By Kudzayi Savious Tarisayi

Over the years I have watched postgraduate students grappling with the demands of postgraduate study. Most of the struggles were in my view, at their core, largely logistical. Students spent long hours hunting for the right sources or found themselves paralysed before a mountain of literature with no clear way through it. Students with genuine intellectual promise were spending disproportionate amounts of their time on tasks that were more about information management than about critical thinking. They were exhausted before the real work of scholarship had even begun. That pattern, observed across many supervisions and workshops, is what eventually became Artificial Intelligence in Postgraduate Research Methodology, published by Edward Elgar Publishing.

The world of academic research has changed in ways that would have seemed improbable a decade ago. The release of ChatGPT 3.5 on the 30^th of November 2022 brought Artificial Intelligence (AI) to the fore of academic discourse. Artificial intelligence is no longer a concept confined to computer science departments or technology companies. For postgraduate students and their supervisors across Africa and the globe, the question is no longer whether to engage with AI, but how to do so thoughtfully, responsibly, and effectively.

This book is my attempt to answer that question. Its central argument is that AI, when properly understood and responsibly deployed, creates a powerful new partnership, a synergy between human intellectual capacity and machine efficiency that can elevate the quality of research rather than diminish it. The book is premised on the concept of Intelligence Augmentation (IA), which holds that human intelligence and artificial intelligence are most powerful not in competition, but in combination. The researcher remains the thinker, the ethical agent, the one who asks the questions that matter. AI becomes a capable companion on what is often a long and solitary intellectual journey.

The book follows the full cycle of the research process, beginning with the theoretical foundations of intelligence augmentation and moving through each stage of a typical postgraduate project. Too many discussions about AI in academia treat the technology as a monolithic and intimidating force. My aim was to show scholars exactly where AI tools can be integrated, from the moment a researcher begins to formulate a topic, through literature discovery and knowledge organisation, into data analysis, academic writing, visualisation, and finally the submission of a polished manuscript. The book takes cognisance of language struggles that postgraduate students encounter especially in Africa, where English is not their first language.

The book has a deliberately hands-on character. Rather than speaking abstractly about AI capabilities, I walk readers through specific tools, Elicit for evidence-based literature search and synthesis, Julius AI for data analysis and statistical reasoning, and SciSpace for understanding and contextualising complex research papers. There is also dedicated attention to AI-powered academic databases, knowledge organisation platforms, and language editing tools that help researchers refine their manuscripts before submission. The aim throughout was to give postgraduate students and supervisors a toolkit they can open and use the very next day.

‘This book is a highly relevant and accessible resource for scholars and students interested in artificial intelligence research methodology. I thoroughly recommend.’

– Jeriphanos Makaye, Professor and Pro Vice Chancellor of the Reformed Church University, Zimbabwe

Beyond the expected, the book ventures into territory that sets it apart. The chapter addressing how AI tools can support researchers with disabilities reflects a conviction that conversations about AI in academia must be inclusive. AI has the potential to level playing fields in ways that traditional research environments have failed to do. Equally, the chapter on research gap analysis speaks directly to one of the most anxiety-inducing challenges of postgraduate work, identifying where the original contribution lies. AI can map existing scholarship with extraordinary speed, helping researchers find the white spaces in a field far more efficiently than manual methods allow.

No book on AI in research would be complete without a serious engagement with ethics, and Artificial Intelligence in Postgraduate Research Methodology addresses those questions directly. The concerns are real: AI tools can hallucinate citations, introduce subtle inaccuracies, raise data security risks, and, if misused, become instruments of academic dishonesty. I do not shy away from any of this. Responsible use requires that researchers understand not only what AI can do, but what it cannot, and where its outputs must be interrogated rather than accepted at face value. Academic integrity is not a constraint to be worked around; it is the foundation on which all scholarly work rests.

Writing from the African perspective, I was acutely conscious of the need for a framework that speaks to our context. Postgraduate students in Africa often navigate resource constraints, limited access to expensive journal databases, and supervision ratios that make individualised guidance difficult. Many of the AI tools discussed in this book are accessible at no cost or on a free basic tier, and that fact matters. When a student at a resource-constrained university can use Elicit or STORM to conduct a systematic literature scan that previously required costly institutional database subscriptions, that is not a trivial gain. It is an equity issue.

The book is written for postgraduate students and their supervisors, but experienced researchers venturing into new methodological terrain will find it equally valuable. It is, at its heart, a practical framework, structured, accessible, and grounded in the realities of academic research as it is actually practised. My hope is that it finds its way onto reading lists and into orientation programmes for new doctoral students, and into the hands of supervisors navigating a research environment that is changing faster than our institutions have adapted to. The final chapter, on the future of AI-augmented academic inquiry, acknowledges that we are in the early chapters of a much longer story. The tools will evolve. The ethical debates will deepen. But the researchers who will thrive are not those who resist AI out of fear, nor those who embrace it uncritically out of convenience. They are those who understand it well enough to use it wisely. Artificial Intelligence in Postgraduate Research Methodology is my contribution to building that understanding, and my answer to every postgraduate student I have ever watched struggle alone with a problem that did not have to be so hard.

Kudzayi Savious Tarisayi is an Associate Professor in the School of Mathematics, Sciences and Technology Education at North-West University, South Africa

Artificial Intelligence in Postgraduate Research Methodology: Transforming Researchers for the AI Age is available to preorder in hardback and eBook here.