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

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. Keywords: Expectancy-Value Theory (EVT); Validated Instrument; Generative AI; ChatGPT Introduction The recent launch of ChatGPT (Schulman et al., 2022), an advanced language model based on the Generative Pre-trained Transformer (GPT) architecture, has generated significant interest and excitement in both academic and industry circles (Agrawal et al., 2022; Chui et al., 2022; Cotton et al., 2023; Mucharraz y Cano et al., 2023). With its impressive capabilities to generate coherent and contextually appropriate responses that closely mimic human-like communication, ChatGPT has the potential to become a game changer in students' lives, influencing various aspects of their personal, social and professional experiences. The increasing prevalence of artificial intelligence (AI) in various industries has led to an unprecedented surge in the demand for AI-related skills and knowledge. Generative AI(GenAI), a subset of AI that focuses on generating new content, has shown tremendous potential in applications across numerous domains, revolutionizing the way humans interact with technology and solve complex problems (Russell & Norvig, 2016). In the field of healthcare, AI has been employed in the development of predictive models, diagnosis, and treatment planning, leading to improved patient outcomes (Topol, 2019).