Artificial intelligence (AI) is becoming a core educational concern in undergraduate medical education as AI-enabled tools increasingly shape clinical workflows, learning environments, and patient care. The challenge is no longer simply whether AI should be included in the curriculum, but how AI literacy should be bounded for undergraduate learners and translated into teachable, observable, and assessable educational outcomes.
This focused conceptual narrative review synthesized literature on AI literacy and related constructs in undergraduate medical education, using a structured search and interpretive synthesis with competency-based medical education (CBME) as an interpretive lens. PubMed and ERIC were searched for English-language literature from 1 January 2020 to 15 April 2026. Local screening records identified 94 standardized bibliography records, 66 records screened after deduplication, 40 full-text reports assessed, and 30 publications contributing to the final synthesis.
Five recurring domains were identified: Foundational AI knowledge; applied clinical interpretation and use; data literacy and critical appraisal; ethics, law, and professional responsibility; and human–AI collaboration and professional formation. Through a CBME lens, these domains can be translated into learning outcomes, contextualized tasks, observable performances, and programmatic assessment evidence.
The literature most strongly supports conceptual clarification, domain identification, and curricular translation, whereas evidence for longitudinal development, observable performance, and validated undergraduate assessment remains limited. The proposed framework, examples, milestones, and rubric anchors are synthesis-informed design propositions that require empirical validation before high-stakes use.
Fu C, Li J, Fan H, Ren R, Cui K, Zhang Y, et al. AI literacy in undergraduate medical education: a competency-based interpretive framework for curriculum and assessment. Front Med (Lausanne). 2026;13:1871524. doi:10.3389/fmed.2026.1871524.
García Dieguez M. Comment on the AI literacy in undergraduate medical education: a competency-based interpretive framework for curriculum and assessment. [Internet]. Pan American Health Organization. Bibliographic Repository. Available at: https://campus.paho.org/en/repo/literacy-undergraduate-medical-education-competency-based-interpretive-framework-curriculum
García Dieguez M.
CEEProS Universidad Nacional del Sur
This article is essential reading for educators who are incorporating artificial intelligence into medical education and want to avoid limiting its teaching to the use of technological tools. From a competency-based medical education perspective, the authors propose a conceptual framework that translates AI literacy into observable competencies, learning outcomes, and assessment strategies. It is particularly valuable for curriculum designers, undergraduate medical education leaders, and faculty seeking to integrate AI longitudinally, ethically, and in alignment with future clinical practice.
This conceptual narrative review identified 30 publications through a structured search of PubMed and ERIC (2020–2026). Unlike previous reviews focused primarily on AI technologies, this work examines how to define what undergraduate medical students should learn about artificial intelligence and how these competencies should be assessed within a competency-based medical education framework. Its main contribution is the proposal of five core AI literacy domains: foundational AI knowledge; applied clinical interpretation and use; data literacy and critical appraisal; ethics, law, and professional responsibility; and human–AI collaboration with professional identity formation. Based on these domains, the authors develop examples of learning outcomes, educational activities, progressive performance milestones, and programmatic assessment strategies, including OSCEs, portfolios, workplace-based assessment, and case analysis. The article emphasizes that AI should be integrated longitudinally across the curriculum rather than taught as a stand-alone subject.
Although the review follows a structured search strategy and provides a well-organized synthesis, it is a conceptual narrative review and therefore does not include formal methodological quality appraisal or risk-of-bias assessment of the included studies. Furthermore, the proposed curriculum models, milestones, and rubrics are synthesis-informed frameworks that require empirical validation before being used in high-stakes assessment. The authors also acknowledge that evidence supporting longitudinal assessment and developmental progression of AI competencies remains limited.
- Integrate AI longitudinally into existing courses (clinical reasoning, ethics, evidence-based medicine, and communication) rather than creating a separate subject.
- Teach students to critically interpret AI-generated outputs by identifying bias, errors, uncertainty, and limitations before applying them to clinical decisions.
- Assess competencies through authentic tasks such as AI-supported case analysis, OSCEs, portfolios, and workplace-based assessment instead of relying exclusively on written examinations.
- Develop AI literacy progressively across five competency domains: foundational AI knowledge, clinical application, data literacy and critical appraisal, professional responsibility, and human–AI collaboration.
- Prepare faculty to supervise the responsible use of AI and provide feedback focused on clinical reasoning rather than technical operation of AI tools.