Unveiling the GAIS Constructs: A Qualitative Investigation into the Real-World Manifestations of Effective Generative AI Integration in Scholarly Works

Background and Aim: The integration of Generative AI (GenAI) in scholarly works presents transformative opportunities alongside ethical, cognitive, and institutional challenges. This qualitative study investigates the real-world manifestations of effective Generative AI adoption through the Generative AI Integration in Scholarly Works (GAIS) framework, aiming to explore how scholars operationalize AI tools while addressing critical concerns such as ethical integrity, equity, and governance. Materials and Methods: Adopting an interpretivist paradigm, the study conducted focus group discussions involving 15 scholars drawn from two graduate institutions. Data were analyzed using thematic analysis to identify key dimensions of Generative AI integration in scholarly works. Results: Six central themes emerged: (1) Productivity and Creativity (efficiency vs. over-reliance risks); (2) Ethical Integrity (transparency, plagiarism, authorship); (3) Equity (access disparities, algorithmic bias); (4) Personalized Learning (adaptive knowledge scaffolding); (5) Cognitive Trade-offs (efficiency vs. critical thinking erosion); and (6) Institutional Governance (policy gaps in privacy and oversight). Findings highlight Generative AI’s potential to augment scholarship but reveal tensions in ethical accountability, access inequality, and academic self-efficacy. Conclusion: Significant access disparities persist, intensifying academic inequities. While Generative AI aids personalized learning and knowledge construction, its over-reliance risks diminishing critical thinking and self-efficacy. Institutional governance remains inconsistent, highlighting an urgent need for clear policies, structured oversight, and inclusive training. Practical recommendations include (a) institutional policies on Generative AI ethics, (b) cross-disciplinary Generative AI literacy programs, and (c) inclusive governance models. Future research should pursue longitudinal studies on GAIS evolution and broader stakeholder engagement to ensure equitable and sustainable AI adoption in scholarly works.