JMIR Medical Education

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.

Assessment of technical aptitude, cognitive abilities, and personality characteristics is important in selecting candidates for surgical training. Currently, the selection of surgical training candidates does not systematically include objective assessment of these variables. Instead, it relies heavily on traditional selection methods, such as academic achievement, letters of recommendation, and interviews, whose presumed relationships with later performance are based on limited and inconsistent evidence.

“First, do no harm” is a fundamental principle in health care, and clinical researchers carefully monitor adverse drug reactions to ensure patient safety. However, educational researchers and clinical educators rarely apply the same level of scrutiny to potential adverse effects arising from their own interventions. This reflects a persistent misconception that educational interventions are inherently harmless, an assumption that warrants critical examination. In this tutorial, we highlight the underrecognized concept of adverse effects in medical education by introducing 12 representative educational adverse effects and offering corresponding tips for mitigating them. These include the Dunning-Kruger effect, in which increased confidence does not align with actual competence; the undermining effect, whereby external rewards reduce intrinsic motivation; spoon feeding that stunts independent learning; cognitive overload resulting from excessive information delivery; patient dehumanization when education prioritizes technical proficiency over empathy; critiques of outcome-based medical education that may overemphasize measurable competencies at the expense of holistic professional development; and the expertise reversal effect, in which instructional strategies beneficial for novices become counterproductive as expertise grows. Additional adverse effects include compromised psychological safety despite formal safeguards, authority and confirmation biases that reinforce outdated practices, developer bias in intervention evaluation, the Hawthorne effect influencing observed behavior, and concerns that overreliance on generative artificial intelligence may hinder the development of critical thinking and metacognitive skills. To better understand the nature of these adverse effects, we categorize them into three overarching domains. Cognitive and psychological adverse effects occur within the learner. Structural and cultural adverse effects result from features of the educational environment. Methodological and evaluative adverse effects arise from how educational interventions are designed and assessed. While these domains overlap, they provide a practical framework for identifying how well-intended educational strategies may lead to harm. Some may argue that these phenomena represent unintended consequences rather than adverse effects. However, the term unintended consequence presumes that sufficient effort was made to anticipate and manage possible effects, an assumption that may not always hold in medical education. We argue that educators and educational researchers should explicitly recognize adverse effects, critically evaluate educational interventions, and adopt mitigation strategies with a level of rigor comparable to that applied in clinical research to better protect learners and improve the quality of medical education.

Mobile learning (mLearning) is widely used in medical education. Previous research has focused on technology acceptance and intervention effectiveness, but rarely on their integration. Using realist evaluation, this study investigated the conditions under which mLearning is adopted and associated with learning-related outcomes in an authentic curricular setting.

Exposure-based cognitive behavioral therapy is among the least used evidence-based practices for anxiety disorders in routine care. Providers’ negative beliefs about exposure (eg, fears of harm or intolerability) are a major barrier. Experiential methods can reduce these beliefs but are limited by accessibility, standardization, and fidelity. Virtual reality (VR) offers a scalable way to deliver standardized experiential practice. Guided by an “exposure to exposure” (E2E) framework, we conceptualized VR training as an exposure intervention targeting therapists’ own anxious beliefs about exposure.

Knowledge of pathology is integral to numerous health care and research disciplines, besides routine laboratory-medicine operations. Despite its importance, there remains a significant gap in structured educational programs designed to provide a fundamental understanding of the pathologic basis of disease for an interdisciplinary body of research and health care professionals. This lack of generalized pathology training may contribute to a broader knowledge deficit in its translational applications.

Although artificial intelligence (AI) is increasingly adopted in health care, clinicians face barriers, including insufficient understanding, limited trust, and challenges in interpreting AI outputs. Existing frameworks, such as the United Nations Educational, Scientific and Cultural Organization (UNESCO) AI competency framework, lack clinical specificity. Additionally, there remains limited evidence on framework-based training programs for medical professionals.

Community health workers (CHWs) play an important role in delivering essential health services in low- and middle-income countries (LMICs). Training CHWs using digital approaches is on the rise. Although scoping and systematic reviews of digital training have been conducted for medical professionals in high-income countries (HICs), none have been conducted with lay professionals in LMICs, a population with different considerations.

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.

Lifelong learning (LLL) is increasingly important for health care professionals, particularly within the field of orthodontics, driven by emerging technologies, updated treatment techniques, and rising patient expectations. To maintain competence in current practice, orthodontists are expected to engage in continuous professional development throughout their careers. While the value of LLL is widely acknowledged, engagement levels among orthodontic professionals can vary due to a number of factors.