Human–AI Synergy in Health Communication, Health Literacy and Health Education: Reimagining Public Health Education and Promotion in the Age of Artificial Intelligence

Artificial Intelligence (AI) is rapidly transforming the ways in which health knowledge is generated, communicated, and applied across societies. Recent advances in generative AI, and large language models (LLMs), are reshaping the global health information ecosystem. These technologies are increasingly becoming primary gateways through which individuals seek, interpret, and increasingly generate health information, making AI a central actor in contemporary health communication and education. In particular, conversational AI systems such as medical educational chatbots and autonomous AI agents are emerging as interactive mediators of health knowledge, enabling dialogic, context-aware, and just-in-time learning experiences for patients, students, and healthcare professionals. This transformation calls for a deeper understanding of human–AI synergy, not simply as an interaction between humans and machines, but as a co-evolving relationship in which computational capabilities and human critical judgment operate together within a dynamic feedback loop. As this collaboration becomes embedded in everyday practices, it may also begin to reshape the linguistic framing, reasoning patterns, and critical thinking processes through which individuals interpret and communicate health information.While AI offers unprecedented opportunities for personalized health education, adaptive learning systems, and real-time patient support, it also introduces significant risks. These include the generation of inaccurate information (“hallucinations”), reinforcement of historical biases in ML datasets, and the potential erosion of independent judgment through excessive reliance on automated systems, often described as algorithmic bias or cognitive offloading. These risks are further amplified in conversational and agent-based systems, where users may develop relational trust in AI outputs, increasing susceptibility to misinformation, overreliance, and reduced critical appraisal of health advice. At the same time, AI in health education represents not only a technological advancement but also a form of social innovation. By enabling scalable personalization and data-driven learning environments, AI can tailor health education to diverse linguistic, cultural, and socioeconomic contexts, potentially improving accessibility and inclusion. However, these benefits depend on careful design that avoids digital paternalism and promotes participatory and equitable models of knowledge and decision-making.As AI becomes embedded in everyday learning and communication, an important question is not only what people can do with AI, but what AI may gradually do to people’s ways of thinking, writing, judging, and learning.This Research Topic focuses on the idea of human–AI synergy. We understand this not as the replacement of human expertise by machines, but as a relationship in which AI, particularly interactive agents and educational chatbots, can support human learning, communication, and reflection, while humans remain responsible for critical thinking, ethical judgment, and meaningful decision-making. The central challenge addressed by this Research Topic is therefore the following: How can public health education and promotion systems cultivate shared mental models between humans and AI that strengthen critical thinking, ethical reasoning, and responsible knowledge use in the digital health era?We welcome articles exploring, but not limited to, the following themes:· Curriculum Redesign and Pedagogical Innovation: Moving from static learning to adaptive, AI-integrated curricula in medical schools and public health programs. How do we teach "prompt engineering" as a health communication skill?· Human-in-the-Loop Learning and Metacognitive Safeguards: Developing pedagogical strategies that preserve human oversight, critical reasoning, and responsible decision-making when interacting with AI-generated health information.· Critical AI Health Literacy: Conceptualizing AI literacy as an emerging dimension of health literacy, focusing on the ability to evaluate AI-generated information, recognize algorithmic bias and “hallucinations,” and maintain epistemic agency in AI-mediated information environments.· Measuring AI health literacy: What tools, indicators, or frameworks can be used to assess AI-related health literacy, trust, overreliance, cognitive offloading, critical evaluation, and the impact of AI-supported education including interactions with conversational agents and chatbot-mediated learning environments?· Human–AI Collaboration in Health Communication: Investigating how conversational agents, medical chatbots, generative AI, and AI-assisted decision-support systems can enhance patient education, public health communication, shared decision-making, and longitudinal patient engagement.· Hyper-Personalized Health Education: Utilizing ML to create culturally sensitive, linguistically diverse, and cognitively accessible health content for minority populations.· Socio-Technical Shared Mental Models: Research into the psychological trust-building between patients/providers and AI agents. How do we ensure these models are transparent and explainable?· Professional Development in the AI Era: Training the "Trainers." How do we upskill current healthcare providers and professors to facilitate AI-driven learning environments, including the effective use and supervision of medical chatbots and AI agents in education and patient communication?· Ethics and Equity: Addressing the digital divide. How can AI-driven health literacy empower underserved communities rather than widening the health disparity gap?· Evolutionary Shift in Teacher–Student Dynamics: Analyzing how AI alters the authority of educators and the autonomy of learners in health contexts· Design and Evaluation of Medical Educational Chatbots and AI Agents: Exploring the development, implementation, and assessment of conversational AI systems for medical and public health education. Topics may include pedagogical design, clinical accuracy, user trust, safety, personalization, longitudinal learning, and integration into healthcare and training systems.Particular attention will be given to emerging paradigms in which AI agents act not only as tools, but as collaborative partners in learning ecosystems, raising new questions about pedagogy, accountability, and human agency in public health education. By bringing together interdisciplinary contributions from public health, education, communication, and artificial intelligence research, this articles collection aims to advance conceptual frameworks, empirical evidence, and policy insights for responsible AI integration in health education and public health systems. By shifting the focus from "technological adoption" to "human empowerment," we aim to build a resilient public health framework. We are especially interested in work that helps individuals become not passive users of AI-generated information, but informed, reflective, and empowered participants in their own learning and decision-making. Topic editors: Effie Simou, Helen Hong Chen, James C. L. Chow Source: https://www.frontiersin.org/research-topics/81464/human-ai-synergy-in-health-communication-health-literacy-and-health-education-reimagining-public-health-education-and-promotion-in-the-age-of-artificial-intelligence