Patterns and Rationalizations of Ai-Mediated Academic Dishonesty among University Students: A Systematic Literature Review

This systematic literature review aims to synthesize empirical evidence on the patterns, prevalence, and rationalizations of generative artificial intelligence (GenAI) use among university students, with a specific focus on academic dishonesty and ethical boundary negotiations in higher education contexts. Following the PRISMA framework, a comprehensive search was conducted across Scopus, Web of Science, Google Scholar, SpringerLink, ScienceDirect, IEEE Xplore, and ACM Digital Library for peer-reviewed studies published between 2023 and 2025. Inclusion criteria encompassed empirical studies examining GenAI use by university students in academic contexts. After duplicate removal and screening, 23 studies meeting the criteria were included for narrative synthesis and thematic analysis. The review indicates high prevalence rates of GenAI use (50-80% across studies), primarily for academic writing support, concept understanding, literature summarization, and idea generation. Students actively construct ethical boundaries, rationalizing AI use for brainstorming and editing as acceptable while viewing complete assignment generation as dishonest. Rationalizations are influenced by productivity needs, academic pressures, social norms, and unclear institutional policies. Moral disengagement and situational ethics emerge as common justifications for boundary-crossing behaviors. AI-mediated academic dishonesty represents a complex socio-technical phenomenon involving continuous moral negotiation rather than simple cheating. Institutions must develop clear AI policies, integrate AI literacy into curricula, and redesign assessments to promote ethical engagement. Future research should employ longitudinal and cross-cultural designs to better understand the evolving dynamics of student-AI interactions in academic settings.