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Generative artificial intelligence (GenAI) is emerging in food and nutrition education, offering adaptive learning tools and counseling support while raising concerns about accuracy, integrity, and equity. This review critically examines the role of GenAI through four dimensions—applications, benefits, challenges, and contributions to personalized learning—to answer the question of what is the role of GenAI in food and nutrition education. Peer-reviewed English- and Spanish-language studies (January 2021–August 2025) addressing generative or conversational AI (e.g., large language models, chatbots) in educational or applied nutrition contexts were included. Exclusions comprised non-nutrition topics, purely technical reports, opinion papers, preprints, duplicates, and non-generative AI. Searches in PubMed, Scopus, and Web of Science yielded nine studies after dual screening. Narrative synthesis identified applications of GenAI in university teaching, family nutrition programs, and clinical dietetics to generate readable materials, tailor quizzes and feedback, and support dietary learning. Reported benefits included improved parental nutrition knowledge, enhanced student engagement under supervision, and associations between digital nutrition literacy and sustainable eating behaviors. Challenges encompassed inconsistent adherence to dietary guidelines in complex cases, sensitivity to language and prompt framing, risks to academic integrity and privacy, and digital inequities requiring AI literacy and oversight. Overall, GenAI functions most effectively as a supervised adjunct that enhances access and personalization while safeguarding quality. Ensuring alignment with professional standards, expert review, transparency, and contextual adaptation is essential to responsibly advance its educational value.
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Zambrano et al. (2025) studied this question.
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