<b>Generative AI in Doctoral Research Practices in Botswana: Opportunities, Challenges, and Policy Implications</b>

Doctoral students are increasingly using Generative Artificial Intelligence (GenAI) to support research and writing, yet its role in doctoral research remains insufficiently understood, especially in developing-country contexts such as Botswana. While GenAI may enhance productivity, writing support, literature review, methodological clarification, and access to academic assistance, it also raises concerns about reliability, overreliance, academic integrity, equity, privacy, and the absence of clear institutional guidance. This study adopted an exploratory qualitative design and used semi-structured interviews to investigate doctoral students’ experiences of GenAI use in research. The sample comprised 15 doctoral students from science and engineering disciplines at one university in Botswana, and the data were analysed using qualitative thematic analysis. The findings show that doctoral students mainly used GenAI to support literature review, proofreading and language refinement, methodological clarification, and idea development. At the same time, participants identified major challenges relating to reliability, verification, overreliance, privacy, unequal access, contextual mismatch, and policy uncertainty. The paper contributes empirical evidence from a developing-country context and shows that GenAI in doctoral education should be understood not only as a productivity tool, but also as an issue of scholarly development, equity, and governance. The findings suggest that universities and doctoral supervisors should provide clear, context-sensitive guidance on acceptable GenAI use, including expectations around disclosure, verification, privacy, academic integrity, and human responsibility for research outputs. Institutions should also support responsible use through workshops, practical training, monitored guidance, and equitable access to relevant tools and digital infrastructure. Future research should include supervisors, administrators, and policy makers, compare doctoral GenAI use across disciplines, institutions, and countries, and examine how GenAI shapes doctoral supervision, writing practices, research quality, and doctoral competencies over time.