JMIR Medical Education最新文献

Unlabelled: Limited clinical placements for mental health courses in the United Arab Emirates have made it difficult to provide consistent experiential learning for undergraduate nursing students. As a result, nurse educators are considering technology-enabled learning approaches to deliver clinical skills training. This Viewpoint presents a reflective, theory-informed account of the first-year integration of an artificial intelligence (AI)-enabled, voice-interactive simulated patient into an undergraduate mental health nursing practicum. Grounded in Kolb's experiential learning cycle and aligned with established simulation best practices, the initiative was designed to support therapeutic communication, psychiatric assessment, and clinical reasoning through structured prebriefing, immersive interaction, and guided debriefing. The paper describes the educational rationale, scenario development, implementation processes, and contextual challenges encountered during real-world deployment across university and clinical environments. AI-supported simulations offered a standardized and psychologically safe context for students to engage with complex psychiatric scenarios, particularly when direct patient interaction is constrained. We discuss operational insights related to technical reliability, environmental requirements, faculty preparation, and assessment integration alongside considerations for scalability and sustainability in resource-limited settings. While AI-supported objective structured clinical examinations have been incorporated to support assessment consistency, formal psychometric validation and outcome comparisons have not been undertaken at this stage. By sharing lessons learned from early implementation, this Viewpoint contributes practical insights for nursing educators facing similar structural constraints. AI-enabled simulation is presented as a strategic complement to, rather than a replacement for, traditional clinical placements, with future empirical research needed to evaluate educational outcomes and long-term impact.

This mixed methods pilot study evaluates the feasibility and effectiveness of microlearning for faculty development in cardiovascular education. Microlearning appears feasible and well-received for faculty development, offering a scalable, flexible approach.

Background: Antimicrobial resistance (AMR) is a global problem. Training health care professionals in the rational use of antimicrobials is essential to curb AMR.

Objective: To support efforts to reduce antibiotic resistance, this study assesses how well a gamified educational intervention might improve health care professionals' and students' understanding and use of appropriate antibiotics.

Methods: This is a prospective interventional study conducted for clinical practitioners, undergraduates (MBBS and interns), postgraduates, and pharmacy students. A total of 60 participants were included in the study. Innovative games were administered to support the management of infections across multiple body systems, in accordance with the 2022 Indian Council of Medical Research treatment guidelines and the latest Infectious Diseases Society of America guidelines, incorporating multiple instructional components. Pretest and posttest questionnaires were administered and evaluated.

Results: After the intervention, participants' ability to differentiate between bacterial and viral symptoms in respiratory tract infections and gastroenteritis improved from 48% to 94%. The practice of selecting the appropriate empirical antimicrobial at the correct dose, route, and duration also demonstrated significant improvement, reflecting enhanced adherence to principles of rational antimicrobial use.

Conclusions: The gamified intervention successfully improved participants' knowledge and awareness of rational antimicrobial use. Substantial improvements across all the assessed components highlight the positive impact of the intervention in promoting optimal antimicrobial use and curbing AMR. Innovative gamified interventions may foster better and longer-lasting awareness, supporting appropriate antimicrobial use.

Background: Although artificial intelligence (AI) is being rapidly integrated into medical education, insights from medical students, particularly in the Chinese context, remain limited.

Objective: This study was designed to explore Chinese medical students' perceptions of and attitudes toward the integration of AI into medical education, as well as the factors that may influence their perspectives. The findings of this research offer valuable insights to assist medical educators in the effective implementation of these innovative educational approaches.

Methods: On the basis of the estimated total number of clinical medical students at the target institutions, the sample size was calculated to be 379. A web-based questionnaire survey was designed to investigate the acceptance level of medical students toward the application of AI. The questionnaire consisted of 14 questions across 4 dimensions, which included demographic characteristics, perceptions of AI application, willingness, and concerns. Each dimension contained 3 to 4 questions. Descriptive statistics were used to tabulate the frequency of each variable. Chi-square tests and multiple regression analyses were conducted to measure the influencing factors.

Results: A total of 566 cross-sectional online surveys were distributed from December 2023 to January 2024 through a snowball sampling method. Finally, 490 medical students from various local tertiary medical centers were involved. Overall, a majority of the participants showed a positive attitude toward future learning and the usage of AI, manifested as totally willing to acquire relevant knowledge (222/490, 45.3%), totally willing to use AI tools (230/490, 46.9%), and totally desiring that schools would offer AI-related courses (230/490, 46.9%). However, there is still a large proportion (392/490, 80.0%) of participants who expressed concerns regarding ethical issues. The findings also indicated that gender and educational level were significantly correlated with the AI application. Specifically, regression analysis indicated that male participants were more inclined to acquire AI information through social media (odds ratio 0.458, 95% CI 0.33-0.67; P<.001) and that male or graduate-level participants were more likely to use AI for academic writing purposes (odds ratio 0.476, 95% CI 0.38-0.82; P=.001 for male; odds ratio 1.552, 95% CI 1.32-1.77; P=.009 for graduate students, respectively).

Conclusions: Our findings indicate that a general awareness of AI's role in medical education is evident among students. However, subgroup-specific differences must be taken into account, particularly when designing and optimizing educational strategies integrated with AI. This consideration is critical to ensuring that such tools align with the diverse learning needs of distinct student groups.

Background: Point-of-care ultrasound training is being increasingly integrated into undergraduate medical education, leading to a substantial demand for trained faculty to provide instruction and feedback.

Objective: This study aimed to develop an adjunct tool, a deep learning-based feedback model, to facilitate student learning.

Methods: Renal ultrasound images (N=2807) were used to train a cascaded deep learning-based feedback model that classified images into three categories: optimal, suboptimal, and incorrect. Suboptimal images were further subcategorized as images with artifact, incorrect gain, and/or incorrect positioning. The model was deployed among year 5 medical students receiving bedside ultrasound training, who were invited to upload renal ultrasound images to an online platform for automated image quality grading and feedback. A mixed methods analysis was used to evaluate students' learning experience. Focus group interviews were organized to qualitatively analyze the successes and challenges of implementation. Quantitative analysis was based on responses to a 5-point Likert scale questionnaire and performance on the objective structured clinical examination (OSCE). Objective structured clinical examination scores were compared with mean OSCE scores from the 2 years preceding implementation of the deep learning-based feedback model.

Results: Focus group interviews identified that the deep learning-based feedback model encouraged self-regulated learning but also recognized that discordant curricular design and hardware limitations impeded its use. The 11-item online questionnaire had a response rate of 42.4% (98/231 students). Among respondents, 32% (31/98) to 48% (47/98) found the model helpful in assisting ultrasound training (Likert score of 4-5 for items 1-3), while 49% (48/98) to 76% (74/98) were satisfied with its usability and their interaction with the model (Likert score of 4-5 for items 4-11). The mean OSCE score was 9.73 (SD 0.76) out of 10, compared with mean scores of 9.35 (SD 1.03; P=.06) and 9.45 (SD 0.97; P=.15) out of 10 in the 2 individual years preceding implementation of the model.

Conclusions: A cascaded deep learning-based feedback model was developed and deployed among year 5 medical students receiving bedside ultrasound training, with positive learner responses and enhanced self-regulated learning. The innovation was associated with increased student engagement and improved ultrasound skill acquisition among novice learners.

Background: Cognitive behavioral therapy (CBT) training faces significant challenges, including supervised practice with diverse cases, inconsistent feedback, resource-intensive supervision, and difficulties standardizing competence assessment.

Objective: This study evaluated the acceptability and feasibility of CBT Trainer (TTZ), the first virtual patient platform to provide real-time feedback aligned with established competence frameworks. The mobile app trains psychological practitioners using standardized artificial intelligence patient interactions and the evaluation of therapist responses against competence frameworks to enable structured skill development in a controlled, repeatable environment that complements traditional training methods.

Methods: This mixed methods pilot study used a 2-stage approach. Stage 1 involved usability testing with 4 participants. Stage 2 included 59 participants from psychological practitioner training programs (a Low Intensity CBT Interventions Program and a Doctorate in Clinical Psychology) who engaged with the CBT Trainer voluntarily for over 1 month. Measures of impact included the System Usability Scale (SUS), platform naturalistic engagement, poststudy questionnaire on perceived competency development, comparative evaluation against traditional role-play, and qualitative feedback.

Results: Participants engaged voluntarily with the platform for an average of 95.24 (SD 134.58; median 45.34, IQR 11.57-105.15) minutes of active role-play. Platform usability was rated as excellent (mean SUS 82.20, SD 12.93). Self-reported competence improvement improved most in assessment skills (96.7%) and information gathering (66.7%). When compared to traditional peer role-play exercises, participants rated CBT Trainer moderately favorably (mean 5.90/10, SD 1.94). Qualitative feedback highlighted strengths in competency-aligned feedback, on-demand accessibility, and a psychologically safe practice space.

Conclusions: This pilot study provides evidence that an artificial intelligence-based patient simulation shows promise as a supplementary training tool for psychological therapists who use CBT in their practice, particularly regarding accessibility and immediate feedback. Future research should use randomized controlled designs with objective competence assessments.