How do College Students Express AI Fatigue? A Content and Keyword-in-Context Analysis of Academic AI Use

Abstract

This study examined how Filipino college students express AI fatigue in academic contexts. Using a qualitative descriptive design, the study analyzed 1,000 open-ended online survey responses collected from public higher education institutions in the Philippines through purposive convenience sampling. Directed qualitative content analysis, complemented by keyword-in-context (KWIC) analysis, was used to identify and contextualize fatigue-related expressions in student narratives. Analysis revealed four categories: cognitive fatigue, emotional fatigue, motivational fatigue, and absence of fatigue expression. Cognitive fatigue emerged as the most frequently expressed dimension, characterized by mental tiredness, information overload, and difficulty sustaining focus. Motivational and emotional fatigue were also evident and frequently co-occurred with cognitive fatigue. A substantial proportion of responses described AI use without any fatigue-related strain. These findings indicate that AI fatigue is multidimensional and unevenly experienced. The findings suggest that the use of academic AI does not uniformly reduce effort but may shift cognitive demands toward evaluation and information processing. By grounding interpretation in students’ own language, the study contributes empirical evidence to discussions on sustainable AI integration in higher education, particularly within digitally intensive developing contexts.

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