A Systematic Review of the Impact of Generative AI on Postgraduate Research: Opportunities, Challenges, and Ethical Implications

The rapid improvement of generative artificial intelligence particularly large language models like ChatGPT, and GPT-4, has presented transformative possibilities in postgraduate research. This systematic review investigates the impact of generative AI tools on the quality, efficiency, ethics, and innovation of postgraduate research. A comprehensive literature search across four databases: Google Scholar, Web of Science, IEEE explore, and Scopus was carried out. Following the PRISMA guidelines, a total of 17 peer reviewed published articles between 2019 and 2025 were selected for a detailed review analysis and these were selected based on relevance to the postgraduate research context, explicit declaration of generative AI application, and reported outcomes. The review identified four thematic areas: (i) research productivity and efficiency, where generative AI has improved academic writing, data analysis, and literature reviews; (ii) cognitive and creative support, where AI helps formulate hypotheses, generate ideas, and refine language; (iii) academic integrity and ethical concerns, highlighting the dangers of plagiarism, data fabrication, and an over-reliance on AI outputs; and (iv) capacity gaps and skills transformation, pointing out the growing demand for postgraduate researchers to receive ethical and AI literacy training. Despite the potential benefits of generative AI tools in democratizing access to research tools and improving productivity among postgraduate researchers, the review discovered that most academic institutions lack robust regulatory frameworks and institutional guidelines. Additional concerns arise from disparities in regional access to cutting-age AI tools, hence compromising the global research equity. The review concludes with a recommendation for a tailored framework for the responsible integration of GenAI into postgraduate research with a focus on institutional oversight, human-AI collaboration, and ethical application. The findings contribute to the current discussion over the future of AI and provide scholars, researchers and policymakers with evidence-based guidance on how to maximize AI’s potential while safeguarding academic integrity.