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

Cecilia Ka Yuk Chan, Wenxin Zhou
5/2/2023
cs.CYcs.AI

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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.

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Cite this paper

@article{chan2023deconstructing,
  title  = {Deconstructing Student Perceptions of Generative AI (GenAI) through an Expectancy Value Theory (EVT)-based Instrument},
  author = {Cecilia Ka Yuk Chan and Wenxin Zhou},
  year   = {2023},
  eprint = {2305.01186},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2305.01186v1}
}

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