ChatGPT in class: motivation, schadenfreude and student usage intentions

Purpose Integrating social comparison and social identity theories, this study aims to examine students’ emotional and behavioral responses to the use of ChatGPT in academic settings, focusing on intrinsic motivation, dissonance, envy, schadenfreude and artificial intelligence (AI) usage intentions. Design/methodology/approach The research design consisted of two sequential survey-based studies with undergraduate business students. Study 1, analyzed with SmartPLS, measured students’ intrinsic motivation, cognitive engagement, dispositional envy, emotional dissonance and schadenfreude experienced in response to academic dishonesty related to ChatGPT. Study 2 explored the motivations behind students’ future use of AI tools, examining ethical considerations and emotional responses. Findings Study 1 determined that higher levels of cognitive engagement reduce dissonance and envy among highly motivated students. Nevertheless, driven by cognitive engagement, dissonance and envy, it was established that highly motivated students experience schadenfreude when others are caught misusing ChatGPT. In contrast, low-motivated students only feel schadenfreude as a product of dissonance and envy. The focus of Study 2 was on the adoption of ChatGPT. Results indicate that future usage is driven by ethical considerations for highly motivated students, whereas less dissonance is key for low-motivated students. Originality/value The study’s originality lies in its exploration of schadenfreude in the context of AI use among students, highlighting how cognitive engagement and motivation influence emotional responses. Drawing on social comparison and social identity theories, it sheds new light on the dynamics of academic integrity and the emotional landscape surrounding AI tools like ChatGPT, thus filling a research gap in understanding student behavior and perceptions in higher education.