Artificial intelligence and adaptive learning in education: Barriers, opportunities, and policy implications - Ledesma-Acosta, Brigita Valeria; Penafiel-Romero, Gabriela Elizabeth; Peralta-Gamboa, Dennis Alfredo (Edelweiss Applied Science and Technology, 2026)
This research conducts a systematic literature review examining AI-driven adaptive learning systems and their deployment within educational environments. Drawing on 110 peer-reviewed sources retrieved from Scopus and Web of Science, the analysis investigates the ways these technologies foster individualized learning experiences, while pinpointing major advantages and substantial obstacles. The investigation focuses on three core dimensions: (1) technological and infrastructural obstacles impeding effective rollout, (2) the influence of such systems on learner motivation, involvement, and scholastic results, and (3) essential ethical issues, including algorithmic discrimination, protection of personal information, and system explainability. Results indicate reliable improvements in educational achievement, most notably in STEM fields and virtual learning settings, although they also expose serious concerns regarding access disparities and regulatory oversight. The article offers practical, implementable policy guidance and emphasizes the vital contributions of academic librarians, scholarly publishers, and educational technology providers in promoting ethical integration. Ultimately, the review stresses that multi-stakeholder partnerships, strong ethical guidelines, and equitable digital infrastructure are essential to realizing the full transformative capacity of AI-supported adaptive learning in a fair and enduring way.