Integration of AI into teaching methodologies in health training institutions in Tanzania

What the paper says

Purpose This study investigates the integration of artificial intelligence (AI) into teaching methodologies within health training institutions in Tanzania. It aims to explore expert perspectives on AI’s potential benefits, the challenges to its implementation and strategies for successful adoption. The findings contribute to understanding how AI can transform health education in low-resource settings, helping to prepare future healthcare professionals for an evolving healthcare industry. Design/methodology/approach The study employed a qualitative, interpretivist research philosophy, utilising a case study design. A sample of 15 experts was selected, including policymakers, health educators and AI technical specialists. Semi-structured interviews provided the primary data, exploring participants’ perceptions, challenges and recommendations related to AI integration. Thematic analysis was conducted using constructivism, TPACK and activity theory as guiding frameworks. The study incorporated expert validation and triangulation by consulting subject-matter experts and supporting findings with secondary data, ensuring the reliability and depth of the results. Findings The study reveals that AI adoption in Tanzanian health training institutions is in its infancy, with most applications driven by individual initiatives rather than institutional strategies. Key benefits include personalised learning, enhanced remote education opportunities and streamlined administrative processes. However, significant barriers exist, such as insufficient infrastructure, limited technical skills among educators, financial constraints and resistance to technological change. Proposed strategies to address these challenges include developing a clear policy framework, phased implementation, professional development for educators and fostering collaborations with AI providers. Originality/value This research provides a novel contribution by focusing on AI integration in health training institutions within a low-resource setting, which remains underexplored in the existing literature. The study offers actionable strategies for overcoming barriers and advancing AI adoption in education by applying established theoretical frameworks to analyse the contextual challenges and opportunities. The findings also serve as a foundation for future research and policy development, supporting the broader goal of improving healthcare education in Tanzania and similar contexts.