Generative AI as an Informing Resource for Doctoral Research in Botswana: Opportunities, Challenges, and Governance Needs

Aim/Purpose: Doctoral students increasingly use Generative Artificial Intelligence (GenAI) as an informing resource, but its benefits, challenges, and governance requirements remain underexplored in developing-country contexts such as Botswana. This study examines GenAI’s contribution to doctoral research and the institutional measures needed for its ethical and effective integration. Background: GenAI has become a prominent feature of higher education and doctoral research, supplementing traditional academic and supervisory support within an emerging informing system in which students receive, evaluate, verify, and act upon information from digital tools, supervisors, and institutional guidance. The effectiveness of this system depends on information quality, critical judgment, supervisory practices, equitable access, and institutional governance, particularly in developing-country contexts such as Botswana, where digital and institutional capacity may be uneven. Methodology: This study adopted an exploratory qualitative design and used semi-structured interviews to investigate doctoral students’ experiences of GenAI use in research. Guided by the Informing Science perspective, the study examined how doctoral students receive, evaluate, verify, and act upon information provided through GenAI tools and related institutional channels. The sample comprised 15 doctoral students from science and engineering disciplines at one university in Botswana, and the data were analyzed using qualitative thematic analysis. Contribution: This paper contributes to the body of knowledge by providing empirical evidence on how doctoral students in Botswana use GenAI as an informing resource in their research, the challenges they encounter, and the forms of policy and institutional support they consider necessary for responsible use. It extends a literature base dominated by Global North contexts by offering a developing-country perspective and by applying the Informing Science perspective to doctoral GenAI use. The paper shows that GenAI in doctoral education should be understood not only as a productivity tool, but also as part of an emerging doctoral informing system shaped by information quality, verification, scholarly development, equity, and governance. Findings: The study found that doctoral students in Botswana used GenAI as an informing resource 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 challenges to effective GenAI-based informing, especially reliability and accuracy of outputs, overreliance, verification and traceability, policy uncertainty, financial and access barriers, contextual mismatch and bias, and privacy and security concerns. They emphasized the need for responsibility and integrity, clear boundaries of acceptable use, integration into existing governance, declaration and transparency, policy enforcement, structured educational integration, and training for students and supervisors. Recommendations for Practitioners: Universities and doctoral supervisors should provide clear, context-sensitive guidance on acceptable GenAI use as part of a responsible doctoral informing system. This guidance should address disclosure, verification, privacy, academic integrity, and human accountability for research outputs. Institutions should also strengthen effective GenAI-based informing through workshops, practical training, supervisor-student dialogue, and equitable access to relevant tools and digital infrastructure. Recommendation for Researchers: Researchers should extend this work by including supervisors, administrators, and policymakers, and by examining how GenAI shapes doctoral supervision, writing practices, research quality, and doctoral informing processes. Future studies should also investigate how doctoral students evaluate, verify, and integrate GenAI-generated information across different disciplines, institutions, and national contexts. Impact on Society: The findings suggest that GenAI may reshape doctoral education and research more broadly by changing how students access academic support, develop research skills, and engage with knowledge production. They also highlight that without clear guidance, critical literacy, and equitable access, GenAI could reinforce existing inequalities and create new risks for academic integrity, making responsible governance essential for socially beneficial and inclusive higher education. Future Research: Future research should compare doctoral GenAI use across disciplines, institutions, and countries, especially in underrepresented developing-country contexts. Longitudinal studies are also needed to examine how doctoral students’ GenAI-related competencies develop over time, including critical prompting, verification, disclosure, privacy awareness, contextual judgment, and responsible participation in a doctoral informing system.