AI Health Literacy: a reflective framework for LLM-based generative AI in public health education and promotion

Generative artificial intelligence (AI) is changing how citizens search for, understand, and use health information. Large language models (LLMs) can simplify, translate, summarize, and tailor health-related content to individual questions. At the same time, AI-generated responses may appear fluent, plausible, and empathic without necessarily being complete, up to date, evidence-based, or suitable for a person's individual situation. This creates a public health challenge for Public Health Education and Promotion, as AI-generated answers may increasingly shape how citizens access, interpret, and trust health information. Building on health literacy, eHealth literacy, digital health literacy, and debates on AI-mediated health communication, this article conceptualizes AI Health Literacy as a task- and context-sensitive extension of existing literacy concepts. Its specific contribution is to understand AI Health Literacy as an interdisciplinary judgement competence that requires citizens to assess not only the content of AI-generated health information, but also the task, context, modality, verifiability, and psychological conditions under which such information is received and used. It proposes a reflective framework with three analytical dimensions: task appropriateness, context of use, and critical verifiability. The central argument is that generative AI can support understanding, orientation, translation, and preparation, but should not replace professional advice in diagnostic, therapeutic, triage-related, medication-related, or crisis situations. The framework aims to support public-health-oriented education, communication, and institutional guidance for the responsible use of AI-generated health information and should be understood as a conceptual orientation tool, not as a validated assessment instrument.