Defining AI Fatigue in Academic Contexts: Dimensions, Indicators, and a Stage-Based Model Using Grounded Theory
A recent study explores the concept of AI fatigue in academic contexts, highlighting its distinct nature compared to existing frameworks. The research identifies five dimensions of AI fatigue, including cognitive overload and motivational disengagement, based on responses from over a thousand university students. This study aims to establish a foundation for understanding AI fatigue and its implications for student learning.
- ▪The study develops a conceptual model for AI fatigue, identifying dimensions that arise from sustained academic use of AI tools.
- ▪Five dimensions of AI fatigue were identified: Cognitive Overload, Motivational Disengagement, Moral Unease, Physical Strain, and Attentional Drift.
- ▪The findings led to the creation of the AI Fatigue Model, a stage-based framework explaining how pressures accumulate during AI interactions.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.23123 |
| Publication time | Mon, 25 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-25T04:07:35.648Z |
| Last seen | 2026-05-25T04:07:35.648Z |
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Computer Science > Computers and Society arXiv:2605.23123 (cs) [Submitted on 22 May 2026] Title:Defining AI Fatigue in Academic Contexts: Dimensions, Indicators, and a Stage-Based Model Using Grounded Theory Authors:John Paul P. Miranda, Emmanuel B. Parreño, Jovita G. Rivera View a PDF of the paper titled Defining AI Fatigue in Academic Contexts: Dimensions, Indicators, and a Stage-Based Model Using Grounded Theory, by John Paul P. Miranda and 2 other authors View PDF 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.
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