Abstract Background Communication during crisis management involves not only the exchange of clinical information but also the expression and regulation of emotional tone and sentiment, which may reflect clinician performance during a critical incident. In simulation-based medical education, these emotional dynamics are rarely measured objectively, limiting the ability to capture aspects of performance relevant to competency assessment. Objective This study aimed to examine whether automatically extracted speech- and text-based emotion signals varied across predefined phases of critical incident simulation training and whether these signals were associated with expert-rated performance among anesthesiology residents. Methods In this exploratory observational study, we analyzed 164 simulated crisis scenarios performed by 90 anesthesiology residents from 17 hospitals during a national board preparation workshop. Each high-fidelity simulation comprised 4 predefined phases: initial condition, extreme deterioration, advanced cardiovascular life support (ACLS) management, and recovery. Resident speech was isolated and analyzed using a speech-based emotion recognition (SER) model that estimated 8 emotion categories per utterance. Speech was also automatically transcribed and analyzed using text-based emotion recognition (TER), which estimated 9 emotion categories. Expert anesthesiologists rated performance on a 1-5 scale. ANOVA tested associations between emotion categories and performance, as well as variation across simulation phases. Exploratory mixed-effects sensitivity analyses accounted for repeated simulations and scenario-level clustering. Results After 7 recordings were excluded because of technical issues, 65 of 164 (39.6%) simulations were classified as poor performance (score ≤2), 72 (43.9%) as intermediate, and 27 (16.5%) as excellent (score=5). SER categories differed across performance groups ( F 2,161 =4.61; P =.02): poor performance showed more fear and surprise, whereas excellent performance showed more calm, happiness, and neutrality. In mixed-effects analyses, performance group remained associated with happy, angry, fearful, surprise, calm, and neutral speech-emotion proportions after false discovery rate correction. The largest excellent vs poor differences were observed for neutral (+4.24 percentage points), happy (+4.11), and surprise (−3.17) speech affect. TER showed a directionally similar but nonsignificant omnibus association ( F 2,161 =2.57; P =.08); mixed-effects analyses identified associations with happy and sad text-emotion proportions after correction. During extreme deterioration and ACLS management, fear and surprise together accounted for 64.1% and 64.6% of classified speech emotions, respectively, among poor performances. During ACLS management, neutral and calm emotions accounted for 49.1% and 37.7%, respectively, among excellent performances. Conclusions Automated speech- and text-based emotion recognition captured phase-dependent affective communication patterns during critical incident simulations. Speech-derived features were more consistently associated with expert-rated performance, whereas text-derived findings were more exploratory and emotion-specific. These markers may complement simulation debriefing and formative feedback, but validation using multirater outcomes, preregistered adjusted models, and larger multicenter datasets is needed before they inform competency assessment.
Abstract Student engagement reforms have increased active learning in higher education. However, visible participation does not always require students to rehearse the spoken, interactive forms of reasoning central to professional practice, such as explaining one’s thinking, responding to challenges, building on others’ ideas, and working through uncertainty in real time. We describe this gap as an engagement blind spot. We define audible reasoning as the deliberate externalization of disciplinary or professional reasoning through spoken interaction so that its assumptions, evidence, uncertainty, and justification can be examined, challenged, revised, and developed collaboratively. It is particularly relevant in health professions education and also applies to laboratory-based and scientific disciplines where students must communicate their reasoning under conditions of uncertainty, scrutiny, and collaboration. Digital, hybrid, asynchronous, and AI-enabled learning environments sharpen this blind spot. Shared documents, online quizzes, discussion boards, and generative AI tools can support participation and access; however, they can also allow students to engage without speaking or to produce polished answers without making the underlying reasoning visible. In this context, educators need to ask not only whether students are engaged but also what behaviors they are rehearsing and whether their reasoning is available for examination and development. This tutorial offers 12 practical, evidence-informed tips developed iteratively from recurring challenges encountered in teaching, curriculum design, and faculty development and refined against selected literature. The tips are organized around 4 clusters covering the design of activities that require audible reasoning, the elicitation of reasoning in practice, the development of reasoning through listening and response, and the preservation of audible reasoning in digital and AI-enhanced environments. Across these 4 clusters, we suggest small but intentional shifts that help educators move from silent, constructive tasks toward audible, dialogic, and professionally relevant participation. These include designing tasks around uncertainty, creating low-stakes opportunities for spoken reasoning, using digital tools to surface and organize contributions for further discussion, structuring active listening and peer feedback, reviewing group dialogue formatively, and using conversational AI as a rehearsal partner rather than an authority. These strategies require attention to psychological safety, cultural responsiveness, accessibility, equity, privacy, data governance, and ongoing monitoring. Audible reasoning is not synonymous with speaking more, volunteering more frequently, or sounding fluent. Designing for audible reasoning is not simply an engagement strategy. It is a way of helping students practice the communicative and collaborative behaviors needed to explain, defend, question, and revise human judgment in clinical, scientific, and other professional contexts.
Abstract Urology education must prepare learners to integrate anatomical visualization, diagnostic reasoning, technical performance, communication, and ethical judgment, often under restricted working hours and limited access to high-fidelity cases. This viewpoint argues that large language models (LLMs) and metaverse-related technologies should be integrated as complementary, supervised educational tools rather than treated as autonomous replacements for educators or clinical experience. LLMs can support cognitive apprenticeship, documentation practice, assessment, and communication simulations, whereas extended reality, cloud computing, and virtual digital humans can support spatial, procedural, and remote learning. Our aim is to articulate 3 key messages: first, the educational value of these technologies depends on alignment with defined urological competencies; second, implementation should proceed in stages from governed data infrastructure to intelligent interaction and immersive multimodal training; and third, educational effectiveness must be established through prospective evaluations of transfer to clinical performance, not through technical novelty alone. We emphasize the need for rigorous randomized controlled trials, identify requirements for haptic and biomechanical fidelity, and discuss hallucination, bias, privacy, accountability, and deskilling. The responsible future of intelligent urology education will therefore require collaboration among learners, educators, clinicians, engineers, ethicists, institutions, and professional bodies.
Abstract BackgroundSimulation-based education is increasingly used in medical training to safely develop clinical skills. However, the perceived relevance of a simulation (“utility value”) to a learner’s future work may influence its effectiveness. ObjectiveThis study evaluated a digital radiology simulation module for medical technical assistant trainees in Germany, examining learning gains, group differences in motivational variables, and the moderating role of utility value. MethodsFifty German medical technical assistant trainees (including 24 specializing in radiology and 26 focused on laboratory medicine) completed a pretest and posttest requiring them to arrange 12 steps of a contrast injection procedure in the correct order. All participants then engaged in an interactive computer simulation of the procedure. After the simulation, trainees rated their self-efficacy, as well as the intrinsic value and perceived utility value of the simulation. Learning gains were analyzed using a mixed-design ANOVA on the Spearman footrule distance composite score, and group differences in motivational variables were tested using independent-sample 2-tailed t ResultsTrainees showed significant improvement in procedural sequencing from pretest to posttest (F1,48P2t46PdF1,46P ConclusionsThe simulation produced robust learning gains regardless of learner specialization or perceived relevance. While utility value perception differed markedly between groups, it did not translate into differential learning outcomes, suggesting that well-designed simulations can be effective across learners with varying levels of task relevance.
