ITAA - KSCT Joint Symposium

Understanding Student Adoption of Generative AI for Writing: A Technology Acceptance Model Approach

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Abstract

This study examines college students’ adoption of generative AI (GenAI) for writing assignments using an extended Technology Acceptance Model (TAM). Survey data were collected from 197 U.S. college students using ChatGPT as the focal context. Results from structural equation modeling show that perceived performance and perceived fun significantly and positively influence students’ attitudes toward GenAI use, whereas perceived ease-of-use does not. Attitude, in turn, positively predicts intention to use GenAI for writing. However, the presence of a potential academic penalty significantly weakens this relationship. Moderation analyses further reveal that need-for-cognition strengthens the effect of perceived performance on attitude but weakens the effect of perceived fun. These findings offer practical implications for educators seeking to both encourage responsible use and regulate inappropriate reliance on GenAI tools.

Keywords: generative AI, writing assignment, technology acceptance model

How to Cite:

Hyun, J. & Slaton, K., (2025) “Understanding Student Adoption of Generative AI for Writing: A Technology Acceptance Model Approach”, International Textile and Apparel Association Annual Conference Proceedings 1(1). doi: https://doi.org/10.31274/itaa.21380