The rapid digital transformation of higher education has intensified the need for adaptive, data-driven, and learner-centred approaches to programming education. Programming competence is a multidimensional construct that includes theoretical understanding, algorithmic thinking, code writing, debugging, problem solving, and reflective self-regulation. Traditional instruction often applies the same content, pace, and assessment logic to all learners, although students differ significantly in prior knowledge, learning speed, motivation, error patterns, and practical coding experience. This article proposes an AI-driven adaptive learning platform model for enhancing programming competence in higher education. The model integrates student modelling, learning analytics, recommendation mechanisms, adaptive feedback, teacher analytics, and competence-based assessment. A modelled pedagogical experiment is presented to demonstrate how the platform can be evaluated using pre-test and post-test diagnostics, control and experimental groups, component-based competence indicators, and statistical analysis. The results illustrate that adaptive learning can support personalised pathways, reduce low-performance rates, and improve algorithmic, practical, and reflective programming skills. The article concludes with methodological recommendations for implementing AI-driven adaptive platforms in programming courses.
AI-Driven Adaptive Learning Platforms for Enhancing Programming Competence in Higher Education
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