Students’ behavioral intentions toward generative AI in education: Task-technology fit and moral obligations - Yang, Yann-Jy; Wang, Chih-Chien; Chen, Yi-Hsuan; Chen, Hui-Min (Journal of Education and e-Learning Research, 2025)
The rapid advancement of generative AI tools, such as ChatGPT, has sparked widespread debate over their impact on academic integrity and educational practices. As these tools become increasingly accessible to students, understanding the factors that influence their adoption in academic settings is essential. The current study explores the application of generative artificial intelligence (AI) tools by college students, such as ChatGPT and many others, for completing homework assignments. Drawing on the Task-Technology Fit (TTF) framework and the concept of moral obligation, this research aims to investigate the factors influencing students’ behavioral intentions to use generative AI in academic contexts. Data were collected through an online survey of 136 Taiwanese college students. The results indicate that perceived technology characteristics and self-efficacy significantly enhance task-technology fit, positively affecting behavioral intention. Conversely, moral obligation shaped by perceived teacher attitudes negatively influences students’ intention to use AI tools for coursework. The study employs Partial Least Squares Structural Equation Modeling (PLS-SEM) to test the hypotheses and explains a substantial proportion of the variance in behavioral intention. These findings provide theoretical insights into how technological and ethical considerations jointly influence AI adoption in education. The study also offers practical suggestions for educators and institutions aiming to guide the responsible use of generative AI in learning environments. This study contributes a novel framework for understanding responsible AI use in higher education.