Generative AI in Nutrition Education: Current Applications, Opportunities, Challenges, and Future Directions: A Narrative Review

Abstract

Artificial intelligence is drastically entering every aspect of our lives, including education, training, and communication. There are many models in Generative artificial intelligence (GenAI) such as “large language models (LLMs) including ChatGPT, Gemini, Claude, and Copilot etc. This narrative review focuses on the current peer-reviewed articles (predominantly 2023–2026) on the AI applications, opportunities, challenges and future directions in the field of nutrition education. Most of these applications span three domains: professional training, direct-to-consumer dietary counselling and meal planning (chatbot-delivered advice, personalised diet plans), and public health nutrition communication. Evidence suggests that GenAI can improve nutrition learning's accessibility, scalability, personalisation, and engagement while providing dietetics educators with an affordable substitute for resource-intensive training techniques like standardised patients. However, studies consistently show limitations in accuracy, especially when it comes to calculations of calories and macronutrients, complex or comorbid clinical scenarios, and culturally specific dietary contexts. These limitations are accompanied by concerns about misleading information, algorithmic bias, dependency, data privacy, and the deterioration of critical thinking. Future directions include multimodal food-image analysis, retrieval-augmented generation based on validated nutrition databases, hybrid human-AI counselling models, and formal AI-literacy curriculum for the general public and dietetics students. According to the review, GenAI works best as a scalable supplement to trained nutritionists rather than as a replacement for them.