Deconstructing Student Perceptions of Generative AI (GenAI) through an Expectancy Value Theory (EVT)-based Instrument

TL;DR

This paper investigates how students' perceptions of generative AI influence their willingness to use it in higher education, using an EVT-based questionnaire validated with a student sample.

Contribution

It introduces a new EVT-based instrument to measure student perceptions of generative AI and analyzes their impact on usage intentions.

Findings

Perceived value strongly predicts intention to use AI.

Perceived cost has a weak negative effect on usage intention.

The instrument is validated through confirmatory factor analysis.

Abstract

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.