A Systematic Review of AI-Powered Learning Assistants in Higher Education: Personalization Strategies, Adaptive Frameworks, and Deployment Challenges
DOI:
https://doi.org/10.36602/Keywords:
Adaptive learning, AI learning assistant, Systematic review, Higher Education Resource-constrained, Cold start; LibyaAbstract
This review examines personalization strategies, adaptivity frameworks, implementation challenges, and transferability to resource-constrained higher education, with emphasis on settings that have no learning management system (LMS). Following PRISMA 2020, six search sources were searched in English and Arabic between March and May 2026 for studies published from January 2018 to May 2026. Of 357 records identified, 45 studies were included. One researcher coded the studies and re-coded a 20% sample in a second pass (initial agreement 62.5%); the review is therefore exploratory, and its percentages are directional indicators. Eighteen of the 45 studies (40.0%) used assessment or LMS-generated data as the primary learner-modeling input, and four (8.9%) used interaction-timing signals. Cold start was discussed in eight studies and addressed directly in two. Only four studies reported an operational system deployed without LMS integration; most other studies coded as LMS-free were surveys or reviews that describe no deployed system. Studies from Libya most often reported infrastructure, training, and policy barriers. Evidence on deployed adaptive assistants outside LMS-based settings rests on a small number of studies, and none was found to combine platform independence, infrastructure-aware design, and handling of Arabic dialect input. Controlled evaluations in Arabic-language settings without an LMS are needed.
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Copyright (c) 2026 Laila Esmeda، Anwar Alhenshiri (Author)
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