Text Mining Analysis of Students’ Experiences and Fatigue Towards Frequent AI Tool Use in Academic Work

Resumo

The growing use of artificial intelligence (AI) tools among college students has raised concerns about their psychological and behavioral consequences, particularly in relation to academic fatigue and dependence. However, student-reported experiences with frequent AI tool use remain underexplored in the text mining literature. This study analyzed the open-ended survey responses of 491 college students using text mining methods to determine their experiences and fatigue toward frequent AI tool use in academic work. A total of 14,442 tokens from 2,328 unique words were analyzed. Using hierarchical cluster analysis, the students’ experiences were classified into three themes: AI-assisted academic output; AI and learning engagement; and the cognitive and behavioral impacts of AI dependence. Sentiment analysis revealed that students had predominantly negative sentiments toward frequent AI tool use, driven by words such as lazy, depend, tired, and unmotivate. The Chi-square test confirmed that negative sentiments significantly outnumbered positive ones, χ²(1) = 47.25, p < .001. These findings suggest that while students recognize the convenience of AI tools, frequent use has led to dependence, reduced motivation, and cognitive fatigue. Implications for teaching practice and future research directions are discussed.