Tabriz University of Medical Sciences Research and Development in Medical Education 2322-2719 15 1 2026 01 01 The 5As Framework: Guiding the Integration of Artificial Intelligence in Health Professions Education 33415 33415 10.34172/rdme.33415 EN Siavash Moradi https://orcid.org/0000-0002-0222-9920 Journal Article 10.34172/rdme.33415 2025 12 18 2026 01 01 Introduction: The rapid evolution of artificial intelligence (AI) technologies, including large language models (LLMs) and virtual simulations, offers transformative opportunities for health professions education (HPE) by enabling personalized learning and enhanced clinical reasoning. However, challenges like resource disparities, ethical concerns, and inequities in low- and middle-income countries (LMICs) such as Iran necessitate a structured framework. Methods: This perspective proposes the 5As Framework—Availability, Accessibility, Acceptability, Adaptability, and Affordability—as a comprehensive guide for equitable AI integration in HPE. Building on models like the Technology Acceptance Model (TAM), Diffusion of Innovations, and Unified Theory of Acceptance and Use of Technology (UTAUT), it addresses gaps in procurement, usability, ethics, flexibility, and costs, tailoring solutions to LMIC contexts. Results: Availability ensures procurable tools via open-source platforms like Hugging Face and partnerships, countering proprietary barriers. Accessibility emphasizes intuitive interfaces, natural language processing (NLP) aligned with clinical language, and multimodal interactions compatible with systems like Moodle, reducing learning curves. Acceptability fosters trust through bias audits, co-design, and framing AI as augmentative, as in objective structured clinical examination (OSCE) feedback. Adaptability leverages machine learning for customizable simulations tied to local priorities like Iran’s health needs, supporting curricula from preclinical to continuous professional development (CPD). Affordability promotes open-source alternatives, World Health Organization (WHO) grants, and workload automation for long-term savings. Conclusion: Implications span curriculum design with equitable simulations, faculty development via bias workshops, and assessments like script concordance tests with human oversight. While conceptual, the 5As requires empirical validation through multicentre trials to confirm impacts on outcomes and equity. Ultimately, this equity-oriented roadmap empowers educators and policymakers to harness AI for human-centered HPE, bridging global divides and preparing professionals for AI-augmented practice.