A DOUBLE-EDGED SWORD: WEIGHING THE POTENTIAL AND RISKS OF GENERATIVE AI IN MAINTAINING ACADEMIC INTEGRITY IN HIGHER EDUCATION - Multajimah; Mohamed Shifaz; Aminath Nafeeza (Al-Hijr: Journal of Adulearn World, 2025)
Generative artificial intelligence has rapidly penetrated higher education, reshaping academic writing, assessment practices, and knowledge production, while simultaneously raising serious concerns about academic integrity. This study aims to examine generative AI as a double-edged phenomenon by analyzing its potential benefits and associated risks for maintaining academic integrity in higher education institutions. The research employed a qualitative-dominant mixed analytical design, combining systematic literature review, secondary statistical analysis, policy document analysis, and a focused institutional case study to capture conceptual, empirical, and governance dimensions of AI use. The findings reveal that generative AI does not inherently erode academic integrity; instead, integrity risks emerge primarily from unclear institutional policies, assessment models reliant on final textual outputs, and limited faculty preparedness. Institutions that implemented explicit AI guidelines, faculty training, and process-oriented assessment redesign demonstrated lower perceived misconduct and higher confidence in integrity enforcement. The study concludes that generative AI should not be addressed through prohibition-driven approaches but through adaptive governance, pedagogical innovation, and ethical literacy development. Academic integrity in the AI era depends less on technological restriction and more on institutional capacity to align policy, pedagogy, and assessment with evolving human–AI academic practices. These findings offer guidance for universities navigating responsible AI integration globally.