Students’ Experiences and Meaning-Making in AI-Based Adaptive Learning in Higher Education
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Abstract
The rapid integration of AI-based adaptive learning platforms is reshaping higher education by personalizing learning experiences and transforming student engagement with digital technologies. However, while prior studies have predominantly focused on performance metrics and system efficiency, there remains a limited understanding of how students construct meaning and interpret their interactions with adaptive technologies. Addressing this gap, the present study aims to explore students’ lived experiences and meaning-making processes within AI-driven adaptive learning environments. Using an interpretative phenomenological analysis (IPA), the research contributes methodologically by foregrounding subjective, emotional, and cognitive dimensions often overlooked in technology-enhanced learning studies. Data were collected through semi-structured interviews with twelve undergraduate students and analyzed thematically using IPA to identify shared and divergent patterns of meaning. The findings highlight four key themes: personalized learning trajectories, emotional adaptation and cognitive challenges, evolving student–technology relationships, and negotiation of control and autonomy. Students reported valuing personalization and adaptive feedback, yet also expressed concerns about increased dependency on algorithmic recommendations and diminished autonomy. This study advances the literature on adaptive learning by moving beyond outcome-based evaluations to emphasize the complex interplay between technological efficiency and human-centered educational experiences. The insights not only inform the design of adaptive systems that balance optimization with learner agency, identity, and well-being but also provide a methodological contribution by applying IPA to uncover nuanced perspectives in educational technology research.
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