JMIR Medical Education最新文献

{"title":"Empowering Educators to Build AI Chatbots in Health Professions Education: Tutorial on a No-Code Design Workflow.","authors":"Charlene Xing Le Goh, Eugene Wang, Juanita S M Kong, Judy C G Sng","doi":"10.2196/103399","DOIUrl":"10.2196/103399","url":null,"abstract":"<p><strong>Unlabelled: </strong>AI is entering clinical practice faster than health professions curricula can teach it, leaving many educators eager to use AI-based teaching tools but unsure of how to build them. Generative AI chatbots-configured as simulated patients, clinical coaches, or formative assessment partners-offer scalable, interactive practice without any programming, yet most educators lack a structured method for designing and deploying them well. This tutorial provides that method: a practical, platform-agnostic workflow for building no-code AI chatbots using widely available large language model platforms. The workflow is organized in five sequential sections that follow the arc of a design project: (1) defining the educational purpose, learner group, and persona; (2) configuring behavior through the system prompt, graduated information disclosure, and structured feedback; (3) refining the learner experience through communication-style calibration, voice interaction, and curated knowledge documents; (4) adding realism and testing, including AI avatar generation and rigorous pilot-testing; and (5) embedding the tool within the curriculum and governing its use ethically. Each section pairs concrete, copy-ready design steps with the reasoning behind them, drawing on the technological pedagogical content knowledge framework and on established learning mechanisms-deliberate practice, self-regulated learning, formative feedback, and simulation-based learning-so that design choices are pedagogically grounded rather than merely technical. Throughout, 2 locally developed initiatives, the Virtual Integrated Patient and the Depression Avatars project, serve as illustrative implementation examples that motivated specific design decisions. These are presented as feasibility and acceptability experiences, not as evidence of educational effectiveness. The tutorial also addresses when a chatbot is not the right tool, the principal risks (hallucination, automation bias, data privacy exposure, and bias in generated personas), and a practical governance checklist for safe deployment. Although the examples are clinical, the workflow is discipline-agnostic and transferable across higher education. No-code AI chatbots are a feasible, accessible way for educators to build interactive learning tools; rigorous, multi-institutional evaluation using validated instruments remains the essential next step.</p>","PeriodicalId":36236,"journal":{"name":"JMIR Medical Education","volume":"12 ","pages":"e103399"},"PeriodicalIF":13.9,"publicationDate":"2026-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13476007/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148761707","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The AI Colleague: Reimagining Interprofessional Collaboration and Education in Medicine.","authors":"Chisung Yuh, Chul-Hyun Cho, Young-Mee Lee","doi":"10.2196/94034","DOIUrl":"10.2196/94034","url":null,"abstract":"<p><strong>Unlabelled: </strong>AI is no longer confined to optional decision support; it is becoming a routine presence in clinical workflows, shaping diagnostic hypotheses, triage priorities, risk estimates, and documentation. Yet most educational responses still treat AI as a tool operated by an individual clinician. This framing underestimates how AI reshapes the actual unit of practice: the interprofessional team. We propose the concept of AI-expanded interprofessional collaboration (AI-IPC), in which AI systems function as consequential participants in team cognition-not as moral agents, but as sources of recommendations, uncertainty, and constraints that reorganize communication, authority gradients, and accountability. Building on interprofessional education (IPE) theory, situated learning, and distributed cognition, we argue that \"AI literacy\" alone is insufficient; learners must be trained to coordinate human-AI-human collaboration in realistic clinical settings. We outline a pragmatic, theory-aligned approach for AI-expanded interprofessional education (AI-IPE): clarifying boundary conditions for AI participation, mapping AI-specific subcompetencies onto established IPE frameworks, and evaluating performance at the level of team behaviors rather than knowledge recall. We present the Interprofessional Education Collaborative (IPEC)-aligned evaluation scaffold with concrete learning activities and assessment approaches, and we outline how the approach can be tailored across undergraduate, postgraduate, and continuing education settings. We further emphasize that implementation requires digital infrastructure, institutional governance, and educator capacity that bridges AI, clinical workflow design, and IPE facilitation. Finally, we address the deliberate use of the \"AI colleague\" metaphor-not to anthropomorphize AI, but to make the relational and coordinative demands of AI integration visible and teachable. The pedagogical aim is calibrated, critical engagement: teams that verify, question, and, when warranted, override AI contributions rather than defer to them. AI will not replace interprofessional collaboration; it will change what collaboration requires. Education should make that change explicit, rehearsable, and assessable.</p>","PeriodicalId":36236,"journal":{"name":"JMIR Medical Education","volume":"12 ","pages":"e94034"},"PeriodicalIF":13.9,"publicationDate":"2026-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13478053/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148761379","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Effects of an Immersive 180° Video-Based Virtual Reality Instructional Module on Learning Satisfaction, Technology Acceptance, Motivation, and Quiz-Based Learning Achievement in Undergraduate Physical Therapy Students: Randomized Controlled Trial.","authors":"Hyoshil Yoon, Yongwoo Lee","doi":"10.2196/94122","DOIUrl":"10.2196/94122","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Virtual reality (VR) offers immersive learning opportunities in health professions education, yet its effects on learner experience and short-term learning outcomes in education on execution of musculoskeletal special tests remain underexplored.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This randomized controlled trial evaluated the effects of an immersive 180° video-based VR instruction, compared with conventional 2D video-based instruction, on undergraduate physical therapy students' learning satisfaction, technology acceptance, learning motivation, and quiz-based learning achievement, reflecting short-term procedural knowledge.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;Undergraduate physical therapy students were randomly assigned to either the immersive 180° video-based VR instruction group (VR group) or the conventional 2D video-based instruction group (control group). Both groups received identical instructional content on 4 musculoskeletal special tests (pronator teres, Hawkins-Kennedy, Yergason, and Neer tests). The VR group participated in an immersive 180° video-based instructional module delivered via a head-mounted display, whereas the control group viewed the same content on a standard monitor. Participants completed 4 instructional sessions within a single day, each lasting approximately 3 to 4 minutes. The primary outcome was learning satisfaction. Secondary outcomes were technology acceptance, learning motivation, and quiz-based learning achievement. These outcomes were assessed before and after the intervention using validated questionnaires and a 12-item quiz. Between-group posttest differences were primarily analyzed using analysis of covariance (ANCOVA) with pretest scores as covariates. Supplementary within-group and unadjusted between-group analyses were also performed.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Fifty-two students were randomly assigned to receive either immersive 180° video-based VR instruction (VR group; n=28) or conventional 2D video-based instruction (control group; n=24) on 4 musculoskeletal special tests, with all 4 instructional sessions completed within a single day. After adjustment for baseline scores, the VR group showed significantly higher learning satisfaction (adjusted mean 107.04 vs 93.28; F1,49=13.710; P=.001; ηp2=0.219) and technology acceptance (adjusted mean 74.44 vs 68.03; F1,49=4.402; P=.04; ηp2=0.082) than the control group. Adjusted between-group differences were not significant for quiz-based learning achievement (F1,49=1.175; P=.28). For learning motivation, the homogeneity of regression slopes assumption was violated; therefore, the adjusted estimate was interpreted descriptively and did not indicate a clear between-group advantage (F1,48=1.729; P=.20).&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;Compared with conventional 2D video-based instruction, immersive 180° video-based VR instruction showed a clear advantage in learning satisfaction, while technology acceptance also favored the ","PeriodicalId":36236,"journal":{"name":"JMIR Medical Education","volume":"12 ","pages":"e94122"},"PeriodicalIF":13.9,"publicationDate":"2026-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13476000/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148761340","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AI for Clinical Competency Assessment: Scoping Review of Methods and Applications.","authors":"Wenjia Stella Zhang, Benjamin B Daniels, Carol Mita, Hoang Nguyen, David B Duong","doi":"10.2196/92826","DOIUrl":"10.2196/92826","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Strengthening the global health workforce is central to achieving universal health coverage, but health systems cannot improve what they cannot measure. Valid and scalable assessment of clinical competency is essential for monitoring workforce readiness and ensuring that expanded service coverage translates into high-quality care. Traditional standardized patients, however, remain resource-intensive, difficult to scale, and vulnerable to evaluator-related bias. Recent advances in AI have enabled AI-led simulated standardized patients (SSPs) that may address these limitations.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;Although existing reviews have examined AI-SSPs broadly within medical education, their use as clinical competency assessment systems remains less well characterized. This scoping review aimed to address this gap by systematically mapping the scope, design features, and validation approaches of AI-SSP tools for clinical competency assessment.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;We registered the protocol prospectively with the Open Science Framework and conducted a scoping review following the Joanna Briggs Institute Manual for Evidence Synthesis. We searched MEDLINE, Embase, CINAHL Complete, Education Source, Web of Science Core Collection, Inspec, ERIC, and Cochrane CENTRAL, supplemented by manual searching. Eligible primary studies involved an AI-based SSP interaction that yielded a quantitative score of respondents' clinical competency. Two reviewers independently screened records and resolved conflicts through discussion. Data were charted on study characteristics and populations, frontend platform and interface features, backend AI architectures, performance scoring mechanisms, and tool evaluation methods and outcomes.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Of 5987 database records and 4 identified through other methods, 21 studies describing 20 unique AI-SSP systems were included. Studies were published between 2008 and 2026, with 15 (71%) published in 2024 or later. Most studies were conducted in high-income settings and involved medical students. Systems shifted over time from rule-based virtual patient to large language model-enabled conversational platforms after 2022. History-taking was the most frequently assessed competency, and checklist coverage was the most common scoring approach. Among studies comparing AI-generated scores with human ratings, findings were mixed: some reported moderate to strong agreement, while others showed score inflation and inconsistent performance. Most studies focused on feasibility; fewer evaluated large-scale use or sustained real-world implementation.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;This review is innovative in shifting attention from AI-SSPs as educational simulations to AI-SSPs as emerging infrastructures for clinical competency assessment. Unlike prior reviews of virtual patients or AI in medical education, this review maps not only system design and learner interactio","PeriodicalId":36236,"journal":{"name":"JMIR Medical Education","volume":"12 ","pages":"e92826"},"PeriodicalIF":13.9,"publicationDate":"2026-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13478279/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148759557","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AI Integration in Undergraduate Medical Education: Qualitative Study of Faculty Perspectives in the United Arab Emirates.","authors":"Azhar T Rahma, Uffaira Hafeez, Munawar Farooq","doi":"10.2196/90192","DOIUrl":"10.2196/90192","url":null,"abstract":"<p><strong>Background: </strong>AI is transforming health care, creating an imperative to integrate AI into medical education. While student perspectives are well-studied, faculty views, particularly in non-Western contexts, remain underexplored.</p><p><strong>Objective: </strong>This qualitative study explores the perspectives of 10 medical faculty members from 2 institutions in the United Arab Emirates on integrating AI into undergraduate medical education.</p><p><strong>Methods: </strong>This multi-institutional qualitative study used purposive and reflexive sampling to recruit faculty from both public and private medical universities in the United Arab Emirates. Semistructured interviews were conducted with 10 faculty members involved in curriculum design, teaching, or assessment. Data collection followed COREQ (Consolidated Criteria for Reporting Qualitative Research) guidelines. Data were analyzed using a mixed inductive-deductive approach guided by the thematic analysis framework of Braun and Clarke. Findings were interpreted using the FACETS (Form, AI Use Case, Context, Education, Technology, and SAMR: Substitution, Augmentation, Modification, Redefinition) framework, which supported a structured examination of AI integration across different dimensions of teaching and learning.</p><p><strong>Results: </strong>Faculty primarily used generative AI tools, such as ChatGPT, for content creation, assessment development, and teaching support, reflecting a preference for accessible and general-purpose technologies. AI was mainly used to enhance teaching efficiency and support student learning, including personalized study planning and practice activities. Its application extended across preclinical and clinical contexts, with strong emphasis on adapting content to local cultural and ethical norms. While AI was perceived to improve efficiency and alignment between teaching and assessment, concerns were raised regarding equity, overreliance, and variability in student use. Overall, adoption remained focused on enhancing existing practices, with limited transformative use but recognition of future potential for more advanced applications.</p><p><strong>Conclusions: </strong>The UAE medical faculty demonstrate cautious optimism toward AI integration, recognizing its potential to enhance educational efficiency and personalization while emphasizing the critical importance of cultural contextualization. Current implementation remains at early adoption stages, focused on enhancement rather than transformation. Successful integration requires faculty development, context-sensitive policies, and equitable implementation strategies that address both technological and sociocultural dimensions of AI adoption in medical education.</p>","PeriodicalId":36236,"journal":{"name":"JMIR Medical Education","volume":"12 ","pages":"e90192"},"PeriodicalIF":13.9,"publicationDate":"2026-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13472525/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148761729","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integrating Generative AI Into Clinical Reasoning Education for Medical Students: Mixed Methods Study.","authors":"Cheng-Heng Liu, Yu-Ting Chen, Chiun Hsu, Huey-Ling Chen, Chih-Wei Yang","doi":"10.2196/94951","DOIUrl":"10.2196/94951","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;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.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;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.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;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.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;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&lt;.001; all remaining significant after false discovery rate correction), and collaborative learning attitudes increased substantially (d=0.94, P&lt;.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 r","PeriodicalId":36236,"journal":{"name":"JMIR Medical Education","volume":"12 ","pages":"e94951"},"PeriodicalIF":13.9,"publicationDate":"2026-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13472228/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148762006","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Augmented Reality-Based Training for a Rapid Blood Transfusion Device Among Emergency Nurses: Randomized Controlled Trial.","authors":"Wooyoung Jang, Wooree Chai, Yeji Kim, Youjung Moon, Suyoung Yoo, Sun Gyoung Na, Soyeon Shin, Meong Hi Son","doi":"10.2196/98073","DOIUrl":"10.2196/98073","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Emergency nurses must be proficient in operating the Level-1 rapid infusion system to manage hypovolemic shock effectively. However, training opportunities for this infrequently used but life-critical device remain scarce, owing to resource constraints and limited access to equipment. Augmented reality (AR) has emerged as a promising educational technology that provides immersive, hands-on learning experiences without compromising patient safety; yet its application to specialized medical device training in nursing has not been rigorously evaluated.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study aimed to evaluate the effects of an AR-based training program using Microsoft HoloLens 2 on emergency nurses' clinical competency, self-efficacy, and educational satisfaction in operating the Level-1 rapid infusion system, compared with traditional guideline-based self-directed learning.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;A posttest-only randomized controlled trial was conducted at Samsung Medical Center in Seoul, Republic of Korea. Between July 17 and July 20, 2023, 42 registered nurses with no prior Level-1 experience were enrolled and randomly assigned in a 1:1 ratio to an experimental group receiving AR-based training on a single HoloLens 2 device (n=21) or a control group performing self-directed learning from a printed manual (n=21). Clinical competency was assessed by time (learning and performance), accuracy (a manufacturer-aligned checklist scored out of 100, and an expert-validated 22-step pass or fail evaluation), and the number of assistance requests. Self-efficacy (6-item scale; Cronbach α=0.80) and educational satisfaction (4-item scale; Cronbach α=0.87) were measured by questionnaire. Because most outcomes were non-normally distributed, groups were compared using the Mann-Whitney U test, with data reported as medians and IQRs.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Learning time was longer in the experimental group (median 18.20, IQR 15.48-21.67 vs 8.98, IQR 5.85-11.68 min; P&lt;.001), but device setup time was markedly shorter (3.67, IQR 2.90-4.63 vs 9.85, IQR 8.03-11.35 min; P&lt;.001). The experimental group achieved higher median device operation competency scores (90.00, IQR 80.00-100.00 vs 70.00, IQR 50.00-75.00 of 100; P&lt;.001), passed more of the 22 evaluation steps (20.00, IQR 20.00-22.00 vs 16.00, IQR 13.00-18.00; P&lt;.001), and required fewer assistance requests (0.00, IQR 0.00-1.00 vs 2.00, IQR 2.00-3.00; P&lt;.001). Self-efficacy (20.00, IQR 18.00-25.00 vs 16.50, IQR 13.00-20.00; P=.003) and educational satisfaction (18.00, IQR 16.00-18.00 vs 12.00, IQR 9.75-15.00; P&lt;.001) were also significantly higher in the experimental group. Effect sizes for the principal competency outcomes were large (Cohen d=1.2-3.1).&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;AR-based training significantly improved emergency nurses' clinical competency, self-efficacy, and educational satisfaction in operating the Level-1 rapid infusion system compare","PeriodicalId":36236,"journal":{"name":"JMIR Medical Education","volume":"12 ","pages":"e98073"},"PeriodicalIF":13.9,"publicationDate":"2026-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13472624/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148761930","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Human-Edited Generative AI-Assisted Multiple-Choice Questions in Postgraduate Family Medicine: Blinded Cross-Sectional Comparative Psychometric Study.","authors":"Kai Ping Sze, Jia Qing Lim, Yng Miin Loke, Teck Yong Gabriel Ding, Wei Chieh Wee, Tongyuan Chen, Jingkai Lawrence Wu, Vivek Bansal, Qi Wei Fong, Minyang Chow","doi":"10.2196/100179","DOIUrl":"10.2196/100179","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Generative artificial intelligence (GenAI) is increasingly used to draft multiple-choice questions (MCQs) for health professions education, but much evidence concerns raw model outputs, expert ratings, or item difficulty alone. Educators edit GenAI drafts before use, and whether such items are psychometrically ready for postgraduate assessment remains unclear.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study aimed to compare human-edited GenAI-assisted and educator-crafted MCQs for postgraduate Family Medicine Applied Knowledge Test-level assessment, examining difficulty, discrimination, reliability, distractor functioning, and participant perceptions.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;We conducted a blinded cross-sectional, within-participant comparative psychometric evaluation in Singapore. Sixty best-of-five single-best-answer MCQs were evaluated, 30 human-edited GenAI-assisted items and 30 educator-crafted items, topic-matched across postgraduate FM domains and randomized across 2 assessment sets. Eligible participants were postgraduate doctors enrolled in FM residency or postgraduate family medicine programs, preparing for the Applied Knowledge Test, and blinded to item origin; incomplete paired responses were excluded. Outcomes included paired total scores, score correlation and agreement, Kuder-Richardson Formula 20 reliability, item difficulty index, corrected point-biserial discrimination, distractor functioning, and perceived difficulty, clarity, and relevance. Analyses used paired-sample tests, Pearson correlation, Fisher exact tests, and item-level psychometric statistics, with α=.05 and Bonferroni correction within comparison families.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Of 74 participants, 73 completed both item sets and were included in the analysis. The final sample comprised 36 graduate diploma in FM trainees, 5 MMed FM trainees, and 32 FM residents. Paired-sample testing showed lower scores on GenAI-assisted than educator-crafted items (mean 19.12, SD 2.83 vs mean 21.10, SD 3.42 out of 30; mean difference -1.97, 95% CI -2.72 to -1.23; P&lt;.001; Cohen d=0.62), indicating that GenAI-assisted items were not easier. Scores were positively correlated (r=0.49, 95% CI 0.30-0.64; P&lt;.001), but Bland-Altman analysis indicated limited agreement. Kuder-Richardson Formula 20 reliability was lower for GenAI-assisted items (0.38 vs 0.60). Mean difficulty index did not differ significantly (0.64 vs 0.70; mean difference -0.07, 95% CI -0.19 to 0.06; P=.29), and more GenAI-assisted items fell within the acceptable difficulty range (18/30, 60.0% vs 13/30, 43.3%). However, mean corrected point-biserial discrimination was lower for GenAI-assisted items (0.09 vs 0.18; mean difference -0.08, 95% CI -0.16 to -0.01; P=.04), and negative discrimination was more common (6/30, 20% vs 3/30, 10%). GenAI-assisted items also had more nonfunctioning and negatively discriminating distractors, although these differences were not statistically ","PeriodicalId":36236,"journal":{"name":"JMIR Medical Education","volume":"12 ","pages":"e100179"},"PeriodicalIF":13.9,"publicationDate":"2026-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13456530/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148707727","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Prompt Framing and Evidence Requirement for AI-Generated Educational Responses in Dental Education: Experimental Study.","authors":"Man Hung, Corban Ward, Owen Cohen, Jacob Marx, Zachary Smit, Jacob Newman, Martin S Lipsky","doi":"10.2196/90736","DOIUrl":"10.2196/90736","url":null,"abstract":"<p><strong>Background: </strong>Large language models are increasingly used in health professions education; however, the role of prompt design in shaping their outputs remains poorly understood in clinical training contexts. In dentistry, where information presentation, perceived credibility, and procedural reasoning are important, the effects of instructional framing and evidence requirements on AI-generated educational responses are particularly relevant.</p><p><strong>Objective: </strong>This study examined whether instructional framing and evidence requirements were associated with rater-assessed perceived factuality, tone, stance orientation, citation behavior, safety notices, hedging, and response length in responses about cavity preparation generated using GPT-5 through its web-based interface.</p><p><strong>Methods: </strong>In a 2×2 factorial experiment, we manipulated instructional framing (patient-centered vs skill-centered) and evidence requirement (evidence-required vs no evidence) across 10 base prompt topics and 4 experimental conditions, yielding 40 outputs. Five trained raters independently coded each response for perceived factuality, confidence tone, stance orientation, hedging, citation presence, and safety notices. Response length was calculated programmatically. Interrater reliability was assessed using intraclass correlation coefficients and Fleiss κ. Consensus measures were analyzed using factorial analyses of variance and chi-square tests.</p><p><strong>Results: </strong>Evidence requirement was associated with greater citation presence (19/20, 95% vs 5/20, 25%; P<.001) and longer responses (P<.001). It was also associated with higher rater-assessed perceived factuality in exploratory analyses (P=.02). Hedging and confidence tone showed nonsignificant patterns. Instructional framing was associated with stance orientation (P=.006) but not with response length. No refusals occurred, and safety notices were infrequent across conditions. Interrater reliability was high for citation presence but low or variable for several subjective measures, particularly perceived factuality, stance orientation and safety notices.</p><p><strong>Conclusions: </strong>Prompt design was associated with differences in the presentation, structure, and orientation of large language model-generated educational responses in dentistry. Evidence requirements increased citation inclusion and response length, whereas instructional framing was associated with the stance emphasized in the response. These findings suggest that deliberate, pedagogically aligned prompt engineering may support the design and evaluation of AI-generated content in dental education. However, the effects of prompt wording on objectively verified accuracy, clinical safety, and learning outcomes require further investigation.</p>","PeriodicalId":36236,"journal":{"name":"JMIR Medical Education","volume":"12 ","pages":"e90736"},"PeriodicalIF":13.9,"publicationDate":"2026-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13446791/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148686010","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Use, Concerns, and Perspectives on AI in Health Care Among French Health Professionals and Students: Web-Based Cross-Sectional Survey.","authors":"Aurelia Alati, Grégoire Pigné, Carole-Anne Brugère, Jean-Emmanuel Bibault","doi":"10.2196/89152","DOIUrl":"10.2196/89152","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;AI is increasingly discussed and deployed in health care, yet safe and effective implementation depends on the preparedness, trust, and training of the professionals who are expected to use these tools.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study aimed to assess current AI use, perceived benefits and concerns, confidence, and training needs among French health care professionals and students.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;We conducted a national web-based cross-sectional survey distributed through the PulseLife professional community between December 4, 2024, and March 5, 2025. The survey instrument was administered in French and included respondent characteristic items together with 12 substantive closed-ended questions covering current AI use, confidence, perceived benefits, and concerns, and interest in AI-related training. Access was restricted to authenticated individual PulseLife accounts, and multiple submissions from the same account were not allowed. Questions were not mandatory; incomplete questionnaires were retained for item-level analyses, and percentages were calculated using item-specific denominators. Because the exact invitation denominator was not retained by the platform, view, participation, and completion rates could not be calculated. Descriptive statistics and Pearson chi-square tests were performed using R. Internal consistency and exploratory psychometric properties were assessed using the Cronbach α, exploratory factor analysis, and confirmatory factor analysis.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;A total of 1625 respondents participated, including 1212 (74.6%) health professionals and 413 (25.4%) students. Among professionals, physicians represented the largest group (642/1212, 53%), followed by nurses (232/1212, 19.1%) and pharmacists (92/1212, 7.6%). Only 6.6% (90/1366) of the respondents reported prior AI-specific training, whereas 78.3% (920/1175) wished to receive such training. Confidence in AI for diagnosis and patient management remained limited: only 9.2% (120/1301) of the respondents reported being very confident. Nearly half (673/1455, 46.3%) of the respondents who answered this item reported no current AI use in professional activity, whereas 10.5% (153/1455) reported frequent use. Physicians and younger respondents reported more frequent AI use, and prior AI training was associated with greater confidence (P&lt;.001 in all cases). Commonly perceived benefits included improved diagnosis (774/1625, 47.6%), time savings (685/1625, 42.2%), reduced medical errors (634/1625, 39%), and improved patient follow-up (593/1625, 36.5%). Frequently reported concerns included algorithmic bias (785/1625, 48.3%), limited transparency (666/1625, 41%), deterioration of the patient-health care professional relationship (628/1625, 38.6%), and data confidentiality (557/1625, 34.3%).&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;In this national French sample, formal AI training was uncommon despite high interes","PeriodicalId":36236,"journal":{"name":"JMIR Medical Education","volume":"12 ","pages":"e89152"},"PeriodicalIF":13.9,"publicationDate":"2026-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13441111/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148680272","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}