JMIR Medical Education

Learner autonomy—the ability to self-direct and regulate learning—is a key determinant of success in online education, yet its quantifiable impact in voluntary noncredit courses remains unclear. Understanding how autonomy translates into measurable behaviors and outcomes in clinical skills training may inform more effective online learning design and learning outcomes.

To further optimize the clinical and scientific training of high-level doctoral graduates, the Office of the National Postdoctoral Administration launched a clinical postdoctoral program in 2015. This program provides postdoctoral clinical medicine trainees with three years of individualized, intensive training through a full mentorship system, interdisciplinary collaboration, and a multi-team teaching platform.

The use of artificial intelligence (AI)–based large language model chatbots such as ChatGPT has become increasingly popular in many disciplines. However, concerns exist regarding ethics, legal considerations, accuracy, and reproducibility with its use in health care practice, education, and research.

Video-sharing sites such as YouTube and TikTok have become indispensable resources for learners and educators. The recent growth in generative artificial intelligence (genAI) tools, however, has resulted in low-quality, AI-generated material (commonly called “slop”) cluttering these platforms and competing with authoritative educational materials. The extent to which slop has polluted science education video content is unknown, as are the specific hazards to learning from purportedly educational videos made by artificial intelligence (AI) without the use of human discretion.

Digital tools are increasingly used to support clinical decision-making in dental education. However, the accuracy and efficiency of different support tools, including generative artificial intelligence, in the context of dental trauma management remain underexplored.

Artificial intelligence (AI) is increasingly being integrated into medical education. As AI technologies continue to evolve, they are expected to enable more sophisticated student tutoring, performance evaluation, and reforms of curricula. However, medical education entities have been ill-prepared to embrace this technological revolution, and there is anxiety concerning its potential harm to the community.

Deepseek-R1, an open-source large language model (LLM), has generated significant global interest in the past months.

Mock examinations are widely used in health professional education to assess learning and prepare candidates for national licensure. However, instructor-written multiple-choice items can vary in difficulty, coverage, and clarity. Recently, large language models (LLMs) have achieved high accuracy in medical examinations, highlighting their potential for assisting item-bank development; however, their educational quality remains insufficiently characterized.

The rise of generative artificial intelligence (gAI) has created both opportunities and challenges for higher education. Although the potential benefits of learning support are widely recognized, little is known about how incoming medical students in Japan perceive and intend to use this technology.

Project Extension for Community Healthcare Outcomes (ECHO) is an innovative model to increase capacity to treat patients in their community. Despite a growing body of evidence supporting its effectiveness, little is known about implementation processes of multiple ECHO programs within an institution from the perspective of executives and institutional leaders.

Information security is a critical challenge in the digital age, especially for hospitals, which are prime targets for cyberattacks due to the monetary worth of sensitive medical data. Given the distinctive security risks faced by health care professionals, tailored Security Education, Training, and Awareness (SETA) programs are needed to increase both their ability and willingness to integrate security practices into their workflows.

Free Open Access Medical Education has the potential to democratize access to medical knowledge globally; however, this potential remains largely unrealized, particularly in resource-limited settings. Content is increasingly concentrated on a small number of platforms, each hosting large volumes of material compiled from diverse sources.