Deconstructing Student Perceptions of Generative AI (GenAI) through an Expectancy Value Theory (EVT)-based Instrument
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Abstract (arXiv)
This study examines the relationship between student perceptions and their intention to use generative AI in higher education. Drawing on Expectancy-Value Theory (EVT), a questionnaire was developed to measure students' knowledge of generative AI, perceived value, and perceived cost. A sample of 405 students participated in the study, and confirmatory factor analysis was used to validate the constructs. The results indicate a strong positive correlation between perceived value and intention to use generative AI, and a weak negative correlation between perceived cost and intention to use. As we continue to explore the implications of generative AI in education and other domains, it is crucial to carefully consider the potential long-term consequences and the ethical dilemmas that may arise from widespread adoption.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 3 of its 83 references that are in the map. First posted on arXiv 2 May 2023.
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3 of 83 references.
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Citing papers per year: 2024: 1; 2025: 0; 2026: 2.
3 papers.
- 2024Understanding Student and Academic Staff Perceptions of AI Use in Assessment and Feedbackcited by 2
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- 2026Co-Writing with AI: An Empirical Study of Diverse Academic Writing Workflows28 Apr 2026
- 2024Understanding Student and Academic Staff Perceptions of AI Use in Assessment and Feedback22 Jun 2024