THE IMPACTS OF GENERATIVE AI ON ACADEMIC INTEGRITY IN HIGHER EDUCATION: A SYSTEMATIC LITERATURE REVIEW

Abstract

The advent of generative artificial intelligence (AI) has raised serious concerns of students’ academic integrity in higher education institutions (HEIs). These academic misconducts undermine the credibility of education institutions and pose significant risks to long-term students’ development of skills that are crucial for their future career prospects. Therefore, this study aims to explore the impacts of generative AI on students’ academic integrity and suggested policy and pedagogical interventions to remedy the situation. It utilized a systematic literature review method, guided by the PRISMA framework. Articles in SCOPUS and ERIC databases were rigorously assessed, resulting in the selection of 15 eligible empirical studies. The findings revealed that generative AI-related misconducts are tied to advanced AI capabilities, demographic and cultural factors, and policy gaps. These can be mitigated through AI ethics-integrated curricula, clear national directives, and adaptive assessment strategies, achieved through joint collaboration between stakeholders. This review highlights the complexity of generative AI’s impacts on students’ learning behaviours and suggests crucial evidence-based pedagogical practices in HEIs. These can be used to inform the development of comprehensive global policies pertaining to ethical use of generative AI among students.