Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
Showing 15 appraisals
JMIR medical education
Evaluating the Pediatric Behavior Guidance of Students Based on Actual Clinical Transcripts Scored by Faculty and Large Language Models: Pilot Comparative Study
BACKGROUND: Personalized feedback improves the clinical pediatric behavior guidance performance of students but is prohibitively time-consuming to provide. Large language models (LLMs) can automate the process of evaluating clinical sessions but are limited to text-only input and consistency issues. OBJECTIVE: This study compared the use of text-only transcripts against the use of video recordings for evaluating the clinical behavior guidance performance of dental students. Additionally, the consistency and accuracy of LLMs in evaluating the transcripts were compared against a human assessor. METHODS: This study was conducted by using 40 video-recorded clinical encounters involving final-year dental students who were managing patients aged between 4 and 12 years at the Faculty of Dentistry, National University of Singapore. The videos were scored by using a previously validated pediatric behavior guidance scale. Clinical encounters were transcribed verbatim and scored by a study member using a modified version of the scale (nonverbal components removed). The time taken to rate the transcripts was recorded. Video scores were compared with transcript scores. Both the free-to-use version and the paid version of the ChatGPT LLM were also used to score the transcripts; consistency was evaluated and compared against the human assessor. RESULTS: The average time taken to rate the transcripts (mean 12, range 3-25 min) was significantly (P<.001) lower than the average video length (mean 73, range 37-120 min). Comparing transcript scores with video scores resulted in a consistency intraclass correlation coefficient of 0.830 (95% CI 0.679-0.910; P<.001), demonstrating good reliability. Comparing transcript scores with the free-to-use LLM's and paid LLM's scores yielded an absolute agreement intraclass correlation coefficient of 0.729 (95% CI 0.475-0.859; P<.001) and 0.670 (95% CI 0.377-0.825; P<.001), respectively, demonstrating moderate agreement. The LLMs were inconsistent, producing variable scores with the same prompt. The free-to-use and paid versions produced the same score for all 3 runs in only 7 (18%) and 4 (10%) of the 40 clinical encounters, respectively. CONCLUSIONS: Using transcripts to evaluate students' clinical behavior guidance was time-saving for faculty, demonstrated good agreement with video-based evaluation, and could improve clinical teaching. Although LLMs can automate the task, improvements are needed to improve their consistency and accuracy.
14 June 2026
Read appraisal →JMIR nursing
Assessing Digital Literacy Among Nursing Faculty Under the Artificial Intelligence-Enhanced Technological Pedagogical Content Knowledge Framework: Cross-Sectional Survey Analysis
BACKGROUND: Digital literacy has become a core competence for nursing faculty to adapt to the digital transformation of education, improve teaching quality, and cultivate innovative nursing talents. OBJECTIVE: This study aimed to investigate the digital literacy status and influencing factors among nursing faculty in colleges and universities in Fujian province, China. METHODS: A cross-sectional survey analysis was conducted among 339 participants from July 2023 to June 2024 by selecting nursing faculty from 3 medical universities and 5 colleges in Fujian province. A general information questionnaire and a teacher digital literacy questionnaire (designed based on the artificial intelligence-enhanced technological pedagogical content knowledge theoretical model) were used to conduct the survey. The questionnaires included the following dimensions: an intelligent integration ethics layer (digital social responsibility), attention to the moral boundaries of applying technology; an awareness layer (digital awareness), the willingness to actively adapt to technological changes; a knowledge layer (digital technology knowledge and skills), mastery of the integration point of intelligent tools and subject teaching; an ability layer (digital application), the ability to design intelligent teaching plans; and a thinking layer (professional development; innovative thinking for the critical integration of technology). Statistical analysis included descriptive statistics, a 1-way ANOVA, Pearson correlation analysis, and multiple linear regression (using SPSS 28.0 software). RESULTS: The overall digital literacy of the nursing faculty was at a medium level (mean score 101.92, SD 16.47), with the highest score in digital awareness (mean 20.21, SD 9.43) and the lowest score in digital technology knowledge and skills (mean 9.68, SD 2.92). For the dimension of digital technology knowledge and skills, both age (β=-.142; P=.009) and years of teaching (β=-.147; P=.006) were significant negative predictors. Regarding digital application, age was found to be a significant negative predictor (β=-0.124; P=.02) but teaching experience was a positive predictor (β=0.123, P=.02). Similarly, age also had a significant negative impact on the professional development dimension (β=-.153; P=.005) and the overall digital literacy level (β=-.136; P=.01). CONCLUSIONS: The digital literacy of nursing faculty is at a medium level, with especially low scores in technical knowledge. Age and teaching experience are key influencing factors. Recommendations to promote balanced development of awareness and skills include providing targeted training for senior faculty, integrating courses with clinical practice, optimizing the allocation of digital resources, promoting interdisciplinary cooperation, and strengthening ethical and safety training.
14 June 2026
Read appraisal →Medicine
A bibliometric analysis of artificial intelligence in anatomy education: Current situation, hot spots, and global trends
INTRODUCTION: Artificial intelligence (AI) is increasingly being integrated into anatomy education, yet a comprehensive overview of the field's development and emerging trends remains lacking. This study identifies research hotspots and trends in AI applications in anatomy education through a bibliometric analysis. METHODS: English-language publications on AI in anatomy education were systematically collected from the Web of Science database. Using bibliometric tools, including R, VOSviewer, CiteSpace, and Excel, data were synthesized to explore global research trends and focal areas. RESULTS: A total of 184 papers published between 2005 and 2024 were included. The United States emerged as the leading contributor in terms of publication output and citation impact. Over the past 2 decades, research focus has shifted from general medical education applications toward AI-driven image segmentation, deep learning, and surgical guidance. More recently, large language models (e.g., ChatGPT) have begun to attract attention in anatomy education. CONCLUSION: This bibliometric analysis maps the intellectual landscape of AI in anatomy education, identifying key thematic shifts and emerging frontiers. The findings provide a foundation for guiding future research directions in this rapidly evolving field.
31 May 2026
Read appraisal →JMIR medical education
Exploring the Feasibility of an Examiner-Worn Neck-Mounted Camera for Objective Structured Clinical Examination Assessment: Pilot Feasibility Study
BACKGROUND: The Objective Structured Clinical Examination (OSCE) is a prevalent method for evaluating clinical competence in medical education. As OSCEs become increasingly standardized and resource intensive, alternative evaluation methods are being explored, particularly because of the limited availability of certified examiners. However, few studies have investigated whether wearable technologies can support OSCE assessment. Wearable devices may provide a means of recording clinical skills from the examiner's perspective. OBJECTIVE: This pilot study, conducted in 2024, aimed to investigate the feasibility of using an examiner-worn neck-mounted camera for recording OSCE scenarios and to evaluate the evaluability of clinical performance from the recorded footage. METHODS: In total, 9 experienced medical educators participated in a simulated OSCE scenario involving electrocardiogram lead placement. All participants completed the initial live assessment and the postuse questionnaire, while 8 of 9 (89%) participants completed the subsequent video-based reassessment. Video recordings from both a fixed camera and a neck-mounted camera (THINKLET) were used to assess the evaluability of each OSCE item. Following a washout period, evaluators reassessed the neck-mounted camera recordings by using the original checklist, while fixed-camera recordings were used to judge the evaluability of each item. Agreement between live and video-based scoring was analyzed using percent agreement and the Cohen κ coefficient. A postevaluation questionnaire captured evaluators' experiences with the wearable device. RESULTS: Cohen κ ranged from 0.258 to 0.913 (mean 0.67, SD 0.20). Across checklist observations, more items were judged to be evaluable in the neck-mounted camera recordings than in the fixed-camera recordings, particularly for tasks requiring observation of fine motor skills. Evaluators reported generally positive experiences with the device, although some noted issues related to audio quality, comfort, posture restriction, and limited visibility at low angles. CONCLUSIONS: Although further investigation is needed, this pilot study suggests that an examiner-worn neck-mounted camera may be a valuable supplementary assessment tool for selected OSCE tasks. Further work is needed to refine the device, standardize recording protocols, and clarify how it can best support review and verification alongside live evaluation.
29 May 2026
Read appraisal →JMIR formative research
Virtual Reality-Based Training in Radiologic Technology for Contrast-Enhanced Computed Tomography Brain Imaging: Randomized Controlled Trial
BACKGROUND: Radiologic technology (RT) education faces challenges in bridging theory and practice due to limited clinical opportunities. While virtual reality (VR) enables safe and repeatable practice, a systematic instructional design framework is needed to develop scalable, procedure-focused modules. OBJECTIVE: This study evaluates the Radiologic Technology Virtual Reality (RTVR) framework that integrates 360-degree video capture, instructional overlays, interactive assets, and an immersive content authoring platform to deliver a contrast-enhanced computed tomography (CECT) brain scan module. METHODS: In this open-label, parallel-group, randomized controlled trial, 36 year-2 and year-3 RT students with no prior clinical training in diagnostic radiology at a university hospital in Thailand were randomly allocated (1:1) to a VR group or a conventional document-based instruction (control) group. The VR group completed the VR module, a grounded instructional design framework using 360-degree videos and a structured prebrief and debrief, for 20 minutes using a head-mounted display. The control group studied standard curriculum materials for the same duration. Blinding of participants was not possible. Outcome assessment was blinded. The primary outcome was declarative knowledge gain, assessed using a 20-item multiple-choice test before and after intervention. Secondary outcomes included technology acceptance, student satisfaction, and physiological responses during VR immersion. RESULTS: All 36 randomized participants (VR: n=18, control: n=18) completed the study and were included in the analysis. Experts validated the module as suitable and highly appropriate. Students reported high technology acceptance and satisfaction. Both VR and conventional methods produced substantial gains in declarative knowledge. No statistically significant difference in knowledge gain was detected between groups (test × group: unstandardized regression coefficient β=.056, 95% CI -1.360 to 1.473, P=.94). Year-2 students, who had less prior clinical exposure, showed larger pretest to posttest knowledge gains compared to year-3 students. Physiological monitoring showed a reduction in heart rate across the session, while blood pressure remained stable. No adverse events or VR-related discomfort requiring discontinuation was observed. CONCLUSIONS: The RTVR framework, which uses a real 360-degree video of authentic clinical settings, offers a scalable approach to procedural VR content creation without requiring specialist technical skills, distinguishing it from prior VR studies in radiography. These findings support the RTVR framework as a feasible, evidence-informed supplement to RT curricula for knowledge-focused procedural teaching, with learning outcomes comparable to those of conventional instruction in this context.
29 May 2026
Read appraisal →JMIR formative research
Metaverse-Based Virtual Reality for Remote Anatomy Education: Pilot Randomized Controlled Trial
BACKGROUND: Traditional anatomy teaching relies on cadaveric dissection and 2D resources, which often require in-person attendance and may limit spatial understanding. Virtual reality (VR) provides an immersive, remote alternative that supports 3D visualization from home. Recent evidence suggests that while VR may yield comparable factual knowledge gains to 2D methods, its primary value lies in enhancing learner engagement, motivation, and perceived educational value. OBJECTIVE: This pilot randomized controlled trial compares remote synchronized VR with didactic animated anatomy lectures for the teaching of tracheostomy anatomy. METHODS: Participants were recruited via convenience sampling through the VRiMS (Virtual Reality in Medicine and Surgery) Surgical Society network. All participants first attended a synchronous 20-minute online lecture delivered by a consultant surgeon. They were then individually randomized to one of 2 groups using a computer-generated sequence. Allocation was concealed until the intervention; however, participants and researchers were unblinded. The intervention group completed a 10-minute metaverse-based VR session, delivered by a consultant surgeon via 3D Organon's Medverse platform on the PICO 4 Ultra headset. The control group completed a 10-minute prerecorded 2D animated lecture, accessed on their personal device. Participants then swapped to the other modality. Data were collected via Google Forms at 3 intervals (baseline, postintervention, and postcrossover) to assess confidence, spatial understanding, and knowledge (10-item multiple-choice questions). The analysis of nonparametric data utilized Wilcoxon signed-rank tests for within-group changes and Mann-Whitney U tests for between-group differences. RESULTS: Twenty-four medical students from 11 United Kingdom and Irish medical schools participated. Adherence was 100%, with all participants completing their assigned 10-minute intervention and all assessment points. Ninety-two percent of participants (n=22) reported no prior tracheostomy anatomy teaching. Additionally, 83% (n=20) had no prior remote-synchronized VR anatomy teaching experience. Anatomical confidence improved significantly in the VR group compared with animation (mean change 1.58, SD 1.00 vs mean 0.50, SD 0.80, P=.01). Knowledge scores improved significantly in both groups (VR: mean 1.75, SD 1.54, P=.007; animation: mean 2.83, SD 1.70, P=.003), with no significant postintervention difference between groups (P=.46). VR participants reported significantly superior spatial understanding across all measured domains (all P≤.009). These included depth perception (3.75 vs 2.58, P=.009), appreciation of anatomy from different viewpoints (4.25 vs 2.33, P=.001), mental reconstruction from varying angles (3.83 vs 2.08, P=.002), and spatial depth supporting anatomical understanding (4.08 vs 2.08, P=.001). Following the completion of both modalities, participants rated VR as more engaging (mean 4.54, SD 0.78) and more educationally effective (mean 4.29, SD 0.95) than animation. CONCLUSIONS: Remote VR teaching is feasible and engaging and enhances spatial understanding compared to animation. While knowledge gains were comparable between modalities, VR improved learner confidence and perceived 3D comprehension. Hence, VR may represent a scalable adjunct or alternative to traditional anatomy teaching.
20 May 2026
Read appraisal →JMIR formative research
Feasibility of Large Language Model-Based Standardized Virtual Patients to Support Clinical Decision-Making Training in Operative Dentistry: Mixed Methods Study
BACKGROUND: Clinical decision-making training in operative dentistry commonly relies on real or standardized patients to develop undergraduate students' ability to deliver safe, effective, and patient-centered care. However, training with real or standardized patients can be limited in scalability, cost-effectiveness, and accessibility. Large language models, with their human-like language capabilities, may have the potential to simulate patients in clinical encounters and help overcome some limitations associated with traditional training approaches. OBJECTIVE: This study aimed to evaluate the feasibility of using large language model-based standardized virtual patients to support undergraduate dental students' clinical decision-making training in operative dentistry. METHODS: This mixed methods cross-sectional feasibility study was conducted during a simulation-based clinical decision-making training session in the Operative Dentistry and Cariology course at the College of Dentistry, King Saud bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia. Eligible participants were second-year undergraduate dental students enrolled in the course. A convenience sampling approach was used, with all eligible students (N=50) invited to participate. A total of 41 students completed the study, 23 (56%) of whom were male. The students were divided into 8 groups. Each group interacted with 2 standardized virtual patients powered by ChatGPT-4o (OpenAI) through the Chatbase platform to complete comprehensive history-taking and then reviewed the standardized virtual patients' intraoral photographs and bitewing radiographs. For each standardized virtual patient, students as a group recorded diagnoses, performed a risk assessment, and formulated a treatment plan. Students then completed the Student Satisfaction and Self-Confidence in Learning questionnaire. The quality of the standardized virtual patient responses and overall dialogue realism were evaluated using the Dialogue Authenticity Scale. The dialogues were also thematically analyzed to identify authenticity-undermining response features and explore their context and underlying causes. RESULTS: Students perceived the simulation-based training session positively, with all questionnaire items showing high median scores (4.00-5.00 on a 5-point scale), and both item-level IQRs and 95% CIs spanning no more than 1.0 scale point. In addition, standardized virtual patient responses were largely authentic, with an overall median authenticity rating of 4.50 (IQR 4.00-5.00; 95% CI 4.00-5.00) on a 6-point scale across all interactions. However, several authenticity-undermining response features were identified, including responses that were inconsistent with typical human behavior, contained information beyond a patient's likely knowledge, or were factually incorrect. CONCLUSIONS: This proof-of-concept study supports the feasibility of implementing large language model-based standardized virtual patients in undergraduate simulation-based clinical decision-making training in operative dentistry. In a dental context where this application has been only minimally evaluated, this study provides early evidence of positive student perceptions, acceptability, and largely authentic dialogue, while also identifying important performance limitations. Further research is warranted to optimize performance and to evaluate the educational effectiveness of this approach in improving undergraduate students' clinical skills and knowledge.
20 May 2026
Read appraisal →JMIR medical education
Experiences and Perceptions of Clinical and Graduate Medical Students Regarding AI in Syria: Cross-Sectional Study
BACKGROUND: Artificial intelligence (AI) tools have revolutionized various aspects of education and health care in recent years. Their influence extends across multiple domains of medical education, from traditional learning to research and foreign language acquisition. OBJECTIVE: This study aims to evaluate the experiences and perceptions of AI tools usage in a low-resource setting and identify the factors influencing their adoption. METHODS: A cross-sectional study was conducted to evaluate the experiences with AI tools and perceptions regarding their future applications in education and health care among medical students in Syria. The sample was equally divided between clinical-year students and graduates. Chi-square tests analyzed differences based on demographics and experience, while Mann-Whitney U tests compared group perceptions of AI's future role. Factors studied included academic year, gender, German language learning, computer access, and research experience. RESULTS: Among 400 participants, AI tools were widely used for study preparation (228/400, 57% of participants), assignments (160/400, 40% of participants), and research. Clinical students used AI more than graduates for examination preparation (P<.001), creating cases (P=.03), and writing tasks (P<.001). Males used AI more for research (P=.004) or anatomy (P=.02); German learners relied on AI for language tasks. Despite 76% (304/400) of students believing AI would enhance residency training and 71.8% (287/400) of students supporting institutional policies, only 25.5% (102/400) of students expected career benefits. Ethical concerns were higher among females and researchers. CONCLUSIONS: This study highlights the increasing reliance on AI tools among medical students and graduates for academic and clinical purposes. The highest usage was reported in study preparation, writing tasks, and clinical simulations. Significant differences in AI usage were observed based on academic level, gender, access to technology, and research experience. While perceptions were largely positive, concerns remained around ethical use, potential job displacement, and diminished human interaction in medicine. These findings underscore the importance of developing institutional policies to guide the ethical and effective integration of AI in medical education.
20 May 2026
Read appraisal →Medical education online
Using randomization to compare AI and expert-generated formative assessment questions in medical education
BACKGROUND: AI-generated content is being used across the education spectrum and is beneficial for creating complex multiple-choice questions, such as those used in medical education. However, evaluating AI-generated content is challenging, and existing testing and evaluation methods are falling short. This study uses randomization to compare medical students' performance on and subjective evaluation of AI versus expert-generated questions. We hypothesized that there would be no difference in student performance or subjective evaluation of AI vs expert-generated questions. METHODS: We designed a single-center, randomized study where medical student participants received either one AI- or expert-generated question per day over four weeks. RESULTS: Our study showed that participants had similar perceptions of AI versus expert-generated questions (p = 0.18), with no significant difference between the distributions of the proportion of correct responses. The cumulative proportion of questions answered correctly over 28 days was consistent across the two question sets. However, participants rated 53% of AI-generated questions as very easy or easy compared with only 31% of the expert-generated questions. DISCUSSION: Randomization was crucial to show that AI-generated questions were nearly imperceptible from expert-generated questions, demonstrating the need for additional evaluation methods to compare AI- and expert-generated medical education content.
12 May 2026
Read appraisal →Journal of medical Internet research
GenAI-Supported Virtual Patients in Health Care Education: Systematic Review
BACKGROUND: Generative artificial intelligence (GenAI) is enhancing virtual patient simulations in health care education by enabling dynamic, adaptive interactions, reshaping how clinical skills are taught. A synthesis of the current evidence is needed to guide implementation and future research, given the pace of technological advancement. OBJECTIVE: This systematic review aims to synthesize empirical research on the design, implementation, and educational impact of GenAI-supported virtual patients in health care education. METHODS: A systematic search was conducted across 5 databases (CINAHL, Medline, Embase, Scopus, and Web of Science) from their inception to March 19, 2026. Reference lists of included studies and relevant systematic reviews were also screened. Peer-reviewed studies in English that evaluated GenAI-supported virtual patients using quantitative or mixed methods were included. Two reviewers independently screened studies and extracted data. Study quality and risk of bias were assessed critically using JBI (Joanna Briggs Institute) checklists, with disagreements resolved by consensus. RESULTS: A total of 15 studies met the inclusion criteria (total participants N=645), spanning health care disciplines, including nursing, medicine, pharmacy, radiography, and medical first-responder training. The virtual patients varied in design; input modalities included text (9 studies), voice (5 studies), or hybrid (1 study); output was text (9 studies), speech (5 studies), or both (1 study); 6 studies used 3D-embodied avatars, while 9 used nonembodied interfaces. A total of 13 studies used OpenAI GPT models (eg, ChatGPT), 1 used a fine-tuned model from a different provider, and 1 evaluated multiple model families (Claude, GPT, and open-source). Further, 6 studies used controlled experimental designs, including 3 randomized controlled trials (RCTs); the remainder were cross-sectional or prepost evaluations. Primary outcomes included user perceptions (14 studies), communication skills (4 studies), clinical reasoning (3 studies), and performance (7 studies). In controlled comparisons, GenAI-supported virtual patients consistently improved outcomes relative to control conditions: for example, enhanced clinical decision-making (RCT, n=21), ophthalmology history-taking skills (RCT, n=26), and medical history-taking performance (crossover RCT, n=20). The evidence base is characterized by brief intervention durations, a predominant reliance on single-session interactions, and a general lack of underpinning educational theory. No meta-analysis was performed due to the limited number of studies and significant heterogeneity in designs, interventions, and outcome measures. CONCLUSIONS: The evidence supports the feasibility and acceptability of GenAI-supported virtual patients, with positive learner perceptions and promising outcomes for skills development. However, critical limitations remain in emotional-behavioral complexity, simulation adaptability, and research design rigor (eg, limited use of control groups and validated instruments). The review offers educators, instructional designers, and policymakers actionable insights for integrating dynamic, artificial intelligence-driven simulations while identifying crucial gaps-such as the need for theoretical grounding, longitudinal studies, and standardized design protocols-that must be addressed for safe and effective implementation.
11 May 2026
Read appraisal →The Journal of nursing education
Enhancing Nursing Students' Interdisciplinary Competence via ARCS-Based Digital Health Care Design
BACKGROUND: Digital literacy is increasingly essential in modern nursing education, yet many students lack confidence and prior exposure to digital tools. METHOD: A 6-week project-based course, guided by the ARCS motivational model comprising Attention, Relevance, Confidence, and Satisfaction, engaged 40 first-year nursing students in designing health care applications using the Figma digital platform. The instructional process was organized into three progressive phases: motivational induction, design implementation, and final presentation. RESULTS: Students demonstrated a significant increase in learning motivation (t = 15.784, p < .001) and rated Figma's usability as excellent (System Usability Scale = 84.87). Reflections indicated enhanced digital confidence, interdisciplinary collaboration, and the ability to translate nursing knowledge into practical applications. CONCLUSION: Integrating motivational strategies with project-based learning and an intuitive digital platform effectively promoted student engagement, skill acquisition, and meaningful learning. This model offers a scalable approach for embedding digital competence into nursing curricula.
5 May 2026
Read appraisal →Behavior research methods
Neural cognitive diagnosis modeling incorporating response times
Cognitive diagnosis is a fundamental issue in the field of intelligent education, aiming to identify students' mastery of specific knowledge concepts. With computerized testing, response time (RT) is a process data that can be collected. Incorporating RT in cognitive diagnosis assessment can enhance diagnostic accuracy. However, RT is only considered in traditional statistical cognitive diagnosis models. Compared with traditional statistical diagnostic models, cognitive diagnosis models based on neural networks have advantages such as high precision and strong generalization ability. Therefore, this paper proposes a JRT-NCD (joint response times neural cognitive diagnosis) model that uses neural networks to model the complex nonlinear interactions between exercises and students and incorporates RT as a new feature to refine diagnostic results on student abilities. Research findings on three datasets of PISA2012, 2MFC, and ASSIST09 indicate that: (1) In comparison with traditional statistical models, neural networks have better fitting capabilities for real complex nonlinear data; (2) compared to the NCD model that disregards RT, JRT-NCD achieves higher diagnostic accuracy while maintaining its interpretability, and reduces the misleading effects of "overspeed behavior" on the diagnostic results.
27 Apr 2026
Read appraisal →PloS one
AI literacy among basic school teachers in Ghana: A structural equation modelling analysis
Understanding the synergy of Artificial Intelligence (AI) literacy among teachers is imperative for successful integration in educational settings. This study examined basic school teachers' (BSTs) AI literacy based on four key aspects: AI knowledge and understanding (KUAI), AI application (AAI), AI application evaluation (EAIA), and AI ethics (AIE). The study employed a cross-sectional survey design and purposive sampling to collect data from 319 BSTs in Ghana. A variance-based structural equation modelling (VB-SEM) approach was used to explore the relationships between these dimensions and provide insights into their interrelationships. The study found that AAI positively influenced KUAI, EAIA, and AIE. However, AIE did not significantly influence KUAI and EAAI. In addition, KUAI did not influence EAIA. The findings contribute to understanding the current state of AI literacy among BSTs in Ghana and highlight areas for improvement in AI education and professional development programmes.
19 Apr 2026
Read appraisal →Journal of medical Internet research
Artificial Intelligence in Health Professions Education: Qualitative Study of Student Experiences
BACKGROUND: Artificial intelligence (AI) is increasingly integrated into education and health care, raising questions about how students use these technologies and how AI influences their learning. In health education, understanding these trends is particularly important because student learning directly impacts future clinical skills. OBJECTIVE: This study aimed to explore the use of AI tools by health sciences students at the University of Ottawa. More specifically, it sought to identify the most frequently used AI tools, describe students' usage habits, determine which tools support knowledge acquisition and skill development, and gather students' recommendations for effective strategies to raise awareness and train their peers on the responsible use of AI. METHODS: A qualitative approach was used with students from 10 health professions who reported using AI in their studies. Data were collected through semistructured interviews and an open-ended qualitative online survey. Inductive thematic analysis within an interpretive paradigm was applied to capture patterns, perceptions, and emergent themes. RESULTS: A total of 51 health professions students participated in the study. Most were women between the ages of 20 and 29 years. ChatGPT (OpenAI) emerged as the most frequently used AI tool. Students perceived AI as a complementary tool that facilitated knowledge acquisition, skill development, writing, and problem-solving. AI adoption was driven by curiosity, peer influence, and the desire to improve work efficiency. Students critically evaluated AI results, integrated the tools into their learning processes, and emphasized the importance of technical skills, critical thinking, and digital literacy. Peer learning, hands-on demonstrations, and access to online resources were recommended for effective AI training. CONCLUSIONS: This research demonstrates that health professions students actively use AI tools, particularly ChatGPT, to support learning, skill development, and academic tasks. Although AI is valuable as an educational aid and its use varies by student and context, this highlights the need for structured guidance, critical evaluation skills, and peer-supported training. These findings highlight the importance of thoughtfully integrating AI into educational programs to enhance learning outcomes, foster skill acquisition, and ensure responsible and effective adoption.
4 Apr 2026
Read appraisal →J Surg Educ
Effect of Artificial Intelligence-Augmented Human Instruction on Feedback Frequency and Surgical Performance During Simulation Training
OBJECTIVE: To determine whether personalized feedback from a human instructor receiving artificial intelligence (AI) error data will result in reduced feedback frequency and improvement of surgical skill compared to AI instruction. As feedback was only provided following AI error detection, a reduced feedback frequency is associated with fewer errors in performance. We hypothesized that AI-augmented personalized instruction would result in reduced feedback frequency and improvement in technical skill. DESIGN: This cross-sectional cohort study was a follow-up of a randomized controlled trial, which utilized the NeuroVR, an immersive virtual reality neurosurgical simulator. Participants were stratified by year in medical school and block randomized to receive one of 3 educational interventions as they performed simulated procedures on the NeuroVR: AI tutor instruction, scripted human instruction, and AI-augmented personalized instruction. Performance was assessed by the feedback frequency and technical skill performance metrics. CLINICALTRIALS: gov ID: NCT06273579. SETTING: Neurosurgical Simulation and Artificial Intelligence Learning Centre, McGill University, Montreal, Canada. PARTICIPANTS: Volunteer sample of medical students from 4 Quebec universities in preparatory, first, or second year without prior use of the NeuroVR. Eighty-eight students participated in the study with 87 included in the final analysis; 1 was excluded due to technical issues. RESULTS: By the third repetition, the AI-augmented personalized instruction group received significantly fewer total instructions (incidence rate ratio [IRR], 1.50 [95% CI, 1.16-1.94] instructions; p < 0.001), and high aspirator force instructions (IRR, 1.71 [95% CI, 1.15-2.55] instructions; p = 0.002), compared to the second repetition. Compared to AI tutor instruction, AI-augmented personalized instruction resulted in a significantly lower rate of healthy tissue removal (p = 0.01), instrument tip separation distance (mean ratio, 1.25 [95% CI, 1.05-1.50] mm; p = 0.008), and aspirator force (mean ratio, 1.68 [95% CI, 1.23-2.31] N; p < 0.001). AI-augmented personalized instruction showed a significant improvement from baseline in all subsequent repetitions for all performance metrics. CONCLUSIONS: This cohort study demonstrated that AI-augmented personalized instruction resulted in less frequent feedback, indicating fewer errors in trainee performance, and an improvement in simulated surgical skills.
1 Nov 2025
Read appraisal →