Adoption of Artificial Intelligence in Facilitating the Professional Development of Lecturers

Artificial intelligence (AI), particularly generative AI, is rapidly transforming teaching methodology in universities as well as their assessments, research, and academic work, yet there is only limited evidence about how lecturers in sub-Saharan African universities adopt these tools and what professional development they require. This qualitative study explored AI adoption, perceived benefits and risks, and needs for professional development among lecturers and administrators in the Faculty of Education at one private university in Uganda. A convenience sample of 28 participants took part in individual semi-structured interviews during 14-day of campus visit in January, February, and March 2026. The interview guide was pilot tested; while interviews lasted for 38–67 minutes each, meaning saturation was judged to have been reached by the 25th interview, with three additional interviews required to assess the adequacy of the developing coding framework. Reflexive thematic analysis generated 6 themes: pragmatic but uneven adoption; AI as partner for efficiency and creativity; uncertainty about accuracy, authorship, and academic integrity; infrastructural and institutional constraints; demand for practice-based and discipline-relevant professional development; and the need for governance, communities of practice, and protected learning time. Lecturers more often framed AI through teaching, assessment, and workload concerns, whereas administrators more often foregrounded policy consistency, governance, and institutional support. Participants reported using AI frequently for lesson planning, summarizing, language editing, idea generation, assessment preparation, research support, and routine administration. However, adoption was constrained by unreliable connectivity, subscription costs, uneven AI literacy, limited policy guidance, privacy concerns, and fear of students’ overreliance. The study proposed a contextualized professional-development model combining foundational AI literacy, pedagogical design, research integrity, data protection, assessment redesign, peer mentoring, and continuing technical support. The findings provide a situated account of responsible AI adoption in an East African Faculty of Education and should not be interpreted as representative of Ugandan higher education as a whole.