Students’ Response to ChatGPT: An Adaptive Technology-To-Performance Model

Abstract

This study aims to deepen understanding of how ChatGPT’s adoption affects student learning outcomes, using an adapted Technology-to-Performance Chain (TPC) model that incorporates perceived enjoyment, task dependence, and perceived information reliability. We surveyed undergraduate students across two universities, analyzed the data through structural equation modeling (SEM), and conducted a thematic analysis of open-ended responses. The SEM validated positive correlations among task dependence and satisfaction, enjoyment and satisfaction, task dependence and perceived academic performance, and enjoyment and perceived academic performance. When considering the moderating effect of concerns about ChatGPT’s information reliability, the association between task dependence and satisfaction, as well as enjoyment and satisfaction, is stronger for students who are less concerned about information reliability. Qualitative feedback indicates students find ChatGPT useful but express concerns about information inaccuracy and over-reliance, leading to minimal human oversight in task completion. The study addresses its limitations and outlines both theoretical and practical contributions.

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