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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.

Artificial intelligence increasingly informs consequential decisions, yet high predictive performance alone does not establish that an intelligent system is worthy of trust. This systematic review examines trustworthy AI as a multidimensional, socio-technical, and lifecycle-dependent research domain. It synthesises selected peer-reviewed journal evidence addressing transparency, interpretability, explainability, robustness, safety, privacy, security, fairness, accountability, governance, and meaningful human oversight. The review applies predefined questions, eligibility principles, trustworthiness-dimension mapping, claim-level evidence extraction, and cross-dimensional synthesis without undertaking meta-analysis or inventing quantitative screening results. The evidence indicates that transparency and explanation must be assessed in relation to fidelity, stability, completeness, usefulness, stakeholder needs, contestability, recourse, and the practical authority of human overseers. Robustness and safety require evaluation under adversarial conditions, uncertainty, context shift, system failure, and residual risk, while privacy-preserving approaches must still be examined for leakage, cybersecurity vulnerabilities, and governance limitations. Fairness depends on context-sensitive definitions, bias identification, distributional consequences, documentation, responsibility allocation, auditability, and institutional mechanisms for correction. Cross-dimensional analysis reveals persistent tensions between transparency and privacy, explainability and security, automation and human control, and performance and fairness. Important gaps remain in multidimensional evaluation, realistic human-oversight studies, explanation-usefulness validation, lifecycle monitoring, accountability operationalisation, stakeholder participation, and foundation-model assurance. The review contributes an integrated evidence synthesis showing that AI worthiness of trust should be supported through coordinated technical validation, human-centred assessment, institutional governance, and continuing monitoring rather than inferred from any isolated property.

Explainability has become central to discussions of trustworthy and responsible intelligent systems, yet it is frequently reduced to the technical production of information about model structure, features, rules, or predictions. This critical review examines why opening a computational black box is not equivalent to producing an explanation that people can understand or use responsibly. The review synthesises conceptual, technical, human-centred, decisional, and accountability-oriented scholarship to evaluate the shift from model interpretability toward meaningful explanation. It distinguishes technical properties such as transparency, fidelity, plausibility, completeness, stability, and local or global scope from human outcomes such as comprehension, cognitive accessibility, mental-model development, decision usefulness, calibrated trust, and appropriate reliance. It further examines how explanations may produce overreliance, underreliance, automation bias, false reassurance, cognitive overload, or explanation mismatch when their content and presentation are not aligned with users, tasks, uncertainty, and decision consequences. From an accountability perspective, explanations may contribute to auditability, traceability, contestability, and recourse, but they cannot independently assign responsibility, provide institutional authority, or guarantee effective remedies. The main contribution is a human-centred critical synthesis that treats explainability as a socio-technical achievement rather than a model output. Meaningful explanation requires technical credibility, cognitive accessibility, contextual relevance, decision-sensitive evaluation, uncertainty communication, opportunities for challenge, and governance mechanisms capable of acting on explanatory evidence. The review therefore rejects universal explanation-sufficiency claims and identifies conditions under which explanations may assist understanding and responsible decision use without being treated as proof of trustworthiness, accountability, fairness, safety, or deployment readiness.