AI-enabled adaptive learning systems: A systematic mapping of the literature
Open accessAbstract
Mobile internet, cloud computing , big data technologies, and significant breakthroughs in Artificial Intelligence (AI) have all transformed education. In recent years, there has been an emergence of more advanced AI-enabled learning systems, which are gaining traction due to their ability to deliver learning content and adapt to the individual needs of students. Yet, even though these contemporary learning systems are useful educational platforms that meet students’ needs, there is still a low number of implemented systems designed to address the concerns and problems faced by many students. Based on this perspective, a systematic mapping of the literature on AI-enabled adaptive learning systems was performed in this work. A total of 147 studies published between 2014 and 2020 were analysed. The major findings and contributions of this paper include the identification of the types of AI-enabled learning interventions used, a visualisation of the co-occurrences of authors associated with major research themes in AI-enabled learning systems and a review of common analytical methods and related techniques utilised in such learning systems. This mapping can serve as a guide for future studies on how to better design AI-enabled learning systems to solve specific learning problems and improve users’ learning experiences .
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Adaptive learning platforms powered by AI algorithms can analyse student data in real time, adapting educational content to individual learning preferences and students' pace in learning.
“Adaptive learning platforms powered by AI algorithms can analyse student data in real time, adapting educational content to individual learning preferences and students' pace in learning (Kabudi et al., 2021).”
From Higher Education's Generative Artificial Intelligence Paradox: The Meaning of Chatbot Mania (2024) · cites this paper as Kabudi et al., 2021
AI tools can promote personalized and adaptive learning, accelerate knowledge acquisition, and enhance student motivation.
“The pre-service teachers’ statements supported prior research, suggesting that AI tools can promote personalized and adaptive learning (Chiu et al., 2023; Kabudi et al., 2021), accelerate knowledge acquisition, and enhance student motivation (Chen et al. 2020a, b, c a; Chen et al. 2020a, b, c; Hwang et al. 2020).”
From Integrating artificial intelligence into preservice teacher education: enhancing AI literacy and digital material design competency (2026) · cites this paper as Kabudi et al., 2021
AI-driven learning platforms can help individuals identify skill gaps, access relevant learning resources, and track their progress, facilitating career growth and adaptability.
“AI-driven learning platforms can help individuals identify skill gaps, access relevant learning resources, and track their progress, facilitating career growth and adaptability [107].”
From New Era of Artificial Intelligence in Education: Towards a Sustainable Multifaceted Revolution (2023) · cites this paper as [107]
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Untitled work
Chan, C. K. Y., Lo, N., & Wong, G. (2024)
- Authors
- Kabudi, T., Pappas, I., & Olsen, D. H.
- Affiliations
- University of Agder (NO)
- Corresponding author
- Kabudi, Tumaini
- Venue
- Computers and Education: Artificial Intelligence
- Publisher
- Elsevier BV
- Published
- 2021
- Volume
- 2
- Pages
- 100017
- ISSN
- 2666-920X (ISSN-L)
- OpenAlex
- W3140559152
- Cited by
- 734
- References
- 82
- Citations by year
- 2026: 144 · 2025: 251 · 2024: 186 · 2023: 94 · 2022: 45 · 2021: 14
- FWCI
- 29.84
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Computers and Education Artificial Intelligence (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- NORA - Norwegian Open Research Archives (submittedVersion, cc-by)
- 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, educational technology, personalized learning, learning interventions, research topics, learning experience, system design
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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