JMIR Medical Education

No preview text available.

No preview text available.

Microlearning has emerged as a flexible, technology-enabled instructional approach in health professions education, yet its longitudinal evolution, collaboration patterns, and thematic development remain underexplored.

The demand for mental health treatment is increasing, while the availability of treatment remains insufficient to meet the rising demand. Alternative solutions need to be explored to enable access to care for patients who cannot participate in traditional psychotherapeutic settings due to common barriers like place of residence, professional obligations, or physical limitations.

Large language models (LLMs) are rapidly incorporated into medical education and examination preparation; yet, most benchmarking evidence is derived from English-language material. Whether frontier commercial models and Brazilian Portuguese domain-specialized systems perform equivalently on high-stakes Brazilian medical examinations remains unclear.

High-quality observation and feedback contribute to the development of clinical competence and professional growth in medical education. Faculty often struggle to translate verbal observations into written feedback because of documentation burden and competing demands. Ambient artificial intelligence (AI) scribes, already adopted in clinical practice, may address this challenge by capturing verbal exchanges and generating structured notes.

High-quality wound care requires early and effective interprofessional collaboration between medical, nursing, and pharmacy professionals. However, interprofessional education (IPE) in this context remains limited in higher education. Immersive virtual reality (iVR) seems to be a promising IPE tool, enabling a standardized, realistic, and safe learning environment that allows multiple learners from different professions to train together. However, its educational effectiveness likely depends on instructional design that supports learning while managing cognitive demands.

Competency-based medical education (CBME) relies on entrustable professional activities (EPAs) and Clinical Competency Committee (CCC) deliberation to support defensible decisions about trainee progression. As digital assessment platforms increasingly aggregate workplace-based assessment data across training programs, large-scale learning analytics can provide new insights into how entrustment decisions are generated and interpreted within CBME systems. However, little is known about how national assessment infrastructures influence patterns of entrustment attainment.

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.

Generative artificial intelligence (AI) is quickly changing medical education, even as medical students still face high levels of stress, anxiety, and burnout. These simultaneous trends—technological upheaval and ongoing mental health issues—bring up important questions about how future doctors will be trained and supported. Understanding how these factors might influence each other is crucial for developing resilient, future-ready medical education systems.

Empathy is essential to patient-centered care, yet erosion of empathy has been reported during medical training. Although educational interventions may support empathy development, longitudinal evidence on whether these changes persist remains limited, particularly in non-Western settings.

Large language models (LLMs) have emerged as promising tools in medical education due to their ability to understand, generate, and reason with natural language. Their ability to simulate expert reasoning suggests a potential for supporting quality control in assessment design. In this study, the use of LLMs in identifying ambiguous or poorly constructed exam items in critical care academic assessments was evaluated.