Blending Generative
AI
and Traditional Pedagogy in Geography Education: Evidence From a Pilot Study in Tanzania
Siqin Wang, Xiao Huang, Xuebin Wei, Xianyi Li, Idowu Ajibade ABSTRACT
This paper presents a pilot project exploring the integration of generative artificial intelligence (GenAI) into geography education in Zanzibar, Tanzania. Responding to systemic challenges such as overcrowded classrooms, teacher shortages, and limited resources, the study developed and implemented a localized, GenAI‐assisted teaching platform. Using a mixed‐methods approach, this study involved 133 teachers who experienced both traditional teaching and GenAI‐assisted teaching via a platform featuring interactive maps, a multilingual chatbot, and customized video content. Data was collected through pre‐ and post‐teaching quizzes, surveys, and semi‐structured interviews to compare learning gains, engagement, and perceived effectiveness. Key findings indicate that while traditional teaching yielded marginally higher knowledge gains overall, GenAI‐assisted teaching more obviously enhanced visual learning, exploratory engagement, and self‐paced study. Demographic factors such as gender, age, and prior digital experience influenced the effectiveness of each teaching mode. Crucially, participants endorsed a blended approach, combining the strengths of human teachers with the technological advantages of GenAI. The study underscores the potential of GenAI‐assisted teaching to supplement traditional education in a resource‐limited African context. It offers a replicable model for other African educational systems, emphasizing the importance of localized content, teacher training, and blended pedagogy to bridge educational gaps and foster sustainable digital transformation in line with the UN Sustainable Development Goals.