提出学术场景下AI疲劳的五维模型，揭示学生使用AI时的心理与身体压力。
Defining AI Fatigue in Academic Contexts: Dimensions, Indicators, and a Stage-Based Model Using Grounded Theory
- 基于1054名学生访谈，提炼出认知过载等五类核心压力维度。
- 发现持续使用AI会引发压力累积与相互强化的阶段式疲劳过程。
- 为未来评估工具开发和跨场景研究提供理论基础，适合教育科技研究者。
AI工具在学术场景中的融合引入了一种现有技术压力与数字疲劳框架尚未充分涵盖的新型压力形式。本研究通过扎根理论分析菲律宾三所大学共1054名大学生的开放性反馈，探究了学生在使用AI支持学术任务时面临的认知、动机、情感、身体及注意力方面的压力。分析提炼出五个维度：认知过载、动机疏离、道德不安、身体负担与注意力漂移，每个维度包含两个基于受访者陈述的指标。研究进一步构建了AI疲劳模型——一个阶段性的框架，解释这些压力如何在重复使用AI过程中积累并相互增强。该成果为将AI疲劳确立为独立构念奠定了概念与探索性基础，也为未来量表验证、工具开发及跨情境研究提供了依据。
原文摘要 · Abstract (English)
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.
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