Journal of Advances in Medical Education & Professionalism

Abstract 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, including
empirical studies, systematic reviews, policy documents, and conceptual papers, were included. Reflexive thematic analysis was used to synthesize the findings, while SANRA (Scale for the
Assessment 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, and
justice 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.

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 techniques in curricular components of medical education and identify the gaps in medical education research in India.
Methods: Studies published from inception till 13th June 2025 in five databases of MEDLINE (PubMed), Scopus, Embase, Web of Science, and Cochrane were included. Studies reporting on medical education methods in the curricular components (content, teaching, learning, educational environment, educational strategies, assessment, and evaluation) of medical education, and their effects on reactions/attitudes, knowledge, skills, behaviours, practices, and patient outcomes in the Indian context, were included. Screening and data extraction were conducted by two independent reviewers, with conflicts adjudicated by a third reviewer. EGM was developed using Eppi-Reviewer and Eppi-Mapper. Database search yielded 4342 unique studies, among which 607 studies were finally included in the review.
Results: The majority of studies were conducted on MBBS students (n=534, 88%), while 59 (9.7%) were conducted among MD-MS-DNB students. Teaching-Learning methods were the common curricular components studied (n=548, 90.3%). Physiology (n=118, 19.4%) followed by pharmacology (n=74, 12.2%) were the most common subjects on which studies were conducted. Most studies assessed Kirkpatrick’s Level II (n=570, 93.9%), followed by Level 1 (n=545, 89.8%). The majority of the studies were unfunded (n=589, 97%). Positive educational outcomes were reported for the intervention under evaluation in 83.0% (n=504) of the included studies.
Conclusion: In India, gaps exist in medical education research regarding study populations (postgraduates), curriculum components (educational environment, assessment), and levels of outcomes (levels 3 and 4).

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, 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 rotation
scheduling 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 with
established 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.

Abstract 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.

Abstract Introduction: Sustainable financing of medical education is critical to health workforce development and health system performance. While existing research has largely examined
financing mechanisms and expenditure patterns, less attention has been paid to the policy processes through which financing issues reach the governmental agenda. This study aimed to analyze the agenda-setting process for sustainable medical education financing using Kingdon’s Multiple Streams Framework and Walt and Gilson’s Policy Triangle.
Methods: We conducted a mixed-methods policy analysis, including a scoping review, 15 semi-structured interviews with key stakeholders, and a four-round Delphi process with the same
expert panel. Data were analyzed using framework analysis and interpreted through the two theoretical frameworks.
Results: Economic pressures -including high inflation, international sanctions, declining public investment, and the very low share of education in GDP and health expenditure- dominated the problem stream (mean importance score=4.89/5, expert agreement=97.8%). Demographic shifts, technological demands, and social equity expectations further intensified these pressures. The political stream was characterized by tensions between commitments to free education and limited fiscal capacity, shaped by ideological orientations and external constraints. Although multiple financing alternatives circulated within policy networks, the policy stream remained fragmented, with limited institutionalization of diverse models. Convergence of the three
streams indicated the emergence of a policy window, contingent upon effective policy entrepreneurship.
Conclusion: Effective agenda-setting requires alignment of economic realities, political commitments, and viable policy options. The findings offer transferable insights for other low- and
middle-income countries facing similar structural constraints.

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 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.

Abstract 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 organizational
culture that prioritizes treatment over education. Additionally, causal conditions such as multiple professional pressures and generational gaps further influenced the pace and direction of this
transformation. Overall, the findings depict expertise development as a gradual, evolving process through which faculty members reconcile their clinical and educational roles to become effective
and 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.

Abstract Introduction: Faculty and professional development are intrinsically linked to organizational effectiveness and academic excellence. Understanding barriers to faculty development can help improve the institution. This study aimed to investigate some factors affecting the professional development of faculty members in the Faculty of Medicine, Shiraz University of Medical Sciences.
Methods: In this study, the qualitative content analysis method was used. 17 faculty members were recruited through purposive sampling and participated in semi-structured, in-depth interviews. Data collection continued until saturation was reached. The interviews were first recorded on tape, transcribed word for word, and later analyzed by applying Graneheim and Lundman’s content analysis.
Results: Data analysis resulted in two major categories: organizational and cultural barriers. The organizational barriers consisted of lower salaries, a high workload, a non-supportive organizational system, lack of support for going abroad, inadequate incentives, inadequate access to resources, suppression of motivation, rigid promotion regulations, and poor infrastructure. Professional jealousy, preference for routine work, individual activities, and commercialization of medicine were the cultural barriers.
Conclusion: By recognizing the challenges that faculty members are facing in achieving success or development within the educational system, barriers can be overcome. Improving faculty development means the faculty is improved and contributes to student, organisation, and society development.

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 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 classroom
participation 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.

Abstract Nowadays, innovations in the educational structure of academic institutions are rapidly growing. The speed of this process is shaping the future of universities.