1. Introduction
Artificial intelligence (AI) has played a role in medical education for several years. From virtual simulations to adaptive learning tools, AI has been influencing medical education. The advent of generative AI (GAI) tools such as ChatGPT has increased the integration of AI in medical education. This rise presents both exciting opportunities for improving care and important questions about how we will use these tools in effective and ethical ways [
1,
2]. In recognition of the profound impact of AI on medical education and healthcare practice, 76% of medical schools in Canada and the United States have integrated AI into their curriculum [
3]. This imperative is evident across the world, as countries like South Korea strive to define the AI competencies their medical students will need in this AI-influenced world [
4].
2. Defining AI
AI is a broad, encompassing term for different technologies. AI powers clinical decision-making tools, drug discoveries, precision medicine, among others. The AI in these instances is machine language technology that is embedded into other tools. GAI refers to artificial intelligence designed to create new content from patterns learned during training. It can produce text, images, audio, and other media by drawing on very large datasets. Large language models (LLMs) are a type of GAI that learns from large amounts of language data to respond in ways that feel conversational and meaningful to users. These models are trained at scale so they can recognize patterns in language and generate new text that aligns with the context of a question or prompt. Examples of these tools include ChatGPT, Google Gemini, Microsoft CoPilot, and other conversation-based systems.
LLMs have gained popularity for a few reasons. First is the ease of use. Users can interact directly with LLMs in a conversational manner [
5]. The interface of a messaging structure is easy to use by people of all ages, and users have the ability to refine their prompts to get the response they are looking for. Furthermore, they can engage with the tools in various languages and modalities. Second, is accessibility. The major LLM providers including OpenAI, Google, Anthropic, Microsoft, and others have all provided freemium models that include a feature restricted free version. This allows everyone to be able to use these tools even if at a basic level. Lastly, is their vast knowledge base. From coding to generating poems, predicting stock market movements, explaining concepts, planning holidays, and designing unique recipes, the vast capabilities of these tools add to their popularity [
6,
7].
3. Generative AI in medical education
GAI in medical education offers new ways to support learning for both students and educators. Assessment processes can be simplified through auto-grading [
5]. For students, LLMs can generate study questions, simulate testing environments, and explain concepts. LLMs enable personalized learning by functioning virtual tutors and creating individualized study plans for diverse student needs [
8]. Crucially, LLMs provide fast access to information on medical topics, illnesses, and treatments, offering quick clarification to students and professionals alike. These features of LLMs create new opportunities to enhance knowledge development and application within medical training programs [
3,
8]. Acknowledging the popularity of LLMs in various aspects of medical education, the authors of the four articles in this Special Issue examine the use of LLMs in medical education from different angles. The four articles reflect the growing interest in how LLMs support medical teaching and learning.
4. Current research landscape
Research on artificial intelligence in medical education has increased rapidly. Much of this work focuses on assessment and performance measurement because these areas allow clear evaluation and often benefit from automation. Research has focused on the ability of LLMs to generate standardized exam questions, assess student work, and also assess medical school curricula [
6,
9,
10]. A key application of ChatGPT in medical education is the creation of realistic clinical simulation scenarios, which offer students a safe, efficient way to practice diagnostic and clinical skills while also helping to address the shortage of standardized patients [
11,
12]. ChatGPT can also serve as a teaching resource, innovating teaching methods such as problem-based learning and assisting in curriculum development [
13,
14]. However, fewer studies highlight faculty development and continuing professional learning with GAI. The articles in this Special Issue help address this gap by exploring both current practices and innovative uses of GAI for instruction, assessment, and professional growth.
5. Scope of the Special Issue
Recent scholarship on artificial intelligence in medical education has largely emphasized describing emerging tools, testing model performance, or offering broad conceptual perspectives on future possibilities. This Special Issue takes a different approach by focusing on how GAI is being implemented in real educational settings and how these implementations shape teaching, learning, assessment, and equity. Rather than centering technological novelty alone, the collection highlights practical integration, faculty preparation, learner experience, and institutional consequences. The four articles in this Special Issue explore LLMs in medical education as both tools for learning and subjects of pedagogical reform. They demonstrate the breadth of LLMs’ abilities, from supporting faculty development and enhancing assessment quality to aiding conceptual understanding, while simultaneously raising questions of equity and access. They offer practical insights for medical educators who are navigating this emerging field and seeking grounded examples to guide their own work.
Burbage and Styron [
15] explore how GAI can support learners in interpreting and applying educational research theory. They highlight how GAI can help clarify ideas, provide examples, and reinforce comprehension through interactive dialogue. Their work positions GAI as a supportive learning partner that can foster deeper conceptual engagement. Their findings suggest that GAI can be used as a structured dialogic support in research training courses where faculty time for individualized theory mentoring is limited. Ng et al. [
16] evaluated a large language model as a first-stage reviewer of single best answer questions. Their system performed efficiently and showed strong alignment with human reviewers on structured quality criteria. Their work demonstrates a promising approach to strengthening assessment quality assurance through human-AI collaboration. This approach may help institutions scale question review processes while preserving human oversight for clinical relevance and educational judgment. Kumar and Mans [
17] present an original research study describing a 1-hour virtual workshop designed to prepare health professions educators to teach effectively with GAI. Their work offers a practical and adaptable model for faculty development. Their workshop model can be adopted or adapted by faculty development units seeking rapid and accessible preparation for teaching with GAI. Soh et al. [
18] examine equity concerns related to the rapid adoption of artificial intelligence in medical education. They note that access to AI tools and AI literacy differ widely among learners, which can create advantages unrelated to clinical competence or academic performance. Their paper encourages educators to treat AI adoption as a structural shift that requires sustained attention to fairness and learner support. Their analysis also highlights the need for institutional policies that ensure equitable access to AI platforms and explicit AI literacy support across learner groups.
6. Why this Special Issue matters now
Medical educators are being asked to make decisions more quickly than established guidance can be developed. Experimentation is happening across institutions, yet ethical and responsible use requires shared principles and sustained attention. Medical students are already using GAI in their learning, sometimes in ways that extend beyond current policies. Professional organizations have begun to release recommendations that emphasize ethical use in education and clinical care, but faculty development and continuing education structures have not yet caught up. Educator leadership is necessary to ensure that the introduction of GAI protects ethical decision making and preserves the integrity of medical training.
7. Future directions
Looking ahead, medical educators will need to prepare learners not only to use GAI tools, but also to understand their limits, assumptions, and implications for patient care. Ethical engagement, AI literacy, and ongoing evaluation of outcomes will be necessary to ensure that adoption strengthens learning instead of widening disparities. Collaboration among educators, institutions, and learners will guide the development of shared strategies and safeguards. Evidence informed innovation will help GAI become a tool that promotes high quality and equitable education.
8. Call to action
This Special Issue invites continued conversation about the potential of GAI in medical education. The future will depend on how well we integrate technology with the human relationships, professional judgment, and reflective practice that define health professions learning. With curiosity and commitment, educators can shape a future in which GAI expands opportunities for learning while upholding excellence in patient care.
Acknowledgements
The opinions and assertions expressed herein are those of the author(s) and do not necessarily reflect the official policy or position of the Uniformed Services University or the Department of War.
Funding
None.
Conflicts of interest
No potential conflict of interest relevant to this article was reported.
Author contributions
Conception or design of the work: EJ, AS. Data collection: EJ. Data analysis and interpretation: EJ, AS. Drafting the article: EJ. Critical revision of the article: EJ, AS. Final approval of the version to be published: EJ, AS.
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