interactions among students, teachers, AI, and other mobile devices via broadband to cloud-based tools and platforms. Moreover, the security and privacy of networks are increasingly important in learning, secure authentication and access control should be an integral component of the school wireless networks architecture (Zhu et al., 2020 ).

Second, the 1:1 device to student ratio is found to be essential. To support students’ consistent and immediate access to digital content, simultaneous online collaboration inside/outside the classroom resources, and systematic collection of students’ data for personalized learning, teachers consider providing school-owned one-to-one devices, rather than bring your own device (BYOD) policy. The BYOD system makes it virtually impossible for teachers to monitor whether all students can access the same material at the same speed as each other, and also causes problems when outdated devices fail. In addition, teachers prefer portable and lightweight devices over desktop PC, meaning that a dedicated computer lab is not needed to access technology.

Third, a cloud-based learning platform that collects, analyzes, and processes data generated from various interactions (i.e., between students-AI, AI-other existing digital tools, students-students, students-teachers-AI) is required to adapt and personalize to each learner to give the optimal learning environments. It should, however, be noted that teachers underline that the newly developed AI system should make synergy and combination with other existing digital learning applications such as LMS, digital textbooks, and educational administration systems. S4 well reflects this view:

Such a platform will generate an immense volume of data. If the outputs and data cannot be transferred automatically into the existing NEIS Footnote 2 system, who will then take this job? Me, an informatics teacher? That will add another work for teachers, while AI should automate teachers’ repetitive tasks .

4.8 Environment: Institution

4.8.1 systematic aied policy.

The prerequisites for successful AI applications at the ground level are not only technical in nature. The establishment of long-term systematic AIED policy nationwide that add a value of AI applications in education and implement AIED strategically were found to be the most-in-demand by teachers.

First of all, a system-wide vision and strategic priorities that the nation aspires to achieve with AIED need to be formulated. In particular, teachers advised that the government needs to shape the AIED vision based on an in-depth understanding of students’ learning and development processes and of the impacts that AI will make on learning rather than simply highlight international education trends and market demand. Teachers then expected the government to communicate with them about what are and what are not desirable outcomes of AI-enhanced learning to increase their understanding of how to address SAC in the learning context, and complement and augment student capabilities through SAC. Following the aforementioned suggestion, there is a need for a master plan to inform about a coherent curriculum that clearly defines sets of learning objectives across the school and grade level, utilizing AI in education management to support personalized resources and outcomes, and assessment methodologies on multiple dimensions of competencies and outcomes driven from student-AI interaction during learning.

At the other spectrum, teachers emphasized that the government should set inter-sectoral governance and coordination mechanism to make concerted effort among different stakeholders (i.e., students, teachers, parents, policymakers, researchers, and EdTech providers) and maximize their cross-sector collaboration and resource sharing to truly build ‘educational AI’ and ensure safe and effective implementation of AIED. S1 highlights this view as follows:

Government-schools-research institution-Edtech companies all need to work closely to develop AI itself as well as implement AIED. Especially, AIED collaboration councils consisting of stakeholders and other experts should be established to facilitate virtuous circles of collaboration. Schools need to voice their demands and preference in AI development and necessary educational programs to external stakeholders on an ongoing basis and they can provide useful feedback throughout the implementation of a change initiative .

Her quotes indicate that teachers call for the establishment of a central governing board supporting and overseeing the policy implementation, a coordination body to manage the partners and collaboration, and a team of representatives charged with implementing the policy (UNESCO, 2019b ).

4.8.2 Flexible school system

Teachers anticipate that AI will accelerate personalized learning as its technology develops rapidly. In this context, they suggest that students are better grouped according to competencies within the subject in the school. To do so, teachers emphasized that the school systems should shift from generic ‘education level’ to an emphasis on subjects. For instance, P3 said:

AI works with students at a level appropriate to their domain knowledge. Although students in the same class work on the same AI platform to solve math problems, one works at an advanced level and the other one works at the beginner level. For teachers to better orchestrate and support students at their level, the school system needs to allow students to selectively learn necessary subjects according to their level of domain specific knowledge and their preference .

4.8.3 Teacher capacity building in AIED

The government needs to plan training programs and continuous supports to develop teachers’ AI knowledge that is rapidly evolving and to enable them to apply AI to their practice. Although participating teachers are all leading teachers in AIED, they expressed a strong need to update them with the latest AI knowledge and curriculum design capacity from experts in different fields, including AI engineering, statistics, mathematics, and education, to be able to interpret the output of an AI and translate it into meaningful feedback to students during SAC process as well as apply AI in an educationally meaningful way.

Students often come up with challenging questions that require a deep understanding of mathematics, statistics, and new techniques in AI. So I decided to attend open lectures at universities and workshops run by an EdTech company to learn (S1).

4.9 Environment: Culture

4.9.1 culture of collaborative learning.

Teachers perceived that it is essential to establish a culture of collaborative learning as the basis to drive a profound implementation of SAC. Throughout the interviews, teachers expected students to develop, through teaching and learning AIED, the skills and attitudes that enable collaboration with peers and technologies, which is well reflected in the assessment area as well. They, however, experienced barriers in forming a collaborative learning culture among colleague teachers although it is most pertinent for co-design, implementation, and assessment of AI learning. Particularly, secondary school teachers pointed out that portraying teaching and learning AI as the learning boundary of one specific subject such as informatics or science and technology blocks dialogue among teachers in a wide-area subject. In this regard, teachers suggest supporting teachers’ professional learning communities composed of different subject teachers to understand the broader values of individual subjects, share information and knowledge openly and identify effective AIED practices across the subjects.

4.9.2 Safe to fail

For the goals of SAC to be achieved, teachers highlighted creating a safe to fail environment that supports learning from failure, and developing students’ mindset of ‘have a go’ needs to be embedded in the classroom. Teachers criticized existing schoolwork and assessment practices that have been performed toward attaining higher scores on a school’s standardized exams and solving standard problems within the classroom. Such classroom culture does not easily allow to make failures and appreciate problems or their alternative solutions. In contrast, teachers expected students to frame and value failure as an integral part of learning instead of a hindrance that slows their pace of work through SAC on learning tasks. For instance, P5 said:

I strongly encourage students to make mistakes, or even allow them to experience failure during SAC. Creating such an atmosphere and culture is important for them to treat failure as productive and try different ways to solve problems with AI and their friends. Also, they learn to appreciate or analyze the feedback that failure offers .

This notion is supported by the study of Nachtigall et al. ( 2020 ) arguing that a culture of trial and error scaffolded by teachers helps failure to become a learning opportunity.

4.10 SAC co-evolution

Teachers anticipated that students would develop collaboration with AI through three principal stages: (1) learn about AI, (2) learn from AI, and (3) learn together. First of all, students begin with little understanding of AI itself, the goals of SAC on learning tasks, and the collaboration process. Therefore, at this stage, teachers mainly focus on developing students’ understanding of AI, directing them in the procedural use of AI through step-by-step task execution and fostering a positive attitude toward AI. Students are not likely to relate SAC to the scope outside of the instructional setting in a specific domain.

In the second stage, students experiment and apply SAC for building knowledge and solving authentic problems and real-life tasks. In doing so, students develop strategic ways of working and interacting with AI and formulate supportive relationships. At this stage, teachers need to design learning activities that require the use of knowledge from different subject domains for SAC whereby students can actively test and examine exploration and independent use of AI.

Although teachers expressed the final stage as a seemingly far-fetched scenario, but envision it as a plausible pathway with rapid technological change. At the final stage, teachers expected that AI would serve diverse roles in learning and teaching and bring new forms of school systems that resonated with OECD ( 2020 )’s notion of future school scenario 3: schools as learning hubs. In this scenario, personalized learning will be strengthened within a framework of collaborative work. Students interact with AI for higher-order learning activities in the context of broader learning ecosystems, leveraging resources of external institutions (e.g., museums, libraries, technological hubs, etc.).

Someday in the future, a form of Minerva school would become apparent in the realm of public education whereby students are taught subject knowledge online both by human teachers and AI teachers while they are actively performing in diverse problem-solving projects, engage in a whole community offline, and develop higher-order thinking (S3).

5 Conclusion

Through situating the teachers’ views on the nexus of theory and practice, the study provided a better understanding of how to design and support SAC in four dimensions: (1) curriculum, (2) student-AI interaction, (3) learning environments, and (4) SAC development. Nonetheless, we emphasize that this study’s findings are preliminary to understand SAC in the learning context. The study, therefore, is not target-bound but steps into a point for discussion and suggestions to better design SAC for students’ meaningful learning.

First of all, teachers in the study designed SAC on a learning task in their class while they aimed to augment students’ competencies that go well beyond the knowledge and skills typically measured by schools’ standardized tests. These competencies include improved understanding of complex concepts in the subject, connections among ideas, processes, and learning strategies, as well as the development of problem-solving, visualization, data management, communication, and collaboration skills. These findings echo with the concept of intelligence augmentation (IA) coined by Engelbart ( 1962 ), highlighting that AI should be developed to supplement or support human intelligence rather than attempt to imitate/replicate or replace human cognitive functions and operate independently. In support of this argument, this study calls for educational AI developers to understand the importance of students’ capacities (e.g., creativity) that need to be nurtured through the interaction with AI (Hassani et al., 2020 ; Zheng et al., 2017 ). Educational AI that interacts with students should better be developed to help them to do more than they are currently capable of doing. AI should encourage students to fully accomplish learning tasks on their own (to be autonomous learners) by externalizing their ideas, extending their perspective through a massive volume of data analysis, and providing new experiences enhancing their affective domain in learning (e.g., learning motivation, the joy of learning, and self-efficacy). For instance, Grammarly, the writing correction AI software, can help academic authors excel in writing skills by suggesting better ways of phrasing sentences rather than merely detecting and replacing the grammar errors by an author.

In line with the IA-directed AI development in education, this study directs teachers to pay more attention to instructional strategies for integrating AI to improve students’ thinking skills (e.g., CT, critical thinking, creativity and imagination, and analytical thinking), rather than merely focusing on coding/programming and creating neural networks. Although teachers in this study highlighted a digital capacity such as programming literacy and data analysis, their underlying notion around understanding AI operations and concepts and applying them to gather, evaluate, and use information was meant to enhance students’ higher-order thinking. Along this way, teachers actively support students with CT-related activities (e.g., debugging AI models and error analysis) to better understand AI, interact with AI and solve problems collaboratively with AI. This reflects that the teachers perceive CT as a cornerstone for students’ cognitive development as well as a logical way of thinking for learning and acting with AI. In support of the existing studies highlighting that using technology for drill and practice generally has been found to be less effective than using technology for more constructivist purposes such as writing, research, collaboration, analysis, and publication (Warschauer & Matuchniak, 2010 ), this study recommends teachers’ training programs to enable teachers to build substantial understanding and experience on subject-specific AI applications integrated with CT and AI-driven instructional design. While discussions on CT skills were narrowly positioned within the field of computer science or STEM-related subjects (Barr & Stephenson, 2011 ; Lee et al., 2020 ), this study moves CT forward to be extensible and embedded across disciplines. Yet, its concept, components, and detailed skills should be well understood and contextualized within a subject-specific context, its learning goals, and learning activities together with a range of different teaching and learning approaches underpinning AI of each subject. The development of teachers’ instructional competencies would help students to augment high-level thinking with AI and have educationally meaningful interaction with AI. Furthermore, this study’s findings highlighted the importance of co-design for AIED curriculum planning and co-assessment on SAC performance on learning tasks. In this regard, teacher educators should develop a necessary toolkit/guideline of resources and activities for structuring co-design of AIED curriculum among teachers from various subjects and provide support and make improvements along the way.

Furthermore, this study found a strong need for a system-wide policy that orchestrates top-down and bottom-up reflection. Teachers expressed that top-down reflection needs to orchestrate what learning the nation expects AI to support, what education system we sought to build, and what roles that different stakeholders are expected to play to achieve desired goals of AIED by taking into account evidence about areas of both the AI’s strengths and weakness in students’ learning. In this regard, educational policymakers are called upon to specifically and explicitly address questions related to shaping a newly developing educational system by adopting AI, incorporate the best and safeguarding against the unknown or harmful dimension if such are found, and offer a structured format to those reflections with the expectation of actionable outcomes. On the other hand, policy should support bottom-up coordination to maximize cross-sector collaboration and resource sharing among different stakeholders in which schools’ ongoing needs and challenges are discussed and educationally meaningful AI and pedagogical practices can then be designed via academia-public-private collaborative research and development (R&D). In this regard, promoting opportunities for sustained investment in AIED R&D and for transitioning advances into practices at the ground level is on the call (Big Innovation Centre, 2020; UNESCO, 2021 ).

Although the present study can provide a springboard for other scholars and practitioners to further examine SAC in learning, there are a few limitations to be addressed in the future study. First, this study examined teachers’ perceptions among 10 leading teachers in AIED, which may somewhat limit the generalizability of our results. Therefore, future research needs to apply the proposed framework in the study on a larger scale. Second, while this study proposes a new model to design and examine SAC in the K-12 learning context, more research is needed to validate, further refine and enrich the proposed model by applying and evaluating it on diverse subject classes and different school contexts. For instance, participating teachers in the study anticipated that SAC might evolve over time from the stage of becoming familiar with AI to solving diverse learning tasks with AI, which then leads to disruptive changes in the education system. Reflecting on these findings, future studies can expand this area of research by analyzing current AIED learning design from the SAC co-evolution perspective, what instructional support and AI technologies need to be developed to support gradual evolution between students and AI, and what aspects of the educational system need to be adjusted to meet with the changes driven by SAC co-evolution in learning.

While the Korean primary school is organized with six-year curriculums, the secondary school system consists of three years of middle school and three years of high school.

NEIS stands for the National Education Information System. NEIS sought to centralize the personal data of students from primary and secondary schools across the country. Twenty-seven categories of personal information, including data on students’ academic records, medical history, counseling notes, and family background, are consolidated in NEIS servers maintained by local education agencies.

Ai, R., Bhatt, M., Chevrier, S., Ciccarelli, R., Grady, R., Kumari, V., Li, K., Nazarli, N., Rahimi, H., Roberts, J., Sachs, J., Schepmyer, A., Wang, M., & Wong, H. (2008). Choose your own inquiry. , University Press of America.

Afzal, S., Dempsey, B., D'Helon, C., Mukhi, N., Pribic, M., Sickler, A., Strong, P., Vanchiswar, M., & Wilde, L. (2019). The personality of AI systems in education: Experiences with the Watson tutor, a one-on-one virtual tutoring system. Childhood Education, 95 (1), 44–52.

Article   Google Scholar  

Albacete, P., Jordan, P., Lusetich, D., Chounta, I. A., Katz, S., & McLaren, B. M. (2018, June). Providing proactive scaffolding during tutorial dialogue using guidance from student model predictions. In international conference on artificial intelligence in education (pp. 20-25). Springer, Cham.

Baker, T., & Smith, L. (2019). Educ-AI-tion rebooted? Exploring the future of artificial intelligence in schools and colleges . .

Barr, V., & Stephenson, C. (2011). Bringing computational thinking to K-12: What is involved and what is the role of the computer science education community? Acm Inroads, 2 (1), 48–54.

Bakhtiar, A., Webster, E. A., & Hadwin, A. F. (2017). Regulation and socio-emotional interactions in a positive and a negative group climate. Metacognition and Learning, 13 (1), 57–90.

Big Innovation Center. (2020). AI in education: Embedding AI tools into teaching curricula. Big Innovation Centre.

Google Scholar  

Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3 , 77–101.

Caporarello, L., Magni, M. & Pennarola, F. (2019). One game does not fit all. gamification and learning: Overview and future directions. In A. Lazazzara, R. C. D. Nacamulli, C. Rossignoli, & S. Za (Eds.), Organizing for digital innovation (pp. 179–188). Lecture notes in information systems and organisation. Springer International Publishing.

Chaudhry, M. A., & Kazim, E. (2021). Artificial intelligence in education (AIEd): A high-level academic and industry note 2021. AI and Ethics .

Chin, K. Y., Hong, Z. W., & Chen, Y. L. (2014). Impact of using an educational robot-based learning system on students’ motivation in elementary education. IEEE Transactions on Learning Technologies, 7 (4), 333–345.

Chiu, T. K. (2017). Introducing electronic textbooks as daily-use technology in schools: A top-down adoption process. British Journal of Educational Technology, 48 (2), 524–537.

Cho, Y. H., Kim, K. H., & Han, J. Y. (2019). Student perception of adaptive collaborative learning support through learning analytics. The Journal of Educational Information and Media, 25 (1), 25–57.

Cho, Y., Caleon, I., & Kapur, M. (Eds.) (2015). Authentic problem solving and learning in the 21st century. Springer.

Conderman, G., & Hedin, L. (2012). Purposeful assessment practices for co-teachers. Teaching Exceptional Children, 44 (4), 18–27.

Cuzzolin, F., Morelli, A., Cîrstea, B., & Sahakian, B. J. (2020). Knowing me, knowing you: Theory of mind in AI. Psychological Medicine, 50 , 1057–1061.

Dalmazzo, D., & Ramirez, R. (2017). Air violin: A machine learning approach to fingering gesture recognition. In Proceedings of the 1st ACM SIGCHI international workshop on multimodal interaction for education (pp. 63–66). MIE 2017. New York, : ACM.

DeLiema, D., Dahn, M. Flood, V. J., Asuncion, A., Abrahamson, D., Enyedy, N., Steen, F. F. (2020). Debugging as a context for collaborative reflection on problem-solving processes. In E. Manolo (Ed.), Deeper learning, communicative competence, and critical thinking: Innovative, research-based strategies for development in 21st century classrooms (pp. 209–228). : Routledge.

Dillenbourg, P., Baker, M. J., Blaye, A., & O’Malley, C. (1995). The evolution of research on collaborative learning. In H. Spada & P. Reimann (Eds.), Learning in humans and machine: Towards an interdisciplinary learning science (pp. 189–211). Elsevier.

Dove, M. G., & Honigsfeld, A. (2017). Co-teaching for English learners: A guide to collaborative planning, instruction, assessment, and reflection . Corwin Press.

Engelbart, D. (1962). Augmenting human intellect: A conceptual framework. Summary report. Stanford Research Institute, on Contract AF , 49 (638), 1024.

Fryer, L. K., Ainley, M., Thompson, A., Gibson, A., & Sherlock, Z. (2017). Stimulating and sustaining interest in a language course: An experimental comparison of Chatbot and human task partners. Computers in Human Behavior, 75 , 461–468.

Fu, S., Gu, H., & Yang, B. (2020). The affordances of AI-enabled automatic scoring applications on learners’ continuous learning intention: An empirical study in China. British Journal of Educational Technology, 51 (5), 1674–1692.

Gunkel, D. J. (2012). The machine question: Critical perspectives on AI, robots, and ethics . MIT Press.

Guzman, A. L., & Lewis, S. C. (2020). Artificial intelligence and communication: A human–machine communication research agenda. New Media & Society, 22 (1), 70–86.

Hassani, H., Silva, E. S., Unger, S., TajMazinani, M., & Mac Feely, S. (2020). Artificial intelligence (AI) or intelligence augmentation (IA): What is the future? Ai, 1 (2), 143–155.

Hmelo-Silver, C. E., & Barrows, H. S. (2008). Facilitating collaborative knowledge building. Cognition and Instruction, 26 (1), 48–94.

Hollan, J., Hutchins, E., & Kirsh, D. (2000). Distributed cognition: Toward a new foundation for human-computer interaction research. ACM Transactions on Computer-Human Interaction (TOCHI), 7 (2), 174–196.

Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial intelligence in education: Promises and implications for teaching and learning . Center for Curriculum Redesign. The Center for Curriculum Redesign.

Huang, S. P. (2018). Effects of using artificial intelligence teaching system for environmental education on environmental knowledge and attitude. EURASIA Journal of Mathematics, Science and Technology Education, 14 (7), 3277–3284.

Hutchins, E. (1995). How a cockpit remembers its speeds. Cognitive Science, 19 (3), 265–288.

Hwang, G. J., Sung, H. Y., Chang, S. C., & Huang, X. C. (2020). A fuzzy expert system-based adaptive learning approach to improving students’ learning performances by considering affective and cognitive factors. Computers and Education: Artificial Intelligence, 1 , 100003.

Johnson, D. W., & Johnson, R. T. (2006). Cooperation and the use of technology. In D. H. Jonassen (Ed.), Handbook of research on educational communications and technology (2 ed.), (pp. 785–811). , Lawrence Erlbaum Associates.

Kafai, Y. B., & Burke, Q. (2014). Connected code: Why children need to learn programming . , MIT Press.

KERIS (2020). 2020 White paper on ICT in education Korea. Office Korea Education and Research Information Service.

Kim, J., Merrill, K., Xu, K., & Sellnow, D. D. (2020). My teacher is a machine: Understanding students’ perceptions of AI teaching assistants in online education. International Journal of Human–Computer Interaction, 36 (20), 1902–1911.

Kim, J., Pak, S., & Cho, Y. H. (2021a). The role of teachers' social networks in ICT-based instruction. The Asia-Pacific Education Researcher . https://doi.org/10.1007/s40299-020-00547-5

Kim, S., Jang, Y., Kim, W., Choi, S., Jung, H., Kim, S., & Kim, H. (2021b, May). Why and what to teach: AI curriculum for elementary school. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 35, No. 17, pp. 15569–15576).

Lee, I., Grover, S., Martin, F., Pillai, S., & Malyn-Smith, J. (2020). Computational thinking from a disciplinary perspective: Integrating computational thinking in K-12 science, technology, engineering, and mathematics education. Journal of Science Education and Technology, 29 (1), 1–8.

Lee, S. S., & Kim, J. (2020). An exploratory study on student- intelligent robot teacher relationship recognized by middle school students. Journal of Digital Convergence, 18 (4), 37–44.

Lin, Y. S., Chen, S. Y., Tsai, C. W., & Lai, Y. H. (2021). Exploring computational thinking skills training through augmented reality and AIoT learning. Frontiers in Psychology , 12.

Luckin, R., Holmes, W., Griffiths, M. & Forcier, L. B. (2016). Intelligence unleashed . An argument for AI in Education. .

McLaren, B. M., DeLeeuw, K. E., & Mayer, R. E. (2011). Polite web-based intelligent tutors: Can they improve learning in classrooms? Computers & Education, 56 (3), 574–584.

Ministry of Education. (2021). 2021 education Informatization implementation plan. Korean Ministry of Education.

Nachtigall, V., Serova, K., & Rummel, N. (2020). When failure fails to be productive: Probing the effectiveness of productive failure for learning beyond STEM domains. Instructional Science, 48 (6), 651–697.

Nardi, B. A., & O’Day, V. (1999). Information ecologies: Using technology with heart . MIT Press.

National Research Council. (2010). Report of a workshop on the scope and nature of computational thinking. The National Academies Press.

OECD. (2020). Back to the future of education: Four OECD scenarios for schooling. OECD Publishing.

Okoye, K., Nganji, J. T., & Hosseini, S. (2020). Learning analytics for educational innovation: A systematic mapping study of early indicators and success factors. International Journal of Computer Information Systems and Industrial Management Applications, 12 , 138–154.

Olmos-Peñuela, J., Benneworth, P., & Castro-Martínez, E. (2014). Are sciences essential and humanities elective? Disentangling competing claims for humanities’ research public value. Arts and Humanities in Higher Education, 14 (1), 61–78.

Ouyang, F., & Jiao, P. (2021). Artificial intelligence in education: The three paradigms. Computers and Education: Artificial Intelligence, 2 , 100020.

Peng, H., Ma, S., & Spector, J. M. (2019). Personalized adaptive learning: An emerging pedagogical approach enabled by a smart learning environment. Smart Learning Environments, 6 (1), 9.

Perkins, D. (1993). Person plus: A distributed view of thinking and learning. In G. Salomon (Eed.), Distributed Ccognitions: Psychological and Educational Considerations (pp. 88–110). Cambridge University Press.

Resnick, M. (2006). Computer as paintbrush: Technology, play, and the creative society. In D. Singer, R. Golikoff, and K. Hirsh-Pasek (Eds.), Play=learning: How play motivates and enhances children’s cognitive and social-emotional growth (pp.192–208). Oxford University Press.

Riedl, M. O. (2019). Human-centered artificial intelligence and machine learning. Human Behavior and Emerging Technologies, 1 (1), 33–36.

Rodrigues, R. S., Andrade, W. L., & Campos, L. M. S. (2016, October). Can computational thinking help me? A quantitative study of its effects on education. In 2016 IEEE Frontiers in Education Conference (FIE) (pp. 1–8). IEEE.

Self, J. A. (1998). The defining characteristics of intelligent tutoring systems research: ITSs care, precisely. International Journal of Artificial Intelligence in Education, 10 , 350–364 https://telearn.archives-ouvertes.fr/hal-00197346

Simmler, M., & Frischknecht, R. (2021). A taxonomy of human–machine collaboration: Capturing automation and technical autonomy. AI & SOCIETY, 36 (1), 239–250.

Skinner, B. F. (1958). Teaching machines. Science, 128 (3330), 969–977.

Shute, V. J., Sun, C., & Asbell-Clarke, J. (2017). Demystifying computational thinking. Educational Research Review, 22 , 142–158.

Tan, D. Y., & Cheah, C. W. (2021). Developing a gamified AI-enabled online learning application to improve students’ perception of university physics. Computers and Education: Artificial Intelligence, 2 , 100032.

UNESCO. (2019a). Steering AI and advanced ICTs for knowledge societies a rights, openness, access, and multi-stakeholder perspective . UNESCO Publishing.

UNESCO. (2019b). Beijing consensus on artificial intelligence and education . UNESCO Publishing.

UNESCO. (2021). AI and education: Guidance for policy-makers . UNESCO Publishing..

Utterberg Modén, M., Tallvid, M., Lundin, J., & Lindström, B. (2021). Intelligent tutoring systems: Why teachers abandoned a technology aimed at automating teaching processes. Hawaii International Conference on System Sciences .

Vincent-Lancrin, S., & van der Vlies, R. (2020). Trustworthy artificial intelligence (AI) in education: Promises and challenges . OECD Education Working Papers, 218.

Wang, T., & Cheng, E. C. K. (2021). An investigation of barriers to Hong Kong K-12 schools incorporating artificial intelligence in education. Computers and Education: Artificial Intelligence, 2 , 100031.

Wang, Y. (2021). Artificial intelligence in educational leadership: A symbiotic role of human-artificial intelligence decision-making. Journal of Educational Administration, 59 (3), 256–270.

Warschauer, M., & Matuchniak, T. (2010). New technology and digital worlds: Analyzing evidence of equity in access, use, and outcomes. Review of Research in Education, 34 (1), 179–225.

Wooldridge, M., & Jennings, N. R. (1995). Intelligent agents: Theory and practice. The Knowledge Engineering Review, 10 (2), 115–152.

Yeo, J., & Lee, Y. J. (2012). Knowledge advancement in environmental science through knowledge building. In K. C. D. Tan, & M. Kim (Eds.). Issues and Challenges in Science Education Research (pp. 317–332). Springer.

Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education – Where are the educators? International Journal of Educational Technology in Higher Education, 16 (1).

Zheng, G. (2020). The optimization and application of blended teaching based on artificial intelligence. 2020 3rd International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE).

Zheng, N. N., Liu, Z. Y., Ren, P. J., Ma, Y. Q., Chen, S. T., Yu, S. Y., Xue, J. R., Chen, B. D., & Rui, Y. (2017). From artificial intelligence to augmented intelligence. IEEE Multimedia, 24 (1), 4–5.

Zhu, T., Xiong, P., Li, G., Zhou, W., & Philip, S. Y. (2020). Differentially private model publishing in cyber physical systems. Future Generation Computer Systems, 108 , 1297–1306.

Download references

Availability of data and material

The authors have no relevant financial or non-financial interests to disclose.

Author information

Authors and affiliations.

Department of Education, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul, South Korea

Jinhee Kim, Hyunkyung Lee & Young Hoan Cho

You can also search for this author in PubMed   Google Scholar

Contributions

Jinhee Kim conceived the main conceptual ideas, collected the data, performed the analysis, and drafted the manuscript. Hyunkyung Lee contributed to the literature review. Young Hoan Cho  supervised the research process and contributed to the conceptual framework, material preparation, and the interpretation of findings. All authors discussed the results and commented on the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Young Hoan Cho .

Ethics declarations

The authors have no competing personal relationships that could have appeared to influence the work reported in this study.

Code availability.

Ethics approval..

This study received ethical approval from the university’s Institutional Review Board (IRB No. 2106/002–022).

Consent to participant.

This study received informed consent from all participants.

Consent for publication.

The authors accept responsibility for releasing this material to be published.

Additional information

Publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendix 1 Summary of emergent themes

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Kim, J., Lee, H. & Cho, Y.H. Learning design to support student-AI collaboration: perspectives of leading teachers for AI in education. Educ Inf Technol 27 , 6069–6104 (2022). https://doi.org/10.1007/s10639-021-10831-6

Download citation

Received : 28 August 2021

Accepted : 21 November 2021

Published : 28 January 2022

Issue Date : June 2022

DOI : https://doi.org/10.1007/s10639-021-10831-6

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• AI in education
• Student-AI collaboration
• Distributed cognition
• Learning design
• Find a journal
• Publish with us
• Track your research