AI-Assisted Literary Reading: College Students' Acceptance and User Experience

Mingqin Zhou

doi:10.70088/94wkzk40

Authors

Mingqin Zhou Wannan Medical University, Wuhu, China Author

DOI:

https://doi.org/10.70088/94wkzk40

Keywords:

AI-assisted reading, literary reading, Technology Acceptance Model (TAM), college students, humanities education, human-AI interaction

Abstract

Digital-era college students are reading less literature, thanks to information overload, short attention spans, and texts that can be hard to get through. Large language models (LLMs)—a type of AI—might offer a way to support literary reading. Using the Technology Acceptance Model (TAM), this mixed-methods study looked at 150 Chinese undergraduates and postgraduates, asking about their willingness to use AI for reading literature, how they actually use it, what drives that use, and what their experience is like. Tests show our measures are reliable (Cronbach's α = 0.970; KMO = 0.974, Bartlett's χ² = 2147.750, p < 0.001). Regression results suggest that two things seem to predict acceptance: AI that supplies background context (β = 0.255, p = 0.070) and enjoyable AI-led discussions (β = 0.268, p = 0.054). ANOVA and SNK post-hoc tests indicate that students who actively explore with AI have clearly higher acceptance (M = 4.2308) than those who are neutral (M = 3.1667). Students most often use AI to map character relationships, decode classical references, fill in historical background, and analyse writing techniques—though humanities and STEM majors differ in what they prioritize. Students appreciate how AI lowers barriers to reading, but they worry about becoming over-reliant and losing the ability to read deeply. The takeaway: AI should work as a cognitive scaffold, not a replacement for real literary engagement. This study offers practical pointers for weaving AI into literature teaching.

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