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Cited by 164Intelligent Tutoring Systems and Adaptive LearningArtificial Intelligence in EducationInnovative Teaching and Learning Methods

A systematic review of artificial intelligence techniques for collaborative learning over the past two decades

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Tan, Seng Chee, Lee, Alwyn Vwen Yen, Lee, Min·2022·Computers and Education: Artificial Intelligence
DOI 10.1016/j.caeai.2022.100097
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Abstract

This systematic review focuses on publications related to studies of the use of artificial intelligence (AI) for collaborative learning. The use of AI for collaborative learning is a recent phenomenon and a systematic review of such studies is lacking. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol, 41 journal articles were shortlisted. These articles were analysed in terms of the contexts of study (year, group size, platforms of interaction, and types of learners). Using a thematic approach, two broad foci and five sub-categories of use of AI for collaborative learning were identified: (1) learning outcomes – (a) collective performances, and (b) content of learning; (2) social interactions and processes – (c) sentiments and emotions, (d) discourse patterns and talk moves, and (e) learner characteristic and behaviours. The AI techniques were coded according to three broad purposes (discovering, learning and reasoning) and nine techniques: clustering, ensemble, regression algorithms, deep learning, decision trees, natural language processing, instance-based, fuzzy logic, and agents. A Sankey diagram was used to depict the relationships among the nine AI techniques to the five sub-categories of how AI was used to support collaborative learning. The gaps in the selected articles and limitations of the current review were discussed to suggest future areas of study.

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  • Students can use AI to explore ideas in their personal space, bring these insights into a shared learning environment, and collectively assess and refine these ideas.

    “Students can use AI to explore ideas in their personal space, bring these insights into a shared learning environment, and collectively assess and refine these ideas (Tan et al., 2022).”

    From Measuring undergraduate students’ reliance on generative AI during problem-solving: Scale development and validation. (2025) · cites this paper as Tan et al., 2022

    The Introduction and §3.2.1.2 describe AI supporting exploration of students’ ideas and providing feedback that encourages divergent ideas during collaborative learning. The paper does not describe the claimed sequence of exploring ideas in a personal space, bringing them into a shared environment, and collectively assessing and refining them. (LLM, conf=0.84)

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Cited by

Sorted by citations

Authors
Tan, S. C., Lee, A. V. Y., & Lee, M.
Affiliations
Nanyang Technological University (SG)
Corresponding author
Tan, Seng Chee
ORCID
Venue
Computers and Education: Artificial Intelligence
Publisher
Elsevier BV
Published
2022
Volume
3
Pages
100097
ISSN
2666-920X (ISSN-L)
DOI
10.1016/j.caeai.2022.100097
OpenAlex
W4294733895
Cited by
164
References
7
Citations by year
2026: 51 · 2025: 55 · 2024: 40 · 2023: 16 · 2022: 2
FWCI
1.87
Type
journal article
Language
en
Open access
Licence
Locations
  • Computers and Education Artificial Intelligence (publishedVersion, cc-by-nc-nd)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • NIE Digital Repository (National Institute of Education, Nanyang Technological University) (submittedVersion)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
  • Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
  • Social Sciences › Psychology › Developmental and Educational Psychology › Innovative Teaching and Learning Methods
Keywords
artificial intelligence, cooperative learning, AI techniques, learning outcomes, social interaction, affective learning, discourse patterns, learner behavior, clustering, ensemble methods, deep learning, natural language processing, fuzzy logic, multi-agent systems

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