Understanding Egyptian Private University Students' Perception towards ChatGPT Using Protection Motivation Theory

Abstract

Artificial intelligence applications are increasingly integrated into higher education, transforming how students and teachers use learning technologies. Despite the growing popularity of tools like ChatGPT, little is known about the psychological and behavioral factors influencing students' intention to use such tools. This study aims to understand the factors that affect students' behavior and intention to use ChatGPT from the students' perspective. Protection Motivation Theory (PMT), a behavioral health psychology theory, explains and predicts individual responses to emerging technologies. Many factors affect students' intention to use ChatGPT, such as trust, perceived severity, self-efficacy, ChatGPT accuracy, perceived novelty, and perceived usefulness. Three hundred and eleven students were invited to fill in the questionnaire. The data were analyzed using partial least squares structural equation modeling through SMART PLS 3.0. The main findings showed that behavioral intention is affected by trust, perceived severity, self efficacy, ChatGPT accuracy, perceived novelty, and perceived usefulness. The paper contributes theoretically and practically by introducing a framework that explains users' intentions to use ChatGPT applications.