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.

翻译：人工智能工具在学术环境中的整合带来了现有技术压力和数字疲劳等框架尚未充分揭示的一种独特压力形式。本研究构建了一个概念模型，并明确了将AI疲劳界定为持续使用AI工具从事学术活动所产生压力形式的关键维度。通过对菲律宾三所大学1,054名大学生开放式回答的扎根理论分析，本研究考察了学生在开展AI辅助学术任务时面临的认知、动机、情绪、身体和注意力层面的压力。分析得出AI疲劳的五个维度——认知过载、动机脱离、道德不安、身体疲劳与注意力漂移，每个维度均由两个基于参与者陈述的指标构成。研究结果还提出了AI疲劳模型这一分阶段框架，用以阐释在学术任务中反复进行AI交互时，这些压力如何累积并相互强化。这些成果为将AI疲劳确立为独立概念奠定了探索性理论基础，并为未来在AI中介学生学习的学术情境中开展工具验证、量表开发及跨情境研究提供了依据。