Teacher Professional Development on WhatsApp and LLMs: Early Lessons from Cameroon
This addresses the problem of accessible AI-driven professional development for teachers in low-resource settings, though it is incremental in adapting existing technologies to a specific context.
The study tackled the exclusion of teachers in low-resource contexts from AI in education by deploying a WhatsApp-based chatbot with LLM-supported content for teacher professional development in Cameroon, finding it rated higher in perceived usability and overall experience compared to an online form baseline, with learnability remaining comparable.
AI in education is commonly delivered through web-based systems such as online forms and institutional platforms. However, these approaches can exclude teachers in low-resource contexts, where everyday mobile platforms like WhatsApp serve as primary digital infrastructure. To address this gap, we present a field pilot in Cameroon that deploys a WhatsApp-based chatbot with LLM-supported content for teacher professional development (TPD), compared with an online form baseline. The system was evaluated through a mixed-methods study with 47 primary school teachers, integrating quantitative measures with qualitative insights from interviews and participant feedback. Results show that the chatbot was rated higher in perceived usability and overall experience, while learnability remained comparable. These improvements were driven by platform familiarity, low interaction overhead, and the modular structure of LLM-supported content, but were constrained by connectivity limitations, prepaid data costs, and multilingual needs (English/French). Building on these findings, we outline design directions for multilingual, culturally grounded interaction and for supporting prompting and reflection in AI use. More broadly, this work points to Thoughtful AI that supports reflection, relevance, and sustained professional growth.