Higher Education: A Critical Synthesis of Pedagogical Foundations, Implementation Challenges, and Future Trajectories
DOI:
https://doi.org/10.64235/36nbb244Keywords:
Adaptive learning systems, artificial intelligence, higher education, personalized learning, intelligent tutoring systems, educational technologyAbstract
The integration of artificial intelligence into adaptive learning systems has generated considerable scholarly interest and institutional investment across global higher education contexts. This paper presents a systematic critical synthesis of the theoretical foundations, empirical evidence, and implementation challenges surrounding AI driven adaptive learning technologies in university settings. Drawing upon a comprehensive review of peer reviewed scholarship published between 2019 and 2026, we identify a persistent disjuncture between the transformative claims advanced by technology advocates and the methodologically grounded evidence base that has accumulated to date. The analysis reveals that while AI powered adaptive systems demonstrate measurable benefits for specific learning outcomes, particularly in domains requiring structured knowledge acquisition and skills based competency development, the generalizability of these findings across disciplinary contexts remains substantially limited by heterogeneity in research designs, outcome measures, and implementation conditions. Furthermore, we argue that much of the current literature has inadequately addressed the pedagogical theories that should inform system design, resulting in technology driven rather than learning centered approaches. The paper concludes by proposing an integrative framework that aligns adaptive learning system development with established principles from constructivist, self regulated learning, and cognitive load theories, while identifying specific priorities for future longitudinal and comparative research.
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