John Paul P. Miranda, Emmanuel B. Parreno, Jovita G. Rivera (2026) — Don Honorio Ventura State University / Bulacan State University, Philippines. International Journal of Learning, Teaching and Educational Research, 25(5), 91-107 (2026).
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.
Related Pages
- student-experience — 1,054 students identify five dimensions of AI-related strain
- over-reliance — AI fatigue as downstream consequence of sustained AI dependency
- ai-literacy — Fatigue dimensions reveal gaps in AI literacy interventions
- cognitive-offloading — Cognitive Overload dimension mirrors offloading patterns
- self-regulated-learning — Motivational Disengagement undermines SRL capacity
- affective-computing — Affective strain from AI use formalized as distinct construct
Citation
APA: 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