A Framework for Artificial Intelligence in Medical Education: Could I, Would I, Should I?

Medical Education and Artificial Intelligence

Artificial intelligence (AI) is increasingly influencing medical education, with rapid developments in tools that promise to enhance how physicians teach, learn, and assess competency. While the broader literature on AI in medical education is growing, most studies focus on specific AI functions, like rapidly training novices in bronchoscopy,¹ and not on generative AI (GAI)—text-based models like ChatGPT.

The Association of American Medical Colleges has offered ChatGPT-generated, then human-edited principles for responsible use of AI in medical education.² Initial work in undergraduate medical education has led to proposed AI competencies.³ However, graduate medical education (GME) lags in pragmatic frameworks and strategies for their application. Among the existing GME-relevant literature, much of the work is theoretical and descriptive, only outlining capabilities or raising concerns. A 2024 review in Frontiers in Medicine, for example, provides summaries of the use of GAI in GME settings and discusses opportunities, such as prompt engineering, and risks, such as automation bias, with minimal discussion of how GME educators can practically implement GAI tools in real-world settings.⁴ If practical tips are provided, they are general in nature without actionable steps to guide day-to-day teaching.⁵

This Perspectives article offers a practical, theory-informed, decision-making tool—the “Could I, Would I, Should I?” framework—to help health professions education leaders and educators fill this gap by engaging proactively with GAI tools like ChatGPT.

Could I Use It?

GAI has rapidly attracted many users because it is powerful, accessible, and easy to use. Through natural language processing, GAI has learned to communicate in human languages and can readily translate between these languages, assisting educators in engaging more diverse learners. Unfortunately, as GAI works by predicting a likely response based on probabilistic reasoning about data it has previously encountered, the content it produces can be vague, generic, and inaccurate. Therefore, at least for the time being, GAI may be most useful in the beginning stages of brainstorming and middle stages of refining educational work, rather than producing a final product. For example, GAI can initially serve as a standardized “patient” for practicing communication skills by offering generic, typical responses. In later editing stages, GAI can rapidly analyze a practice transcript to provide coaching and feedback. GAI is designed for dialogue, so iterative back-and-forth exchanges tend to elicit more satisfying results than a single request. If unsure how to write an effective prompt, you can simply ask the GAI to guide you (and, for reasons that remain unclear, you may get better results if you ask nicely). For effective prompting, we often utilize the user-friendly ICIO framework: Instruction, Context, Input Data, and Output Structure. In the Figure, we provide details about the framework and display an example.

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Would I Use It?

One of the challenges facing learners in medicine is how to organize and integrate facts and ideas into frameworks that form the building blocks of expertise in medicine. Cognitive psychologists have studied this learning process for many years, and 5 core learning strategies have been identified as critical for mastery of a topic.⁶ These strategies are (1) spaced retrieval, (2) elaboration, (3) reflection, (4) interleaving, and (5) generation, which form the mnemonic RE-RIG (see Table). Each of these strategies is a highly active process that requires the learner to engage with the material. Additionally, to advance from expertise to mastery of a clinical topic, clinicians require repeated exposure to multiple patient cases with feedback on one’s performance, so that the pathophysiological knowledge becomes embedded within the mind as “illness scripts.”⁷ These scripts are effortlessly retrieved, allowing for highly accurate diagnoses within minutes of working with patients. Unfortunately, GAI can remove much of the mental effort of learning by taking a learner’s input and producing a pleasing summary of complex ideas for memorization. This bypasses the struggle required by these learning strategies and may lead to a regression to the mean, both elevating struggling learners by providing them with average answers to clinical problems and lowering top performers by limiting the creative insights produced by mastery of a topic. To counter this trend, as educators, we will need to teach our learners how to use GAI in a manner that allows these cognitive strategies to flourish so that expertise can grow. Educators may benefit the most from GAI if they understand it as a “copilot” that helps provide feedback so that they can challenge and refine their ideas.⁸ In terms of “Would I use it?” we believe that the use of AI in medical education is most effective when combined with evidence-based effective learning strategies.

Table How a Learner Can Use ChatGPT as a Copilot and Leverage its Power Synergistically With Cognitive Learning Strategies

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Should I Use It?

Numerous ethical concerns have been associated with the development, implementation, and use of AI. Well-established bioethical principles like non-malfeasance, beneficence, justice, and fidelity⁹ provide a familiar structure, conceptual clarity, and proven applicability across similar contexts for organizing key ethical concerns.

Conclusion

AI is here to stay and will substantially impact medical education. While tools like ChatGPT can help medical educators generate large amounts of structured content quickly if prompted effectively, their predictive nature can also bypass essential learning processes and raise ethical concerns, making the role of educators more critical than ever. We propose the “Could I, Would I, Should I?” framework and associated strategies to help GME leaders make thoughtful, context-specific decisions about when and how to use GAI. To move to deliberate integration, we recommend that GME curricula support AI literacy. These curricula should include structured prompting strategies such as ICIO; practical use that reinforces critical thinking, reflection, and deep learning; and ethical considerations. Educators should link emerging GAI competencies to established GME core competencies and offer adaptable, program-specific frameworks. Additionally, we encourage GME stakeholders to consult the American Association of Directors of Psychiatric Residency Training AI in Psychiatric Education Taskforce Report,¹⁰ which outlines potential AI applications to support informed, context-driven decisions. By deliberately shaping AI’s use, GME programs can ensure that GAI strengthens—rather than undermines—the mission to train skillful, ethical, and reflective physicians.