An Artificial Intelligence-Based Chatbot as a Tool for Promoting Nutrition in a Community Setting

Abstract

Malnutrition remains a major public health problem in Tanzania, worsened by limited access to personalised, culturally relevant nutrition information. Existing gaps in community nutrition education across sub-Saharan Africa, combined with conventional approaches ignoring individual dietary needs, contribute to undernutrition and chronic disease. However, while AI-powered chatbots have shown global effectiveness in scalable health education, evidence from low-resource, Swahili-speaking settings is scarce. This study, aligned with SDGs 2, 3, and 4, developed Lishe Yetu, a culturally localised digital nutrition tool aimed at bridging these gaps. A pre-post quasi-experimental mixed-methods design evaluated Lishe Yetu among 400 Tanzanian residents between January and April 2025. Built on retrieval-augmented generation using GPT-3.5-turbo, the platform drew from over 2,500 expert-reviewed sources including WHO, FAO, TFNC, and validated Swahili materials. Nutritional knowledge was measured using a validated 20-item questionnaire, with engagement tracked via server logs and satisfaction assessed through Likert-scale surveys and thematic analysis of qualitative feedback. Results showed strong engagement, with average sessions lasting 12 minutes and 15.2 messages exchanged; 87% completed at least one full session. Satisfaction averaged 4.3/5.0, and 88% found the tool easy to use. Knowledge scores improved significantly from 52% to 73%, a 21 percentage-point gain with a large effect size (Cohen's d = 1.57, p < 0.001). Additionally, 65% reported intentions to adopt healthier diets. These findings provide strong preliminary evidence that a Swahili-language AI chatbot can improve nutrition literacy in low-resource African settings, though randomised controlled trials are needed to confirm sustained behaviour change.