This special issue of The Journal of Health Education & Behavior aims to characterize the rapidly evolving artificial intelligence landscape as it relates to health education and health behavior research and the translation into public health and clinical practice. For the purposes of this issue, artificial intelligence refers broadly to computational methods that draw on large, complex datasets to enable machine to perform or augment tasks of predictive analytics and decision-making. Examples include machine learning, deep learning, and natural language processing.

We are interested in understanding applications and the impact of AI-based tools in the context of behavioral interventions, health literacy and education, patient and/or community engagement, provider training, in addition to clinical decision support. Critical assessments of machine learning models’ bias and fairness are appreciated as are studies of AI tools’ effectiveness in improving health education, shaping health behavior, and reducing disparities across diverse communities. We also welcome submissions that address safety, governance, and regulatory guardrails around AI-based health interventions or tools.

Some compelling prompts include: are clinical decision support tools, whether predictive or generative, scalable or clinically impactful in health systems, and in what ways? Similarly, how do data-centric tools that improve clinical guidelines, enhance data quality, or that reduce the administrative burden affect patients, providers, care teams, health systems, and/or communities? How can AI-powered tools strengthen health education curricula, community-based programs, or digital behavioral interventions? What are the broader implications for health equity, cost-effectiveness, patient activation, caregiver support, and sustainability of health behavior change? What policies are currently in place or are needed to facilitate impact and sustainability, improve care and safety, and/or reduce clinical burnout? Finally, what are the unintended consequences ethically, socially, or financially for implementing AI tools in health education and behavioral practice contexts?

We welcome original research, brief reports, systematic reviews, perspective pieces, and policy papers that address a wide range of themes including but not limited to:

• Training, testing, and/or validating predictive AI models for behavior change or for clinical decision support.
• Use cases for the application of AI tools for behavioral health and health promotion interventions.
• Studies regarding the development of tools to reduce administrative burden in clinical settings.
• Implementation science – from prototyping to pragmatic trials – regarding AI clinical or patient-facing tools, including studies reporting null or with less promising results.
• Methodological studies that engage communities and end-users in various stages of AI model development and implementation.
• Innovative and/or intersectional methods for identifying and/or addressing bias, fairness, and equity of AI models and digital behavioral or decision support interventions.
• Ethical, equity, structural, policy, accountability, and transparency considerations in AI-based clinical or data tools, whether patient- or provider-facing.

Pieces may span any number of health domains: chronic disease, infectious disease, aging and the life course, prevention, acute care, primary care, disability, mental health, behavioral health, sexual health, community health, social determinants of health, environmental health, occupational health, health systems, and genomics/precision health. Submissions that focus solely on technical or computational methods without direct relevance to health education, behavior, or public health fall outside the scope of this issue.

Submission process:

Please email an unstructured abstract – of no more than 300 words – due by October 31, 2025, 12am ET, to he*@*****************ns.net. Please also declare the article type (e.g., original research, brief report, etc.). As with all submissions of original research articles to HEB, please specify the theoretical framework that guides the intervention, behavior change process, or its implementation. Along with an unstructured abstract, please include a short bio of the first author highlighting your relevant expertise and training (no more than 250 words).

Decisions will be made by November 15, 2025, and full manuscripts will be due by Feb 27, 2026, 12am ET. An invitation to submit a full manuscript does not guarantee acceptance for publication. All submissions will undergo the journal’s peer-review process.