As technology advances, virtual reality (VR) is increasingly being integrated into healthcare education to enhance learning outcomes. This systematic literature review and meta-analysis examined the effectiveness of virtual reality-based healthcare education. Randomized controlled trials (RCTs) published over the past 10 years were retrieved from 10 databases using VR, healthcare, and education as the primary keywords. Following the inclusion and exclusion criteria, 45 studies were included in the final analysis. A meta-analysis was performed to analyze the effects of VR in terms of knowledge, skill, and attitude. The results revealed that the use of VR significantly improved the knowledge (SMD: 0.28, 95% CI: 0.18–0.39, p < 0.001) and skill scores (SMD: 0.23, 95% CI: 0.11–0.34, p < 0.001), shortened the skill performance time (SMD: −0.59, 95% CI: −0.82 to −0.35, p < 0.001), and improved the satisfaction (SMD: 0.65, 95% CI: 0.48–0.81, p < 0.001) and confidence levels (SMD: 0.60, 95% CI: 0.41–0.80, p < 0.001). The in-depth analysis highlighted the significant potential of VR and provided practical implications in educational settings. In conclusion, effectively integrating VR with traditional educational methods is necessary to enhance both the quality of learning and the overall competence of healthcare professionals.

Yejin Han. Korean J Acupunct 2024;41:27-32. https://doi.org/10.14406/acu.2024.001

Syndrome differentiation and disease diagnosis are central to Traditional Asian Medicine (TAM) because they guide personalized treatment. Yet, most TAM courses give students few structured opportunities to practise these paired skills. We developed Gen-SynDi, a knowledge-guided generative-AI framework that links syndrome differentiation with disease diagnosis to improve training. Using standardized patient files from the National Institute for Korean Medicine Development, we built a fatigue-focused dataset covering five Western-defined diseases and seven TAM syndromes. Carefully designed prompts and a large language model produced 28 virtual patient cases by joining compatible disease–syndrome pairs while preserving clinical realism. Inside an interactive web simulation, students conduct history-taking, receive free-text answers, and propose both syndrome and disease diagnoses; immediate feedback highlights missing questions, reasoning gaps, and overall accuracy. A built-in scoring module supplies quantitative measures of inquiry coverage and diagnostic precision, plus brief explanations of overlooked clues. A prompt-component role analysis confirmed that our prompt design improves response fidelity, and external experts endorsed the scenarios’ realism and educational value. Gen-SynDi therefore offers a scalable bridge between textbook knowledge and clinical practice, strengthening learners’ skills in differential diagnosis and syndrome differentiation.

Background: In healthcare education, understanding and managing cognitive load is crucial for enhancing learning outcomes for students, healthcare professionals, patients, and the general public. Despite numerous studies developing cognitive load classification models, there is a lack of comprehensive guidelines on how to effectively utilize these models in healthcare education. This study reviews cognitive load classification models using physiological data to provide insights and guidelines for their development in healthcare contexts. Methods: A scoping review was conducted on studies published between 2015 and 2024, identified through databases including Scopus, Web of Science, PubMed, EMBASE, and PsycINFO. The search terms included “cognitive load,” “physiology,” “data,” and “classification.” Inclusion criteria were peer-reviewed journal articles in English, focused on the healthcare context, utilizing experimental physiological data, and developing classification models. After screening 351 articles, ten studies met the inclusion criteria and were analyzed in detail. Results: Task design predominantly focused on measuring intrinsic cognitive load by adjusting task difficulty. Data collection mainly utilized EEG (electroencephalogram) and body movement data. SVM (support vector machine) algorithms were the most frequently used for model development, with cross-validation and feature selection employed to prevent overfitting. This study derived the importance of clearly defining cognitive load types, designing appropriate tasks, establishing reliable ground truths with multiple indicators, and selecting contextually relevant data. Conclusions: This study provides a comprehensive analysis of cognitive load classification models using physiological data in healthcare education, offering valuable guidelines for their development. Despite the study’s limitations, including a small number of analyzed papers and limited diversity in educational contexts, it offers critical insights for using and developing cognitive load classification in healthcare education. Future research should explore the applicability of these models across diverse educational settings and populations, aiming to enhance the effectiveness of healthcare education and ultimately improve learning and healthcare outcomes.

Problem: Diagnostic reasoning, the cognitive process of interpreting clinical information to arrive at a diagnosis, is a critical competency in medical education, and has been taught using authentic methods such as standardized patients and peer role-playing that simulate real clinical encounters. Although effective, these approaches require substantial time, space, and cost. Approach: To address the limitations of traditional approaches, artificial intelligence-powered virtual patient chatbots were developed between April 21-28, 2025, with a chatbot builder that incorporates natural language processing. The design was guided by 2 core cognitive models of diagnostic reasoning—the dual process model and the memory model. The chatbots simulated authentic physician-patient interactions across 3 clinical scenarios: fatigue, stomach ache, and memory loss. Students can perform differential diagnoses based on various clinical data and received immediate feedback on diagnostic accuracy. Outcomes: The patient chatbots were evaluated through surveys and interviews with faculty (May 2025) and students (November 2025). Findings consistently indicated that the chatbots functioned as an effective (4.50/5) and usable tool (4.46/5) for diagnostic reasoning practice. Participants perceived that the chatbots supported “core cognitive processes of diagnostic reasoning” by prompting active hypothetico-deductive reasoning, whereas repeated exposure to different cases was viewed as facilitating pattern recognition. Faculty highlighted the chatbots’ cost-efficiency and scalability within curricula, whereas students emphasized authentic “from-zero” clinical reasoning and the value of reviewing dialogue logs for reflection. Overall, the results demonstrate that the chatbots can provide a feasible and educationally meaningful environment for practicing diagnostic reasoning in medical education. Next Steps: Future work should refine artificial intelligence-powered virtual patient chatbots by incorporating tiered case complexity, patient-centered language, and equity-oriented design. Effective use will require systematic instructional design and learner preparation for emotionally challenging scenarios. Larger empirical studies using both outcome and interaction data are needed to evaluate educational impact and guide integration into clinical training.

The 2024 medical crisis in South Korea prompted nationwide leaves of absence among medical students, with multiple entering cohorts now studying together in the premedical curriculum. This study examined premedical students’ college adjustment under these conditions, including differences by student characteristics and experiences, its relationship with professional identity, and students’ support needs. A nationwide cross-sectional survey was conducted in May 2026 at all 39 medical schools offering premedical programs. After invalid responses were excluded, the main analyses included 884 students from the 2024–2026 cohorts. Analyses comprised descriptive statistics, the chi-square test, two-level random-intercept multilevel models with students nested within universities, Pearson correlations, and multiple regression. Overall adjustment exceeded the scale midpoint (mean=3.67 of 5), but adjustment in career preparation was lowest, and crisis-related experiences differed significantly by cohort. With cohort, leave duration, class format, and inter-cohort conf lict considered together, lower adjustment was associated with inter-cohort conf lict (b=–0.328) and exclusively combined-cohort classes (b=–0.221). Cohort itself was not significant, and mixed formats did not differ from same-cohort classes. Professional identity correlated positively with adjustment (r=0.60), and attachment and detachment jointly explained 40.6% of the variance. Students prioritized support for the learning environment and career preparation over emotional support, with needs differing by cohort. Recovery efforts should address inter-cohort relationships, reduce exclusively combined-cohort classes, improve the learning environment, and provide support tailored to each cohort with student participation.