JMIR Medical Education

Large language models are increasingly proposed as a way to deliver Socratic dialogue at scale in medical education, offering personalized, always-available, and lower-stakes inquiry that human educators cannot logistically provide. In this viewpoint, I argue that this promise is plausible but unproven and that 2 failure modes deserve more attention than they currently receive. The first is the mimicry trap: a system that fluently generates Socratic-sounding questions can appear to cultivate reasoning while functioning as interactive content delivery. I position this as an educational instance of the ELIZA effect and of the proxy-outcome problem; separating conversational mimicry from genuine metacognitive gain remains a central unresolved evaluation problem. The second is what I term the Panopticon Paradox: the data collection that makes AI tutoring effective may erode the psychological safety that Socratic inquiry requires, pushing learners toward performative rather than authentic engagement, in a manner analogous to gaming behaviors documented in intelligent tutoring systems. I present this second construct as a testable causal model with specified mediators, moderators, and falsifiable predictions rather than as an established finding. Because the evidence base is dominated by proof-of-concept tools, cross-sectional surveys, and short-term evaluations, I argue that AI is complementary rather than a replacement technology, and I propose a 3-pillar framework (governance, curriculum, and faculty development) tied explicitly to the 2 failure modes. I distinguish 4 modalities of delegation and argue that ethical deliberation, emotionally complex communication, and ambiguous clinical judgment cannot presently be recommended for autonomous or primary AI delivery.

Digital game–based learning (DGBL) is gaining traction in medical and nursing education, but its integration into public health and health sciences curricula remains limited. This study addresses this research gap by exploring student perspectives, use patterns, and expectations regarding DGBL within German public health study programs.

Technology-enhanced health care interprofessional education (IPE) places high demands on students’ self-regulated learning (SRL) and their ability to work productively with others to prepare them for collaborative practice in health care settings. Yet, little is known about how health professions students perceive and combine their own SRL with coregulation from human and AI-based support in such environments.

Standard setting is essential for a defensible assessment in medical education. The modified Angoff method requires several expert judges, and determining the minimally competent candidate is cognitively challenging. However, empirical evidence on the role of AI in standard setting is unclear.

Indonesia faces 3 interlocking medical workforce crises: an absolute specialist deficit projected to reach 70,000 by 2032 (national density of 0.18 per 1000 population vs the Ministry of National Development Planning [Bappenas] target of 0.28), severe maldistribution (with nearly 59% of specialists concentrated in Java), and a structural anomaly in which residents pay tuition while performing essential clinical work. The 2023 Health Law (Law 17/2023) authorized a transformative reform: a hospital-based residency pathway (Rumah Sakit Pendidikan Penyelenggara Utama [primary teaching hospital; RSPPU]) operating in parallel with the long-established university-based system.

Clinical educators regularly use simulation-based education to help learners develop clinical reasoning, procedural skills, and communication strategies. However, differences between simulated and live clinical environments, such as missing case details or workflow interruptions, can reduce realism and divert learners’ attention from the intended learning goals. While published design guidelines recommend pilot testing simulations, they do not explain how to systematically identify usability problems. Human factors evaluation methods can address this gap by identifying issues before piloting and providing design insights.

Medical history taking (MHT) is a foundational clinical competency for medical students; however, traditional training models using standardized patients face challenges such as resource constraints. Large language model–powered virtual standardized patients (LLM-VSPs) offer a safe, repeatable platform for self-directed practice with AI-automated feedback. Nevertheless, their effectiveness in authentic teaching environments and underlying learning mechanisms require further investigation.

As AI fundamentally transforms the healthcare landscape, medical education curricula have struggled to keep pace with these technological shifts. While current research has established a foundation for general AI literacy, less attention has been given to the role-specific competencies required for the diverse functions that healthcare professionals perform in an AI-integrated environment. Although existing tiered and domain-based competency models have clarified general AI competency requirements, they offer limited guidance on how such competencies should be differentiated according to the roles healthcare professionals perform in practice.

Entering clinical training, dental students must learn to read tooth-centered electronic dental records, but limited teaching time in patient-centered clinics can leave gaps in chart-reading literacy. Multimodal large language models (LLMs) that process dental record images may offer scalable educational support, yet their performance has not been benchmarked.

Generative AI tools became widely available to the public in November 2022. The extent to which these tools have been used by medical school applicants during the admissions process is unknown.

High-quality problem-based learning (PBL) during internship is resource-intensive and difficult to scale without consistent facilitation. Although generative AI is increasingly used in health professions education, many applications remain on-demand answer tools that may not reproduce core PBL processes.

Large language model (LLM)–based AI teaching agents are increasingly used in medical education, yet their pedagogical quality is typically judged by platform-generated scores whose scoring criteria are undisclosed and may not reflect the teaching quality of the agent.