Artificial intelligence-enabled adaptive learning platforms: A review
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This review provides a comprehensive analysis of adaptive learning platforms (ALPs) in education, focusing on their pedagogical foundations, AI-driven implementations, and the challenges and successes of real-world applications. By collecting and analyzing learner data, ALPs dynamically adjust instructional content and pathways to offer personalized learning experiences, enhancing learning outcomes. The paper explores ALP design frameworks, core algorithms, and evaluation metrics, and assesses their performance across diverse educational contexts through case studies. Lastly, the review highlights key challenges ALPs face, such as privacy concerns and faculty support for implementation, and offers insights into future trends in developing adaptive technologies. This review provides educators and researchers with valuable insights into how ALPs can be effectively applied in various educational settings.
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This automation significantly reduces instructor workload while maintaining consistency across learning resources and supports scalable adoption in institutional settings.
“This automation significantly reduces instructor workload while maintaining consistency across learning resources and supports scalable adoption in institutional settings [3].”
From AI for All: Adaptive, Accessible, and Inclusive Learning Experiences in the Age of Intelligent LMSs (2026) · cites this paper as [3]
Integrating adaptivity directly into widely used LMS platforms has been repeatedly identified as a key challenge and opportunity for scalable personalization.
“Integrating adaptivity directly into widely used LMS platforms has been repeatedly identified as a key challenge and opportunity for scalable personalization [3].”
From AI for All: Adaptive, Accessible, and Inclusive Learning Experiences in the Age of Intelligent LMSs (2026) · cites this paper as [3]
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- Authors
- Tan, L. Y., Hu, S., Yeo, D. J., & Cheong, K. H.
- Affiliations
- Nanyang Technological University (SG)
- Corresponding author
- Cheong, Kang Hao
- Venue
- Computers and Education: Artificial Intelligence
- Publisher
- Elsevier BV
- Published
- 2025-12
- Volume
- 9
- Pages
- 100429
- ISSN
- 2666-920X (ISSN-L)
- OpenAlex
- W4410882319
- Semantic Scholar
- dde418c0ff78aea0c101f3a7c2c86ab90dd98fac
- Cited by
- 207
- References
- 126
- Citations by year
- 2026: 151 · 2025: 56
- FWCI
- 15.42
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Computers and Education Artificial Intelligence (publishedVersion, cc-by-nc-nd)
- Funders
- Government of Singapore Ministry of Education (MOE2022-TRF-029)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
- Keywords
- adaptive learning systems, artificial intelligence in education, personalized learning, adaptive learning algorithms, learner data, student privacy, faculty support
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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