Defining AI Fatigue in Academic Contexts: Dimensions, Indicators, and a Stage-Based Model Using Grounded Theory

This grounded theory study analyzed open-ended responses from 1,054 university students across three Philippine universities to define AI fatigue as a distinct construct — separate from technostress and digital fatigue. The analysis identified five dimensions, each with two indicators grounded in participant accounts: (1) Cognitive Overload — mental exhaustion from processing AI outputs and prompts; (2) Motivational Disengagement — loss of drive to engage when AI can complete tasks; (3) Moral Unease — ethical discomfort from AI dependency and plagiarism concerns; (4) Physical Strain — bodily fatigue from prolonged AI interaction; and (5) Attentional Drift — difficulty maintaining focus amid AI-mediated multitasking. The resulting AI Fatigue Model is a stage-based framework showing how these dimensions accumulate and reinforce each other across repeated AI interactions.

This construct has significant implications for Over Reliance research: AI fatigue may be the endpoint of sustained Cognitive Offloading and dependency, where the efficiency-gain illusion gives way to genuine cognitive depletion. For AI Literacy interventions, the model suggests that teaching technical AI skills without addressing the affective and motivational costs is incomplete — students need strategies for managing AI fatigue, not just using AI effectively. The Motivational Disengagement dimension directly threatens Self Regulated Learning capacity, as students lose intrinsic drive when AI stands ready to complete tasks. The work also contributes to Affective Computing by formalizing the negative affective dimension of sustained AI interaction. As the first conceptual model of AI fatigue in academic contexts, this provides a foundation for instrument development and intervention design, with direct relevance to Student Experience in AI-mediated learning environments.

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John Paul P. Miranda, Emmanuel B. Parreno, Jovita G. Rivera (2026). Defining AI Fatigue in Academic Contexts: Dimensions, Indicators, and a Stage-Based Model Using Grounded Theory. arXiv:2605.23123. International Journal of Learning, Teaching and Educational Research, 25(5), 91-107 (2026). - Digital Literacy Illusion — Overconfident students may paradoxically disengage from AI learning - AI Productivity Moderation — Incentive structures moderate whether AI adoption leads to fatigue or growth