Balancing AI-Driven Adaptation and Learner Self-Regulation: A Conceptual Framework for Enhancing Learning Outcomes in Generative AI-Supported Education
Author:
Maram Abdullah Al-Harthi
Master's in Educational Technology, University of Nizwa, Sultanate of Oman
doi.org/10.52132/Ajrsp.e.2026.90.3
The rapid proliferation of Generative Artificial Intelligence (GenAI) has expanded the potential for personalization, feedback, and immediate support within educational environments. Despite growing evidence of its ability to enhance certain learning outcomes, increased AI-driven support does not necessarily improve all cognitive and regulatory aspects; indeed, it may raise challenges regarding over-reliance and a decline in learner autonomy. This conceptual paper aims to develop a pedagogical framework to balance AI-driven adaptation with learner self-regulation in GenAI-supported educational settings. The framework was constructed based on an integrative analytical review of recent literature in the fields of GenAI in education, adaptive learning, self-regulated learning, and human-AI hybrid regulation. The framework proposes the concept of "Balanced AI–Learner Regulation," which structures the relationship across three graduated levels: AI-led regulation, co-regulation, and learner-led regulation. It also proposes the principle of "Adaptive Fading of AI Support," based on providing support according to need and then gradually reducing it as the learner’s capacity for independent self-regulation grows. The paper’s primary contribution lies in shifting the focus from maximizing GenAI usage to designing a pedagogical balance that preserves autonomy, self-regulation, and critical thinking alongside the improvement of academic performance.
Keywords:
Generative Artificial Intelligence; Adaptive Learning; Self-regulated learning; human-AI coordination; learning outcomes; learner autonomy
Academic Journal of Research and Scientific Publishing
