AI-BASED ADAPTIVE LEARNING MODELS: THEIR INFLUENCE ON LEARNING PERSONALIZATION AND STUDENT AUTONOMY
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AI-Based Adaptive Learning Systems (ALS) promise personalized education but risk creating “algorithmic paternalism.” A critical, unexamined tension exists between system-driven optimization—which often removes learner choice—and the development of student autonomy and metacognitive skills essential for lifelong learning. This study empirically investigates this trade-off. We aimed to compare the influence of two distinct AI design philosophies—a “prescriptive” high-control model (Group A) and a “balanced” advisory model (Group B)—on both academic performance and measured student autonomy. A 15-week, mixed-methods, quasi-experiment was conducted with 284 undergraduates. Participants were assigned to the prescriptive (n=95), advisory (n=98), or a non-adaptive control (n=91) group. Autonomy was measured using the Academic Self-Regulation Questionnaire (SRQ-A) in a pre-test/post-test design. The prescriptive model (Group A) yielded the highest exam scores (87.4%), marginally outperforming the advisory model (85.9%). However, this came at a significant cost: Group A showed a statistically significant decrease in autonomy (-0.42 SRQ-A), whereas the advisory Group B showed a significant increase (+0.85 SRQ-A). The findings confirm a measurable trade-off between optimization and autonomy. Prescriptive AI poses a tangible risk to self-regulatory skill development. An advisory, “metacognitive scaffold” model represents a superior pedagogical paradigm for balancing high academic performance with the critical goal of fostering student autonomy.
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