Deconstructing Student Perceptions of Generative AI (GenAI) through an Expectancy Value Theory (EVT)-based Instrument
This addresses the problem of predicting AI adoption in education for educators and policymakers, but it is incremental as it applies an existing theory to a new context.
This study tackled the problem of understanding student intentions to use generative AI in higher education by developing an Expectancy-Value Theory-based questionnaire, finding a strong positive correlation between perceived value and intention to use (with a weak negative correlation for perceived cost) based on a sample of 405 students.
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.