Adaptive and Intelligent Systems for Collaborative Learning Support: A Review of the Field
Abstract
This study critically reviews the recently published scientific literature on the design and impact of adaptive and intelligent systems for collaborative learning support (AICLS) systems. The focus is threefold: 1) analyze critical design issues of AICLS systems and organize them under a unifying classification scheme, 2) present research evidence on the impact of these systems on student learning, and 3) identify current trends and open research questions in the field. After systematically searching online bibliographic databases, 105 articles were included in the review with 70 of them reporting concrete evaluation data on the learning impact of AICLS systems. Systems design analysis led us to propose a classification scheme with five dimensions: pedagogical objective, target of adaptation, modeling, technology, and design space. The reviewed articles indicate that AICLS systems increasingly introduce Artificial Intelligence and Web 2.0 techniques to support pretask interventions, in-task peer interactions, and learning domain-specific activities. Findings also suggest that AICLS systems may improve both learners' domain knowledge and collaboration skills. However, these benefits are subject to the learning design and the capability of AICLS to adapt and intervene in an unobtrusive way. Finally, providing peer interaction support seems to motivate students and improve collaboration and learning.
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These techniques provide potential indications for investigating the formation of homogeneous and heterogeneous groups in an educational context.
“ese techniques provide potential indications for the investigation of forming homogeneous and heterogeneous groups in an educational context [21].”
From A Review of Artificial Intelligence (AI) in Education from 2010 to 2020 (2021) · cites this paper as [21]
To design successful adaptive systems in education, curriculum designers and system designers have to include the modelling of the problem-solving process in the specific domain knowledge and the use of big data.
“To design successful adaptive systems in education, curriculum designers and system designers have to leverage on to include the modelling of the problem-solving process in the specific domain knowledge and the use of big data [21,44].”
From A Review of Artificial Intelligence (AI) in Education from 2010 to 2020 (2021) · cites this paper as [21]
Deeper internal characters, such as human mental status and creativity, were barely noticed and studied in AI in education research.
“e deeper internal characters, such as human mental status and creativity, were barely noticed and studied [21].”
From A Review of Artificial Intelligence (AI) in Education from 2010 to 2020 (2021) · cites this paper as [21]
The interview and the review paper have surfaced the challenges or misunderstanding of AI in education.
“However, the interview and the review paper have, respectively, surfaced the challenges or misunderstanding of AI in education [4,21].”
From A Review of Artificial Intelligence (AI) in Education from 2010 to 2020 (2021) · cites this paper as [21]
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BackgroundInfluential<scp>AI</scp>‐driven adaptive learning for sustainable educational transformation
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Dahl, J. E., & Mørch, A. (2025) · Education and Information Technologies · doi:10.1007/s10639-025-13636-z · Cited by 12 · see citing works
- Authors
- Magnisalis, I., Demetriadis, S., & Karakostas, A.
- Affiliations
- Aristotle University of Thessaloniki (GR)
- Venue
- IEEE Transactions on Learning Technologies
- Published
- 2011-01
- Volume
- 4
- Issue
- 1
- Pages
- 5-20
- ISSN
- 1939-1382 (ISSN-L)
- OpenAlex
- W2098992993
- Cited by
- 231
- References
- 105
- Citations by year
- 2026: 7 · 2025: 10 · 2024: 17 · 2023: 22 · 2022: 13 · 2021: 7 · 2020: 16 · 2019: 17 · 2018: 28 · 2017: 20 · 2016: 19 · 2015: 14 · 2014: 15 · 2013: 9 · 2012: 11
- FWCI
- 12.08
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Learning Technologies (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Social Sciences › Psychology › Developmental and Educational Psychology › Innovative Teaching and Learning Methods
- Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
- Keywords
- classification systems, learning effects, peer interaction, Web 2.0, artificial intelligence in education, collaboration skills, domain knowledge, student motivation
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