Students’ engagement with generative AI in academic learning: A self-determination theory and epistemic network analysis study

Abstract

This qualitative case study examines undergraduate students' engagement with generative artificial intelligence (GenAI) in academic learning in an English-medium university setting. This study employs self-determination theory (SDT) as the primary interpretive framework, treating technology acceptance perceptions (e.g., usefulness and ease of use) as descriptive cues rather than explanatory constructs. Data from 23 semi-structured interviews were analyzed using reflexive thematic analysis, complemented by epistemic network analysis (ENA) to examine the structural relationships among themes in students' discourse, with automated coding validated against a manually coded subset. Students frequently described GenAI as supporting efficiency and conceptual understanding; however, their accounts revealed persistent tensions concerning creativity, trust, and academic integrity. The ENA results showed that these concerns were systematically interconnected: discussions of learning support consistently co-occurred with verification practices, reflecting a “trust-but-verify” repertoire through which students calibrated their reliance on AI while maintaining epistemic control. Beyond instrumental evaluations, students' narratives highlighted broader value- and norm-related considerations, including algorithmic bias, environmental sustainability, and the positioning of AI within human–teacher learning networks. Overall, the findings suggest that students' engagement with GenAI is best understood as a motivated and socially situated learning practice shaped by the negotiation of competence, autonomy, and relatedness. Pedagogically, the results support a shift from prohibition-oriented responses to transparent institutional guidance, autonomy- supportive scaffolding of verification practices and AI literacy, and process-oriented assessment designs that make students' reasoning visible. Learning analytics approaches, such as ENA, may further assist educators in examining how these practices become integrated into students’ learning processes.