Determinants of Responsible Generative Artificial Intelligence Use among Higher Education Students: Integrating Technology Beliefs, Ethical Awareness and Social Norms

This study tested an integrated TAM-TPB model of responsible generative artificial intelligence use among 382 higher education students. PEOU, PU and EA significantly predicted ATT, while PU, ATT and SN significantly predicted RGI. EA was the strongest predictor of ATT and ATT was the strongest predictor of RGI. ATT also partially mediated the relationship between PU and RGI. Gender differences were not significant, whereas use-frequency groups differed across the constructs. The findings establish that responsible academic use is shaped by more than technology-related convenience or utility. Ethical awareness, favourable attitudes towards responsible practice and clear social expectations make substantial contributions to students' intentions. The model therefore provides a focused explanation of how technology beliefs and academic-integrity conditions operate together in higher education. Future research can extend the framework through longitudinal analysis, behavioural measures, perceived behavioural control, institutional policy clarity and comparisons across disciplines and institutions. Multi-group analysis can examine whether structural relationships differ by study level, discipline or institutional type, while qualitative research can clarify how students interpret verification, acknowledgement and independent contribution in different assessment settings. These extensions would deepen understanding of how responsible-use intentions translate into academic practice.

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