Search

The rapid evolution of generative artificial intelligence, most notably large language models like ChatGPT and GPT-4, has unlocked revolutionary opportunities for postgraduate scholarship. This systematic review evaluates how generative artificial intelligence utilities alter the quality, execution speed, ethical standards, and innovative potential of postgraduate inquiries. A thorough literature search was conducted across four major repositories: Google Scholar, Web of Science, IEEE Xplore, and Scopus. Adhering strictly to PRISMA protocols, 20 peer-reviewed articles published between 2019 and 2025 were chosen for detailed investigation based on their direct alignment with postgraduate research settings, explicit use of generative AI applications, and documented outcomes. The synthesis revealed four core dimensions: (i) research productivity and efficiency, where generative AI refines academic composition, data processing, and literature mapping; (ii) cognitive and creative assistance, where AI aids in drafting hypotheses, brainstorming novel concepts, and polishing text; (iii) academic honesty and ethical dilemmas, which highlight vulnerabilities surrounding plagiarism, data fabrication, and over-dependence on machine-generated text; and (iv) capacity shortages and skill evolution, indicating an accelerating necessity for postgraduate scholars to undergo training in AI literacy and ethics. Although these generative AI systems offer significant advantages by broadening access to analytical tools and boosting operational output for postgraduate scholars, this investigation reveals a stark lack of rigorous regulatory oversight and institutional frameworks across most universities. Furthermore, unequal regional access to state-of-the-art AI systems threatens global research equity. The review culminates in a proposal for a customized framework designed for the conscientious integration of GenAI into postgraduate studies, prioritizing institutional governance, human-machine partnerships, and ethical deployment. These insights enrich the ongoing debate regarding the trajectory of AI, offering scholars, investigators, and administrators data-driven strategies to harness AI capabilities while defending academic standards.