Artificial-intelligence-based adaptive learning systems (AI-ALS) are increasingly promoted as a means of personalizing instruction at scale, yet their design in higher education continues to be driven predominantly by technical and algorithmic considerations rather than by explicit pedagogical reasoning. This gap between technological capability and pedagogical grounding limits the instructional effectiveness of many implemented systems and complicates their adoption by teaching staff. The purpose of this study is to substantiate the pedagogical and methodological foundations that should underpin the design of AI-based adaptive learning systems for university students. The study employed a theoretical-analytical design based on a comparative-pedagogical analysis of ten peer-reviewed and internationally recognized sources spanning foundational learning-science research, systematic literature reviews, practitioner design frameworks, and recent empirical interventions. The analysis identifies four interdependent architectural-pedagogical components that any AI-ALS must integrate - the learner model, the domain model, the pedagogical model, and the adaptive engine and interface - and specifies the didactic principles that should govern each component, including the sequencing of scaffolded support, the calibration of adaptivity to a learner's zone of proximal development, and the maintenance of teacher agency within algorithmically mediated instruction. The findings show that systems designed around an explicit pedagogical model, rather than around algorithmic capability alone, produce more consistent gains in engagement, motivation, and academic performance. The study concludes with a structural-methodological model and a set of design recommendations intended to guide instructional designers, educational technologists, and university administrators in the pedagogically sound implementation of AI-based adaptive learning systems.
Pedagogical and Methodological Foundations for Designing Artificial-Intelligence-Based Adaptive Learning Systems in Higher Education
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