Do Human Beliefs and Traits Influence the Adoption of ChatGPT among Programming Students?

Abstract

Abstract: Increased use of generative artificial intelligence or AI in various academic activities such as programming is a significant milestone in technology diffusion in learning. To bring AI closer to how programmers think, behave, and interact, it is imperative for research to establish a clear connection between various human factors that lead to its adoption. Using a model based on the Theory of Reasoned Action, we positioned human traits of academic stress, risk propensity, neuroticism, and computer self-efficacy as factors that positively influence attitudes toward the use of AI in programming among university students. We further posited that attitude and social norms lead to the behavioral intention to use AI in programming. We used PLS-SEM to analyze responses from 131 programming students who use ChatGPT to accomplish learning tasks. We found that both academic stress and computer self-efficacy influence attitudes toward using AI in programming. While attitude positively influences the behavioral intention to use ChatGPT, we found that risk propensity and neuroticism do not affect attitude, and social norms do not influence behavioral intention. We discuss the implications of our investigation to the industry and the academe.

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