Jul 2026· Medical Teacher· pp.
1-17
· 0 citations· 15 references
Medicine
TL;DR
AI currently augments rather than transforms OSCE assessment, performing best in structured, observable tasks and least well in relational, situated, and culturally mediated competencies.
Abstract
INTRODUCTION
Objective Structured Clinical Examinations (OSCEs) are widely used to assess clinical competence, but face challenges related to examiner workload, scoring variability, delayed feedback, and resource demands. Although AI may address these constraints and support precision medical education, the evidence remains fragmented. This scoping review maps AI applications in OSCEs.
Methods
We followed PRISMA-ScR. We searched MEDLINE, Scopus, Embase, Web of Science, ERIC, LILACS, and IEEE Xplore from inception to June 2025. Eligible studies examined AI within any phase of an OSCE in health professions education. Data charting captured AI form, technology, OSCE phase, competencies, outcomes, faculty and resource implications, and ethical/governance issues. Synthesis used a hybrid approach: deductive coding with FACETS, SAMR, and operationalized P4 properties, plus inductive coding for emergent themes. Findings were stratified by evidence maturity rather than formal quality scoring.
Results
Of 421 records screened, 22 studies were included. AI was used for learner preparation, station/material construction, scoring/evaluation, and operational delivery. Benefits were strongest for grading, feedback speed, and consistency in structured tasks, but weaker for relational competencies. Most applications reflected SAMR Augmentation or Modification. Personalization dominated P4 alignment. Ethical concerns centered on privacy, bias, accuracy, transparency, access, and human oversight.
Discussion
AND
Conclusion
AI currently augments rather than transforms OSCE assessment, performing best in structured, observable tasks and least well in relational, situated, and culturally mediated competencies. Claims that AI delivers precision medical education through OSCEs are not yet supported by evidence; alignment with P4 is partial and conditional. Realizing the potential of AI in OSCEs will require human-in-the-loop governance with concrete safeguards and equity-focused implementation in resource-limited settings.
A theoretically grounded synthesis of research gaps and implementation priorities for AI development aligned with nursing clinical judgement is provided, identifying research gaps and implementation priorities for AI development aligned with nursing clinical judgement.
J. Alves, Ana Rita Ribeiro de Azevedo, R. Encarnação et al.· Journal of Advanced Nursing· 0 citations
Artificial intelligence (AI) is transforming medical education, enhancing knowledge acquisition, teaching, assessment, and curriculum delivery. While AI offers the potential to democratise access and improve inclusivity, little is known about how equity is addressed in AI-enabled medical education. This scoping review maps current evidence, identifies gaps, and provides insights for equitable implementation.
A scoping review was conducted following the Joanna Briggs Institute methodology. Peer-reviewed literature was identified through MEDLINE, Evidence-Based Medicine Reviews (Health Technology Assessment), Health and Psychosocial Instruments and Global Health. Screening, data extraction, and thematic analysis were performed by multiple reviewers. Quantitative data were summarised descriptively, and qualitative data were synthesised to identify narrative themes.
Of 256 records identified, 80 full-text articles were reviewed, and 35 studies were included. Most were published between 2022 and 2025 and originated predominantly from high-income countries. AI applications focused on large language models (48%), with curriculum design (49%) being the most common area of focus. Equity themes were: (1) equitable access (demographic, geographic, and distributed learning disparities); (2) bias (algorithmic, cultural, and socioeconomic); and (3) AI literacy (limited curricular exposure and preparedness).
Previous literature reviews have examined AI and its ethical use in medical education, but not through an equity lens. This scoping review highlights that medical education is at a pivotal juncture, shaped by converging imperatives: inclusivity and the rapid evolution of AI. To expand access and standardisation, equitable implementation requires AI literacy, contextually relevant tools, and active bias mitigation. Future research should prioritise inclusive, contextually appropriate, and accessible AI interventions across medical education.
J. Beattie, N. Waidyatillake, Cailin Mellberg et al.· International Conference on...· 0 citations
Future research should prioritize validated, multilingual, and patient-centered evaluation tools that integrate conventional information-quality attributes with clinically relevant dimensions such as safety, actionability, personalization, transparency, empathy, and response efficiency.
Jingwen Song, Norafisyah Makhdzir, Zarina Haron et al.· Frontiers in Public Health· 0 citations
Artificial intelligence (AI) is reshaping higher education through applications in teaching, learning, assessment, and curriculum design. Despite growing adoption, the literature remains fragmented, lacking structured frameworks to evaluate AI's integration and impact.
This review aimed to map how AI has been integrated into higher education, classify applications using the SAMR model (Substitution, Augmentation, Modification, Redefinition), analyze reported outcomes and challenges, and apply the FACETS framework (Form, AI use case, Context, Education focus, Technology, SAMR) to enhance comparability across studies.
The review followed PRISMA 2020 guidelines and was registered using the PRISMA-P protocol. Eight databases (PubMed, Medline, Web of Science, ProQuest, Scopus, Dimensions, OpenAlex, IEEE Xplore) were searched for English-language articles published between January 2015 and July 2025. Eligible studies examined AI integration in undergraduate higher education. A total of 959 records were screened in Rayyan QCRI. After removing duplicates and exclusions, 22 studies met the inclusion criteria. Data extraction covered study design, discipline, AI tool, SAMR level, outcomes, and FACETS dimensions.
Most included studies originated from North America and Asia, with medicine, computer science, and engineering as leading disciplines. ChatGPT, was the most common platform. Applications clustered around assessment automation and personalized learning support. Most implementations were at the Substitution or Augmentation levels, with fewer Modification and one Redefinition. Reported benefits included efficiency, personalization, and engagement, while challenges were equity, ethics, and academic integrity.
AI in higher education remains largely incremental, enhancing existing practices rather than transforming pedagogy. Its greatest potential lies in personalization.
M. AlSheikh, Rania Zaini, Manahel Almulhem et al.· Frontiers in Education· 0 citations
A conceptual Clinical Co-pilot Framework is proposed to position GenAI as a collaborative partner that supports clinicians rather than replaces them, which provides a conceptual basis for future empirical validation and may help inform the responsible implementation of GenAI in healthcare.
Lina Cheng, Chia-Yu Hung, Te-Nien Chien· International Journal of Adv...· 0 citations
Purpose To explore the current status of medical AI educational programs and to review how these programs were developed, implemented, and improved. Methods Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) guidelines, this study searched English articles from 2021 to April 2026 published in PubMed Central, Web of Science, Scopus, and MEDLINE databases. All curricula included in this review were implemented programs, not theoretical frameworks. Results Among the 7652 screened documents, 36 were identified. Data extraction focused on programs’ status, construction methods, and course frameworks. The number of programs has increased rapidly but remains small, with most in the pilot stage. Learners were predominantly medical students, with small class sizes. Curriculum development mainly relied on expert experience. Learning objectives mainly focused on the understanding level, but also covered advanced cognitive skills, and the content, covering AI principles, clinical applications, and ethics, demanded high cognitive engagement. Most programs were offered as modular elective courses in online or face-to-face formats, employing diverse teaching methods and activities. The learning outcomes and course evaluations mainly relied on student feedback. This review identified persistent gaps: the lack of standardized curriculum frameworks, affective learning objectives, and objective assessment tools; limited content on AI development, applications, collaboration, communication; weak integration with existing curricula; and insufficient involvement of learning designers. Conclusion Medical AI education programs are still in the initial stage. There is a need to accelerate curriculum development pathways, enhance practical skill trainings and affective competency developments, and establish effective instructional design and evaluation systems.
Yue Wang, He Wang, Ting Wang et al.· Journal of Multidisciplinary...· 0 citations
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