<b>Generative AI as an Informing Resource in Doctoral Research in Botswana: Opportunities, Challenges, and Governance Needs</b>

Doctoral students are increasingly using Generative Artificial Intelligence (GenAI) to support research and writing, yet its role in doctoral research remains insufficiently understood, particularly in developing-country contexts such as Botswana. Guided by the Informing Science perspective, this study examines GenAI as an informing resource within an emerging doctoral informing system, focusing on how doctoral students receive, evaluate, verify, and act upon information provided through GenAI tools and related institutional channels. The study adopted an exploratory qualitative design based on semi-structured interviews with 15 doctoral students from science and engineering disciplines at one university in Botswana. Data were analysed using qualitative thematic analysis. The findings show that doctoral students used GenAI mainly for idea development, literature review support, proofreading and language refinement, methodological clarification, and, to a lesser extent, data analysis and programming support. ChatGPT was the most frequently reported tool. Participants also identified major barriers to effective GenAI-based informing, including reliability and accuracy of outputs, overreliance, verification and traceability, policy uncertainty, financial and access barriers, contextual mismatch and bias, and privacy and security concerns. The study contributes empirical evidence from an underrepresented developing-country context and shows that GenAI in doctoral education should be understood not only as a productivity tool, but also as part of a doctoral informing system shaped by information quality, verification, scholarly development, equity, and governance. The findings suggest that universities and supervisors should provide clear guidance on acceptable use, disclosure, verification, privacy, academic integrity, and human accountability, while also supporting responsible use through training, supervisor-student dialogue, and equitable access to relevant tools and digital infrastructure. Future research should compare doctoral GenAI use across disciplines, institutions, and countries and examine how doctoral students’ GenAI-related competencies develop over time.