The DOI matches its Crossref record; 123 of the 126 references it cites were found in a registry or an index, and 1 are not checked yet.
Artificial intelligence-enabled adaptive learning platforms: A review
Open accessAbstract
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 [3].”
From AI for All: Adaptive, Accessible, and Inclusive Learning Experiences in the Age of Intelligent LMSs (2026) · cites this paper as [3]
In the citing paper
“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]
Citations
Cites
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The 2 Sigma Problem: The Search for Methods of Group Instruction as Effective as One-to-One Tutoring
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Educational data mining and learning analytics: An updated survey
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Adaptive Learning Using Artificial Intelligence in e-Learning: A Literature Review
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- Muñoz, 2022, Systematic review of adaptive learning technology for learning in higher education, Eurasian Journal of Educational Research, 98, 221
Cited by
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Items 201–207 of 207
Liu, Y., Kamarudin, N., & Sulaiman, T. (2026) · Frontiers in Psychology · doi:10.3389/fpsyg.2026.1908596
Datar, Y. (2026) · Quality Assurance in Education · doi:10.1108/qae-03-2026-0090
EDUVOYAGER: An AI-Orchestrated Cognitive Learning Path Way System
Sambhare, P., Khilare, V., More, S., Bane, K., & Vibhute, A. (2026) · International Conference on Information, Implementation, and Innovation in Technology (I2ITCON) · doi:10.1109/i2itcon69735.2026.11747594
Auna, H. S. A., Kuswandi, D., Praherdhiono, H., Degeng, M. D. K., & Ulfa, S. U. (2026) · Participatory Educational Research · doi:10.17275/per.26.83.13.6
PEFT-Enabled AI Scaffolding for Adaptive Problem-Based Learning A Systematic Literature Review
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Metadata
- 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
- Social Sciences › Education › 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
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