A policy framework for leveraging artificial intelligence in theoretical medical education in Iran
Bibliographic Data
| ID | 22436673 |
|---|---|
| Authors | Mohammad Rasool Khazaei (0000-0001-9818-0625, Kermanshah University of Medical Sciences), Auob Rustamzadeh (0000-0002-8246-5631, Qazvin University of Medical Sciences), Elham Niroumand (0000-0001-6406-6779, Kermanshah University of Medical Sciences), Elham Niromand, Mohammad Mehdi Khazae (Kermanshah University of Medical Sciences), Rastegar Rahmani Tanha (0000-0001-2012-5829, Kermanshah University of Medical Sciences), Ghobad Ramezani (0000-0002-8192-5587, Kermanshah University of Medical Sciences, corresponding author) |
| Year | 2026 |
| Volume | 5 |
| Issue | 1 |
| Publication date | 2026-05-04 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Discover Education (JOURNAL) |
| Journal identifiers | ISSN: 2731-5525 • E-ISSN: 2731-5525 |
| Publisher | Springer Science and Business Media LLC (PUBLISHER) |
| DOI | 10.1007/s44217-026-01360-w |
| OpenAlex | W7160032681 |
| Language | EN |
| References cited | 12 |
With the emergence of Artificial Intelligence (AI), unprecedented opportunities have arisen to redesign and optimize educational processes in medical sciences. Theoretical courses—often challenged by heavy content loads, passive learning, and limited individual interaction—can greatly benefit from AI-driven tools such as adaptive learning systems, educational chatbots, AI-based simulators, and learning analytics platforms. This policy brief presents a strategic framework for policymakers and faculty members of Iranian medical universities to responsibly and purposefully leverage AI in order to enhance the quality of theoretical education. The framework emphasizes three key dimensions: (1) empowering faculty through targeted training and continuous professional development; (2) establishing infrastructure and governance mechanisms that guarantee equitable, secure, and reliable access to AI technologies; and (3) integrating AI with evidence-based pedagogical methods to promote active learning, critical thinking, and student engagement. By aligning innovation with ethical standards and academic integrity, this approach can significantly improve theoretical medical education in Iran and position universities as regional leaders in AI-driven teaching excellence. This framework is grounded in a qualitative review of ministerial guidelines, academic workshops, and stakeholder consultations, ensuring that the recommendations align with both national policy priorities and institutional realities. This framework is grounded in a qualitative, purposive review of national and international policy documents and academic activities related to artificial intelligence in medical education. Specifically, approximately 10 key national and international policy documents, strategic reports, and ministerial guidelines were selected based on their relevance to AI governance, medical education reform, and higher education policy. In addition, insights were drawn from the review of outputs from approximately 20–30 academic workshops, expert panels, and faculty development programs conducted at Iranian medical universities and national academic forums between recent years. Stakeholder consultations included informal discussions with faculty members, educational experts, and academic leaders involved in curriculum design and educational technology initiatives. Data were analyzed using thematic qualitative analysis, whereby recurring concepts and priorities were iteratively identified, compared, and synthesized into the three core dimensions of the proposed framework. This approach enhanced the transparency, contextual relevance, and credibility of the resulting policy recommendations.
Expert system · Public policy · Artificial Intelligence in Healthcare and Education · Machine Learning in Healthcare · Simulation-Based Education in Healthcare
| Highly cited | No |
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