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 to identify recurring domains.
Abstract
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.
Abstract Background Generative AI (GenAI) is increasingly integrated into clinical learning and practice. However, medical students often lack the competencies required for safe and critical use, including prompt design, output verification, and recognition of limitations. Educational interventions that integrate GenAI with clinical reasoning frameworks remain limited. Objective This study evaluated a structured, theory-informed workshop integrating GenAI, prompt engineering, and clinical reasoning education to enhance medical students’ self-perceived AI literacy and collaborative learning attitudes, and to assess whether patient-centered orientation changed following intensive AI exposure. Methods We conducted a single-group, pre-post, explanatory sequential mixed methods study with fifth-year medical students enrolled at an academic medical center between April 2024 and May 2025. The 3-hour workshop comprised 6 modules integrating clinical reasoning, cognitive-bias awareness, verification-oriented GenAI use, and hands-on prompt engineering and centered on ChatGPT (OpenAI). Quantitative outcomes were self-reported measures from a 20-item self-report AI-literacy questionnaire adapted from the Meta AI Literacy Scale and were examined with exploratory and confirmatory factor analysis, the 6-item Patient-Practitioner Orientation Scale-Short, and a modified Collaborative Learning Attitude Scale (CLAS). Pre-post change was assessed using 2-tailed paired t tests with Benjamini-Hochberg correction and Cohen d; the Patient-Practitioner Orientation Scale-Short and CLAS were available for a subsample (n=46). Qualitative data from 6 interviews and 17 reflective narratives were analyzed using reflexive thematic analysis and integrated with the quantitative findings. Results Among 150 eligible students, 139 (92.7%) completed paired AI-literacy assessments. Self-perceived AI literacy improved across all domains (Cohen d=0.69‐0.93, all P<.001; all remaining significant after false discovery rate correction), and collaborative learning attitudes increased substantially (d=0.94, P<.001). Patient-centered orientation showed no significant change (d=0.03); however, baseline scores were concentrated at the favorable end of the scale (a floor/restricted-range effect), and the subsample analysis was underpowered (minimum detectable dz=0.42), so this null result is inconclusive rather than evidence of unchanged orientation. Gains did not differ by sex or across the sequential cohorts. Reflexive thematic analysis (interviews: n=6; reflections: n=17) identified five themes and one emergent theme describing a shift toward verification-oriented GenAI use: (1) understanding GenAI capabilities and limitations, (2) prompt-engineering skill development, (3) calibrated trust through verification, (4) GenAI-supported communication and collaboration, and (5) ethical considerations, with emerging reconceptualization of professional identity. Conclusions A brief, theory-informed educational intervention integrating GenAI with clinical reasoning was associated with medium-to-large improvements in self-perceived AI literacy and collaborative attitudes. No detectable change in patient-centered orientation was observed; however, this finding should be interpreted as inconclusive, given measurement and power constraints. Embedding verification practices within clinical reasoning frameworks may offer a scalable approach for preparing physicians for responsible human-AI collaboration. Future studies should incorporate comparative designs, performance-based assessments, and longitudinal follow-up.
Cheng-Heng Liu, Yu-Ting Chen, Chiun Hsu et al.· JMIR Medical Education· 0 citations
Evidence indicates a transition from isolated AI educational initiatives toward competency-based and longitudinal curriculum integration and effective AI education extends beyond technical literacy and increasingly incorporates ethical, clinical, and professional competencies necessary for responsible AI adoption in healthcare.
M. Morales-Cevallos, Diego Fabián Vique López, Catherine Hortensia Martínez Avalos et al.· Frontiers in Medicine· 0 citations
This curriculum offers a generalizable, no-cost model for closing the AI evaluation skills gap in healthcare education, combining interactive correction with a mandatory, learner-defined capstone project.
Hiba Hamdar· Journal of Medical Education...· 0 citations
Findings suggest that integrating EBM longitudinally across training, embedding teaching within authentic clinical scenarios, investing in faculty development, and aligning assessments with competency-based frameworks may strengthen skill retention and practice transfer.
S. Quon, Isabel Truong, Leah Moroz et al.· Evidence Based Health Policy...· 0 citations
Artificial intelligence (AI) is being increasingly utilized in medical education, with growing interest in its potential to enhance learning, assessment, and faculty support across undergraduate, postgraduate, and continuing medical education. While much of the literature focuses on specialty training or technology-driven innovation in isolation, family medicine represents a distinct educational domain characterized by its breadth, clinical uncertainty, multimorbidity, longitudinal care, prevention, and shared decision-making. These features present specific educational challenges and opportunities for integrating AI-supported tools. This narrative review synthesizes peer-reviewed literature on the application of AI in medical education, specifically within the context of family medicine. This review utilized PubMed and a consensus search engine to identify relevant reviews, empirical studies, and articles that address AI-supported learning, simulation training, competency-based education, workplace assessment, faculty support, and educational governance. The evidence was integrated through a narrative synthesis, guided by the Scale for Assessment of Narrative Review Articles. The literature indicates that AI can facilitate personalized learning pathways, augment simulation-based training, and aid in the integration of longitudinal assessment data, particularly in educational contexts characterized by distributed supervision and diverse learner requirements. There is a paucity of evidence concerning the application of AI in high-stakes assessments, autonomous decision-making, and its long-term impact on professional identity formation. In all areas, the successful incorporation of AI necessitates robust educational governance, faculty oversight and alignment with fundamental educational principles. AI should be considered an augmentative educational technology that can enhance but not supplant human-centered teaching, supervision, and professional judgment. Future research should prioritize the validation of educational outcomes, the clarification of ethical safeguards, and the establishment of best practices for the responsible integration of AI into family medicine education.
C. Wiedermann, Anne Wiedermann, Hendrik Reismann· Journal of Medical Education...· 0 citations
Preliminary evidence suggests that AI literacy educational interventions may improve nursing students' AI literacy, and future research should prioritize well-designed randomized controlled trials, the development of nursing-specific AI literacy assessment instruments and investigations of long-term outcomes to inform educational practices.
Min-Qi Xia, Qingqing Zhu, Ya-Xuan Luo et al.· Nurse Education in Practice· 0 citations
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