AI-assisted academic cheating: a conceptual model based on postgraduate student voices

Introduction As AI tools become more widely used in higher education, concerns about AI-assisted academic cheating are growing. This study examines how postgraduate students interpret these behaviors. Methods We conducted an exploratory qualitative study. We analyzed ten ten course-embedded reflective essays using conventional content analysis and identified 159 meaning units, 34 codes, 12 categories, and 6 themes. Results Students described two main forms of AI-assisted cheating: misusing AI to complete academic tasks and improperly using AI-generated content. They attributed these behaviors to work pressure, ethical ambiguity, AI affordances, and gaps in institutional policies. They also proposed solutions, including clearer guidelines, improved assessment design, and stronger ethics education. Discussion The findings show that students construct their views on AI-assisted cheating within social, technological, and institutional contexts. Strengthening policy clarity and promoting a culture of ethical AI use can help institutions address these emerging challenges.