🤖 AI Summary
This study addresses a critical gap in understanding the unique psychological and physiological burdens arising from prolonged AI tool use in academic settings. Drawing on grounded theory, it systematically codes and thematically analyzes open-ended responses from 1,054 Filipino university students to propose “AI fatigue” as a distinct construct. The research identifies five core dimensions—cognitive overload, motivational disengagement, moral unease, physical tension, and attentional drift—each operationalized through two empirically derived indicators. Furthermore, it develops a staged accumulation model that elucidates the dynamic interplay and temporal evolution of these multidimensional stressors. By delineating the structural and processual characteristics of AI fatigue, this work establishes a foundational theoretical framework for future scale development and cross-contextual investigations.
📝 Abstract
The integration of AI tools in academic settings has introduced a distinct form of strain that existing frameworks like technostress and digital fatigue have not yet fully addressed. This study develops a conceptual model and identifies the dimensions that define AI fatigue as a form of strain arising from sustained academic use of AI tools. Using grounded theory analysis of open-ended responses from 1,054 university students across three universities in the Philippines, the study examined the cognitive, motivational, emotional, physical, and attentional pressures students experienced during AI-supported academic work. Analysis produced five dimensions of AI fatigue, namely Cognitive Overload, Motivational Disengagement, Moral Unease, Physical Strain, and Attentional Drift, each consisting of two indicators grounded in participant accounts. The findings also yielded the AI Fatigue Model, a stage-based framework that explains how these pressures accumulate and reinforce one another across repeated AI interaction in academic tasks. These contributions establish a conceptual and exploratory foundation for AI fatigue as a distinct construct and provide a basis for future instrument validation, scale development, and cross-contextual inquiry in academic settings where AI now mediates student learning.