Journal of Advances in Medical Education & Professionalism

Review article

Regulation and Ethical Frameworks for AI in Health Professions Education: A Critical Narrative Review

YASAR AHMED; SIMAA KHAYAL

Volume 14, Issue 4 , October 2026, Pages 307-319

https://doi.org/10.30476/jamp.2026.110257.2340

Abstract

Introduction: Integrating artificial intelligence (AI)—particularly generative AI and large language models (LLMs)—into health professions education offers substantial pedagogical ... Read More Introduction: Integrating artificial intelligence (AI)—particularly generative AI and large language models (LLMs)—into health professions education offers substantial pedagogical opportunities, while highlighting critical gaps in existing ethical and regulatory frameworks. This critical narrative review examines how contemporary literature conceptualizes and addresses these challenges.Methods: Publications from 2020–2025 were identified through searching PubMed, Scopus, and ERIC, focusing on AI, medical and health professions education, ethics, governance, and regulation. The initial search yielded 452 records; following two-stage screening (title/abstract and full-text review), thirty-four sources met the pre-defined inclusion criteria. These sources, includingempirical studies, systematic reviews, policy documents, and conceptual papers, were included. Reflexive thematic analysis was used to synthesize the findings, while SANRA (Scale for theAssessment of Narrative Review Articles) informed the weighting of sources, which were categorized into high-, moderate-, and low-weight tiers.Results: Four themes emerged: 1) a regulatory vacuum marked by policy lag and fragmented institutional responses; 2) reinterpretation of autonomy, beneficence, nonmaleficence, andjustice in relation to opaque, datadriven systems, 3) generative AI’s impact on scholarship, authorship, and academic integrity; and 4) equity, bias, and professional identity formation in AImediated learning environments. Integrating these themes with crosssector frameworks and the NIST AI Risk Management Framework, the review develops a riskbased governance taxonomy for AI in Health Professions Education, distinguishing low, medium and highrisk educational use cases.Conclusion: This synthesis indicates that current literature favors evolving beyond reactive policies. Consequently, we propose a theoretical shift toward risk-proportionate governance. We suggest that human-in-the-loop pedagogy should be prioritized as a conceptual framework to guide future research and policy development, rather than presenting it as a validated outcome.

Review article

Medical Education Practices and Their Effects on Educational Outcomes in India: A Scoping Review and Evidence Gap Map

ARAVIND P GANDHI; AMOL DONGRE; PRADEEP DESHMUKH; KALYANI P DESHMUKH; SUJIV AKKILAGUNTA; YOGESH BAHURUPI; PRIYANKA YADAV; MATHAVASWAMI VIJAYAGEETHA; MUBASHSHERA KHAN; NISHAANT RAMASAMY; AISWARYA N R LAKSHMI N R; NANDEESH KURAVATTI; VIKAS DHIMAN; SANGHAMITRA PATI

Volume 14, Issue 4 , October 2026, Pages 320-334

https://doi.org/10.30476/jamp.2026.111416.2419

Abstract

Introduction: The landscape of medical education research has undergone a considerable change in India. This scoping review and the evidence gap map (EGM) explore the medical education ... Read More

Original Article

Implementing a Three-Phase Four-Dimensional Blended Learning Model in Obstetrics and Gynecology Clinical Training: A Quasi-Experimental Study

SHULING ZHANG; KUIMEI ZHANG; XIAOXIA WANG; PEIPEI ZHANG; JIEJIE LI; WENMING CAO

Volume 14, Issue 4 , October 2026, Pages 335-341

https://doi.org/10.30476/jamp.2026.109725.2316

Abstract

Introduction: A significant challenge in obstetrics and gynecology (OB/GYN) training is effective translation of theoretical knowledge into clinical competence. To address this gap, ... Read More Introduction: A significant challenge in obstetrics and gynecology (OB/GYN) training is effective translation of theoretical knowledge into clinical competence. To address this gap, we developed and evaluated a structured “Three-Phase Four-Dimensional” (3P4D) blended learning model to enhance clinical skill acquisition among medical students during their obstetrics and gynecology clerkship.Methods: A quasi-experimental design was adopted, as it allowed for rigorous comparison of educational interventions while accommodating the practical constraints of clinical rotationscheduling in a real-world teaching hospital. A total of 126 fifth-year clinical medical students undertaking their obstetrics and gynecology clerkship were enrolled. To ensure strict group size equality, we randomly assigned participants using block randomization with a fixed block size of 6. Within each block, a predetermined allocation ratio of 2:2:2 to the three groups was applied and randomly permuted via a computer-generated sequence, guaranteeing a final distribution of exactly 42 students per group: traditional lecture-based learning (LBL), online-intensive learning (OL), and the novel online–offline blended learning (OBL) group implementing the full 3P4D model. Outcome measures included Objective Structured Clinical Examination (OSCE), knowledge-based tests, procedural skill assessments, and a self-developed satisfaction questionnaire withestablished validity (content validity index>0.80; construct validity confirmed through exploratory factor analysis). Data were analyzed using IBM SPSS Statistics for Windows, Version 25.0. Intergroup comparisons were performed using one-way ANOVA; where significant differences were detected, post-hoc pairwise comparisons were conducted using the Least Significant Difference (LSD) test.Results: The OBL group demonstrated significantly higher scores in OSCE (89.5±4.2 vs. 80.3±4.8 vs. 76.8±5.1, P<0.001), skill operation (90.2±4.0 vs. 81.0±5.5 vs. 78.5±6.0, P<0.001), and learner satisfaction (4.6±0.5 vs. 3.8±0.7 vs. 3.5±0.8, P<0.001) compared to OL and LBL groups, respectively. Knowledge scores showed no significant differences among the groups.Conclusion: The 3P4D blended learning model effectively enhanced clinical skill acquisition and competency transformation among medical students during their OB/GYN clerkship, bridging the gap between theoretical knowledge and clinical practice.

Original Article

Evaluating an Interpersonal Communication Training Program Based on the Greet–Invite–Discuss (GID) Guideline for Immunization Officers in Tana Toraja District, Indonesia

MOHAMMAD ALIEF IQRA; TITI SAVITRI PRIHATININGSIH; MORA CLARAMITA

Volume 14, Issue 4 , October 2026, Pages 342-352

https://doi.org/10.30476/jamp.2026.111812.2447

Abstract

Introduction: Structured communication training may strengthen immunization education, but it remains limited in resource-constrained settings. Tana Toraja recorded South Sulawesi’s ... Read More Introduction: Structured communication training may strengthen immunization education, but it remains limited in resource-constrained settings. Tana Toraja recorded South Sulawesi’s lowest full basic immunization coverage in 2024. We evaluated a one-day Greet–Invite–Discuss (GID)-based interpersonal communication training program for district immunization officers.Methods: A single-group Kirkpatrick Levels 1–2 evaluation enrolled 24 facility-designated representatives through a facility-level census covering all 23 public primary health centers (Puskesmas) and one private maternal and child hospital. The 09:00–14:30 program provided 265 contact minutes: 110 minutes of didactic instruction/demonstration, 90 minutes of role-play (didactic-to-role-play ratio, 55:45), and 65 minutes of orientation, reflection, and application planning. A physician investigator delivered expert-reviewed slides, a standardized scenario, and a behaviourally anchored GID checklist under the supervision of two senior medical-education faculty members. Level 1 reactions were assessed post-training and Level 2a knowledge both before and after training. Non-normality (Shapiro–Wilk: pre W=0.877, p=0.007; post W=0.834, p=0.001) prompted Wilcoxon analysis. Immediately after training, three observers rated Level 2b video-recorded peer simulations using the 22-item checklist; this post-only assessment measured achievement rather than retention or behavioural change. Approval of the ethics committee was obtained from the Medical and Health Research Ethics Committee, Universitas Gadjah Mada (KE/FK/0302/EC/2026; 13 February 2026).Results: The mean reaction score was 4.66±0.29/5.00. Median knowledge scores increased from 77.50 (IQR 45.00–90.00) to 95.00 (IQR 85.00–100.00; Z=−4.023, p<0.001, r=0.82); all domain scores increased (all p≤0.001), with a post-test ceiling effect. Post-training skills achievement was 67.80% overall (intraclass correlation coefficient [ICC]=0.831), highest for Greet (80.21%) and lowest for Invite (59.72%; ICC=0.574; 95% CI− 0.020 to 0.838). Knowledge change was not significantly associated with total or domain-specific skills achievement (all |ρ|≤0.221; all p≥0.300).Conclusion: GID training was well received and associated with increased immediate knowledge, but the single-group design precluded causal attribution. Post-only skills do not establish improvement, retention, or workplace behaviour; these findings provide a baseline for refining the program; however, Invite-domain estimates should be interpreted with caution.

Original Article

Agenda-Setting for Sustainable Financing of Medical Education: A Mixed-Methods Policy Analysis Using Multiple Streams and Policy Triangle Frameworks

SAEED AMINI; MASOUD ABOULHALLAJ; ALI VALINEJADI; MEGHDAD RAHATI

Volume 14, Issue 4 , October 2026, Pages 353-363

https://doi.org/10.30476/jamp.2026.111161.2400

Abstract

Introduction: Sustainable financing of medical education is critical to health workforce development and health system performance. While existing research has largely examinedfinancing ... Read More

Original Article

Artificial Intelligence–Based Computer Vision Feedback to Improve Clinical Performance in Surgical Technology Education

KIARASH KAMBOOZIA; SEDIGHEH HANNANI; NAZANIN SARRAF SHAHRI; ZEYNAB BARZEGAR; SAEID ZARIF GHASEMIAN

Volume 14, Issue 4 , October 2026, Pages 364-377

https://doi.org/10.30476/jamp.2026.111362.2415

Abstract

Introduction: Advancements in artificial intelligence (AI) and computer vision offer new opportunities to improve clinical skills training. This study developed a real-time, AI-based ... Read More Introduction: Advancements in artificial intelligence (AI) and computer vision offer new opportunities to improve clinical skills training. This study developed a real-time, AI-based computer vision system for detecting surgical instruments and evaluated its association with clinical performance among surgical technology students.Methods: A quasi-experimental pre-test/post-test study with a control group was conducted among 48 eligible surgical technology students at Iran University of Medical Sciences during the 2025–2026 academic year. Participants who met the predefined inclusion criteria were enrolled using census sampling. Following ethics approval (IR.IUMS.REC.1403.928), the participants were allocated to an intervention group (n=31), which received immediate AI-generated visual feedback after each instrument arrangement task, and a control group (n=17), which received routine instructor-led training without AI feedback. A YOLOv8-based deep learning model was trained using transfer learning on annotated images of surgical table setups. Clinical performance was assessed using a validated observational checklist (CVI=0.90; Cronbach’s α=0.87). Data were analyzed using analysis of covariance (ANCOVA), the Wilcoxon signed-rank test, Spearman’s correlation, and the intraclass correlation coefficient (ICC) in SPSS version 27. All 48 participants completed the study (attrition rate=0%).Results: The AI model achieved excellent object detection performance (precision=0.974, recall=0.979, mAP@0.5=0.981, mAP@0.5:0.95=0.808), with an average inference time of 1.6 seconds per image. Clinical performance was significantly higher following AI-assisted feedback (Wilcoxon signed-rank test, Z=−4.879, p<0.001, r=0.88). After adjustment for baseline performance using ANCOVA, AI-assisted feedback was associated with higher post-intervention performance scores than conventional training (F (1,45)=19.51, p<0.001, partial η²=0.302). Excellent agreement was observed between AI-generated assessments and expert human ratings (ICC=0.984).Conclusions: Real-time, AI-based computer vision feedback was associated with improved surgical instrument arrangement performance among surgical technology students while providing an objective and reliable assessment of practical skills. The proposed system represents a promising proof-of-concept educational tool for simulation-based training and competency assessment and may complement conventional instructional approaches in surgical technology education.

Original Article

Gradual Transformation toward Expertise: A Grounded Theory of Clinical Medical Teachers’ Development Process

AZIZEH BARRY; KAMRAN SOLTANI ARABSHAHI; FAZLOLLAH AHMADI; ZOHREH SOHRABI

Volume 14, Issue 4 , October 2026, Pages 378-391

https://doi.org/10.30476/jamp.2026.110120.2334

Abstract

Introduction: Expertise is an experiential capability that cannot be acquired through formal education alone; rather, it develops through sustained reflection and iterative practice. ... Read More Introduction: Expertise is an experiential capability that cannot be acquired through formal education alone; rather, it develops through sustained reflection and iterative practice. Despite the importance of expert clinical faculty, the process of attaining this educational expertise has not been systematically conceptualized. This study was conducted to develop a paradigmatic model of the educational competency development process in clinical medical faculty and identify its dimensions, components, and interrelationships.Methods: This qualitative grounded theory study was conducted in 2024–2025 at Iran University of Medical Sciences using the Corbin and Strauss approach (2015). A total of 15 participants, including 13 clinical faculty members (with at least five years of teaching experience and recognized teaching competence) and 2 medical students, were initially recruited through purposive sampling. Snowball sampling was used solely as a recruitment technique to identify additional information-rich participants. As we collected and analyzed data, we used theoretical sampling. This helped us refine the categories and reach theoretical saturation. Data were collected through semi_structured, in-depth interviews lasting 30–50 minutes (mean: 45 minutes). All interviews were audio-recorded and transcribed verbatim. Data were analyzed concurrently with data collection using the constant comparative method, including open coding, category development, contextual analysis, and theoretical integration. Rigor was ensured through prolonged engagement with the data, member checking, peer debriefing, and the maintenance of an audit trail. Results: Data analysis led to the emergence of the core category, “Gradual Transformation toward Expertise,” which explains how clinical faculty members evolve from expert clinicians into transformative educators. This process is dynamic, non-linear, and highly context-dependent. Five interrelated themes describe this developmental trajectory: 1) reflection and competence development, 2) experiential learning and clinical observation, 3) mentorship and knowledge transfer, 4) motivational and contextual influences, and 5) barriers and professional transformation. The process was shaped by six contextual factors: a) the complex and dynamic nature of clinical education; b) the need for continuous competency development; c) generational differences in teaching and learning styles; d) workload and competing clinical responsibilities; e) the necessity of integrating practical experience with scientific evidence; and f) an organizationalculture that prioritizes treatment over education. Additionally, causal conditions such as multiple professional pressures and generational gaps further influenced the pace and direction of thistransformation. Overall, the findings depict expertise development as a gradual, evolving process through which faculty members reconcile their clinical and educational roles to become effectiveand transformative clinical educators.Conclusion: Clinical teacher expertise develops through a dynamic interplay of individual effort, experiential learning, and environmental factors. This transformation requires personal commitment to lifelong learning and reflection, supported by organizational initiatives such as faculty development programs, mentoring, and aligned incentive systems. The proposed grounded theory provides a practical framework for designing professional development initiatives and fostering educational settings that enhance expertise in similar non-Western contexts.

Original Article

Barriers to Professional Development among Clinical Faculty Members: A Qualitative Study

ALI ASGHAR HAYAT; MOHAMMAD HASAN KESHAVARZI; AZITA TABATABAIE ESFAHANI

Volume 14, Issue 4 , October 2026, Pages 392-493

https://doi.org/10.30476/jamp.2026.110674.2372

Abstract

Introduction: Faculty and professional development are intrinsically linked to organizational effectiveness and academic excellence. Understanding barriers to faculty development can ... Read More

Commentary

Seven Technology-Based Active Learning Models for Educational System Transformation: A Policy Brief

ZAHRA SADEQI-ARANI; HAMED TAHERDOOST

Volume 14, Issue 4 , October 2026, Pages 404-410

https://doi.org/10.30476/jamp.2026.111498.2423

Abstract

Introduction: With the spread of new technologies, significant changes have occurred in teaching and learning, which can be used to overcome the challenges of the traditional education ... Read More Introduction: With the spread of new technologies, significant changes have occurred in teaching and learning, which can be used to overcome the challenges of the traditional education system,such as lecture-based teaching methods, limited opportunities for active learning, lack of attention to problem-solving skills such as creative and critical thinking, and rote assessment methods.Method: The study adopts a policy analysis and conceptual synthesis approach, reviewing existing literature to propose seven technology-based educational models: gamified learning,problem-based learning, AI-driven classrooms, IoB-based smart schools, team-based learning, lifelong learning, and flipped learning.Results: Results indicated that these models collectively enhanced student engagement, motivation, personalization, collaboration, critical thinking, and well-being. Gamified learning increased motivation and participation; PBL strengthened problem-solving and experiential learning. Also, AI-driven classrooms enabled adaptive, personalized learning, and IoB-based smart schools supported real-time monitoring of learning behaviors and mental health. Moreover, team-based learning improved collaboration and communication skills, and lifelong learning fostered self-regulated and continuous skill development; flipped learning increased active classroomparticipation and academic performance. Together, these approaches provide complementary pathways for addressing structural inefficiencies in traditional education systems and improving overall educational quality and equity.Conclusion: Integrating these seven models offers a coherent framework for transforming traditional education into a technology-enhanced, learner-centered system. However, successful implementation requires phased adoption, infrastructure development, teacher training, and attention to ethical and equity considerations. Gradual integration, starting with low-cost pedagogical models and progressing toward advanced AI and IoB systems, can lead to sustainable and context-sensitive educational reform in developing educational contexts overall.

Letter to Editor

Moving Toward Fourth-Generation Universities: Practical Steps beyond Digitalization

RAMIN MAZAHERI NEZHAD FARD; MARYAM YAZDANIZAD

Volume 14, Issue 4 , October 2026, Pages 411-412

https://doi.org/10.30476/jamp.2026.112078.2460

Journal of Advances in Medical Education & Professionalism

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Volume & Issue: Volume 14, Issue 4, October 2026, Pages 307-412

Regulation and Ethical Frameworks for AI in Health Professions Education: A Critical Narrative Review

Abstract

Medical Education Practices and Their Effects on Educational Outcomes in India: A Scoping Review and Evidence Gap Map

Abstract

Implementing a Three-Phase Four-Dimensional Blended Learning Model in Obstetrics and Gynecology Clinical Training: A Quasi-Experimental Study

Abstract

Evaluating an Interpersonal Communication Training Program Based on the Greet–Invite–Discuss (GID) Guideline for Immunization Officers in Tana Toraja District, Indonesia

Abstract

Agenda-Setting for Sustainable Financing of Medical Education: A Mixed-Methods Policy Analysis Using Multiple Streams and Policy Triangle Frameworks

Abstract

Artificial Intelligence–Based Computer Vision Feedback to Improve Clinical Performance in Surgical Technology Education

Abstract

Gradual Transformation toward Expertise: A Grounded Theory of Clinical Medical Teachers’ Development Process

Abstract

Barriers to Professional Development among Clinical Faculty Members: A Qualitative Study

Abstract

Seven Technology-Based Active Learning Models for Educational System Transformation: A Policy Brief

Abstract

Moving Toward Fourth-Generation Universities: Practical Steps beyond Digitalization

Abstract