The field needs studies that isolate the incremental effect of AI rather than testing a technology bundle

The gap

Evidence profile

Sourced from the future work and inline gaps and stated research gap of the source papers, classified as general, drawn from work published between 2025 and 2026, spanning 6 journals. Those papers have been cited 27 times in total.

Supporting evidence — 7 representative gaps

• Artificial intelligence in anesthesiology education: transformative applications, challenges, and future perspectives (2026) · Frontiers in Medicine · doi

  6.1 Toward a human-centric, augmentative model A key consideration emerging from this synthesis is that artifi- cial intelligence may be most appropriately positioned as an aug- mentative tool within a human-centric model for anesthesiology education, rather than as a replacement for traditional teaching methods. This perspective is supported by the challenges identified in this review, including overreliance on automated feedback, potential deficits in non-technical skills, and AI’s inherent lack of humanistic reasoning. These limitations suggest that AI is unlikely to replace the nuanced clinical judgment, empathy, mentorship, and contextual decision-making provided by experienced educators and clinicians (19, 20, 49). Current evidence indicates that AI-driven simulations and ITS can support foundational training, procedural practice, and knowledge acquisition in controlled environments. However, most existing studies focus on simulation-based learning outcomes, skill acquisition, or learner satisfaction rather than long- term clinical performance or patient outcomes. Therefore, AI should be viewed as a supplementary educational tool that may support early skill development and knowledge reinforcement, while higher-order competencies such as clinical reasoning, com- munication, teamwork, and ethical decision-making continue to rely heavily on human instruction and mentorship. Gamification and AI-assisted interactive learning environments may improve learner engagement and provide opportunities to simulate complex patient management scenarios (72). Nevertheless, the educational effectiveness of these approaches remains dependent on instruc- tional design, faculty involvement, and appropriate integration into existing curricula rather than the technology itself. Similarly, the increasing use of large language models such as ChatGPT in medi- cal education illustrates the potential of AI to support learning and information retrieval, but concerns regarding accuracy, hallucina- tions, and overreliance require that such tools be used cautiously and under appropriate educational supervision (62). Overall, the available evidence suggests that the most realistic near-term role of artificial intelligence in anesthesiology education is likely to be aug- mentative rather than transformative. AI may support simulation training, feedback generation, and personalized learning pathways, but it is unlikely to replace human educators in areas requiring pro- fessional judgment, ethical reasoning, and mentorship. Future research should therefore focus not only on technological develop- ment but also on determining the appropriate balance between AI-assisted learning and traditional faculty-led education. 6.2 Overcoming implementation barriers: ethics, equity, and governance Building upon the challenges outlined in Section 5, particularly algorithmic bias and data privacy, proactive strateg

  generalfuture workevidence 5/5
  Keywords: learning human education rather support reasoning clinical mentorship educational appropriate centric model intelligence mentative tool
• Piloting an AI Introduction Program for Incoming Medical Students: A Novel Approach to Medical Education (2026) · Medical Science Educator · doi

  The orientation session will continue for future student cohorts at the University of Minnesota Medical School. However, based upon the results of this study, including minimal student background in using AI, more demonstra- tion of basic AI prompting and iterative generation will be incorporated, more time will be given for student practice with using AI in virtual small breakout rooms, and more time for Q&A with large group debrief and discussion will be planned. The student cohort was quite thoughtful regard- ing the limitations and harms of using AI so this will again be emphasized with recommendations for students on best practices of using AI as a teammate and a catalyst for pro- duction, and when to consult a faculty member. Existing instruction in evidence appraisal and uncertainty manage- ment as already incorporated into medical education, should expand to encompass AI-related decision-making. These skills ought to be expanded to include AI as a tool in the physician’s armamentarium. To build on the momentum of this pilot program, future sessions and data collection will include longitudinal fol- low-up to assess the long-term effects of AI education on knowledge, attitudes, and behavior. The longitudinal course called “clinical skills” in our curriculum, tasked with edu- cating students on essential clinical competencies, will be introducing dedicated AI sessions for pre-clinical medical students in future years with more direct consideration of AI use in future practice including separated and spaced ses- sions related to: 1. Clinical and healthcare specific uses including differ- ential diagnosis, documentation, answering clinical questions, prior authorization documentation, and other clinical tasks. 2. Non-clinical uses such as for time management and organization tasks, email drafts or professional communication, research and project management frameworks, coaching, and others. 3. In depth discussion of the Ethics of AI including but not limited to future role as a physician and physician responsibilities, bias and impact on underserved popula- tions, environmental considerations, and others. Plans include collaboration with clerkship directors to intro- duce AI-integration discussions relating to clinical practice, learn from what students have seen in their rotations, and have a more complete understanding of the rapidly evolv- ing integration of AI into healthcare to inform future pre- clinical sessions’ content and considerations. Investigating the integration of AI into these additional aspects of medical education could provide deeper insights into AI’s value and the development of standardized AI cur- ricula incorporating meaningful ethical principles, crucial in equipping future physicians with the necessary skills and judgment to navigate an increasingly AI-integrated health- care environment. Acknowledgements None.

  generalfuture workevidence 5/5
  Keywords: clinical future student medical including using students time practice education skills include physician sessions integration
• Artificial Intelligence in Health Education and Practice: A Systematic Review of Health Students’ and Academics’ Knowledge, Perceptions and Experiences (2025) · International Nursing Review · cited 27× · doi

  Further research into the curriculum design of some healthcare courses could identify any gaps in knowledge when considering ethical principles and the use of AI in health practice. AI algorithms can analyse data on student performance to identify gaps in knowledge and suggest targeted interventions. In addition, this systematic review found a gap in the literature regarding research about AI education in health-related courses.

  generalinline gapsevidence 5/5
  Keywords: courses identify gaps knowledge health further curriculum design healthcare considering ethical principles practice algorithms analyse
• AI-Powered Avatars in Medical Education: Advancing Virtual Coaching for Clinical Readiness (2026) · International Journal of Advanced Corporate Learning (iJAC) · doi

  This study contributes evidence that AI-powered avatars can play a transformative role in medical education, particularly in preparing learners for complex, emotionally 100 International Journal of Advanced Corporate Learning (iJAC) iJAC | Vol. 19 No. 2 (2026) AI-Powered Avatars in Medical Education: Advancing Virtual Coaching for Clinical Readiness charged, and ethically sensitive encounters. By offering adaptive, repeatable, and unbiased practice opportunities, Conversation Mastery addresses limitations inherent in traditional lectures, role-play, and standardized patient encounters. The pilot results demonstrate that learners trained with avatars not only achieve higher success rates in conflict de-escalation but also develop greater confidence and adherence to best practices. Beyond quantitative outcomes, the qualitative feedback highlights an equally important dimension: learners valued the ability to practice “without judgment,” repeat encounters as needed, and receive structured, objective feedback. These findings resonate with the principles of deliberate practice, suggesting that avatars can provide the high-frequency, feedback-rich rehearsal that underpins skill mastery. From a broader perspective, Conversation Mastery represents more than a technological innovation; it is a pedagogical shift. Its design reflects a commitment to responsible AI use—ensuring transparency, inclusivity, accessibility, and data privacy. The system complements rather than replaces human faculty, freeing educators to concentrate on higher-order reflection and debriefing. By enabling cultural and linguistic customization, it also supports global scalability, reducing inequities in access to high-quality simulation-based education. Looking ahead, several priorities emerge for future research and development: 1. Longitudinal Impact – Studies should examine whether improvements achieved through avatar-based practice translate into sustained performance gains in clinical settings. 2. Multi-Center Validation – Expanding pilots across diverse institutions and cultural contexts will test generalizability and support global adoption. 3. Curricular Integration Models – Research should explore optimal ways to embed avatar-based training within medical and nursing curricula, from undergraduate education to residency and continuing professional development. 4. Advanced Analytics – Future iterations of the platform may incorporate predictive learning analytics, enabling early identification of learners at risk of underperformance and supporting personalized remediation. 5. Ethical Horizons – Continued dialogue is needed around the boundaries of emotional AI in education, ensuring that innovation remains aligned with professional values and regulator

  generalfuture workevidence 5/5
  Keywords: education avatars learners practice medical encounters mastery feedback based powered play role advanced learning ijac
• Transformation and Reinvention: A Comprehensive Analysis of Frontiers and Trends in AI-Empowered Medical Education by 2025 (2026) · Contemporary Education and Teaching Research · doi

  imaging diagnostics, novel drug development, and precision medicine (Maity & Saikia, 2025; Yu et al., 2025; Mizna et al., 2025). Transformations at the level of diagnostic and therapeutic technologies inevitably necessitate corresponding adjustments in medical education models (Khakpaki, 2025; Cheng & Zhu, 2025). In the face of a highly intelligent healthcare medical education is under considerable pressure with regard to the pace of knowledge renewal, the specificity of skills training, and the adaptability of its pedagogical philosophy (Tucker, 2025; Miguez-Pinto et al., 2025). environment, traditional To ensure that future healthcare professionals can safely, ethically, and effectively utilize AI tools, arising from technological the current medical education system must undergo systematic reform (Cho Kwan et al., 2025). Such reform requires not only innovation in curriculum design and assessment strategies, but also proactive engagement with the ethical dilemmas and practical barriers integration (Boscardin et al., 2025). Encouragingly, preliminary achievements have emerged in AI-enabled medical education. Applications such as adaptive learning pathway simulation immersive are training, gradually being implemented (Turner et al., 2025; Seneviratne & Manathunga, 2025; Wang et al., 2025). design, and automated feedback systems virtual However, during the process of technological deployment, substantial heterogeneity persists among faculty and students with respect to AI literacy, acceptance, and readiness, warranting objective evaluation (Clement David-Olawade et al., 2025; Yazdi et al., 2025). Moreover, algorithmic bias, risks to academic integrity, data privacy concerns, and regulatory lag constitute major bottlenecks limiting equitable and widespread adoption (Stern et al., 2025; Liu et al., 2025; Sun et al., 2025). Against this issue, 41 Corresponding Author: Mingzhe Li The First Affiliated Hospital (The First Clinical Medical School) of Guangdong Pharmaceutical University, P.R. China ©The Author(s) 2026. Published by BONI FUTURE DIGITAL PUBLISHING CO.,LIMITED. This is an open access article under the CC BY License(https://creativecommons.org/licenses/by/4.0/) Contemporary Education and Teaching Research Vol. 7 Iss. 2 2026 the present review synthesizes studies published throughout 2025 to examine the current state of AI literacy among medical students and educators, analyze the effectiveness of emerging application models, and critically evaluate the associated ethical and implementation challenges. The ultimate aim is to provide theoretical grounding and practical guidance for constructing a future-oriented, human– AI collaborative framework in medical education.

  generalfuture workevidence 5/5
  Keywords: medical education future corresponding models healthcare training technological current reform design ethical practical among students
• Transformation and Reinvention: A Comprehensive Analysis of Frontiers and Trends in AI-Empowered Medical Education by 2025 (2026) · Contemporary Education and Teaching Research · doi

  intelligence: A artificial developing nation’s context. BMC Medical Education, 1060. https://doi.org/10.1186/s12909-025-07223-6 Tong, X., Hu, Y., Long, Y., et al. (2025). The application of problem-based learning (PBL) guided by ChatGPT in clinical education in the of nephrology. BMC Medical department Education, 1048. https://doi.org/10.1186/s12909-025-07427-w Tucker, F. (2025). Doing philosophy and the future of the “good doctor” paradigm. Medicine, Health Care and Philosophy, 28(4), 669–677. https://doi.org/10.1007/s11019-025-10294-3 25(1), Contemporary Education and Teaching Research Vol. 7 Iss. 2 2026 Turner, L., Kelleher, M., Overla, S., et al. (2025). Harnessing the generative power of AI to move education. closer personalized medical Academic Medicine, 1447–1451. https://doi.org/10.1097/ACM.000000000000618 5 100(12), to Ugoala, O., Ebubechukwu, U., Mares, A. C., et al. (2025). Visual in cardiology: Past, present, and future. American

  generalfuture workevidence 5/5
  Keywords: education https medical philosophy future medicine intelligence artificial developing nation context tong long application problem
• Artificial intelligence as a constructivist scaffold in anaesthesia education: a mini review (2026) · Frontiers in Education · doi

  The field needs studies that isolate the incremental effect of AI rather than testing a technology bundle. There is a lack of evidence on the effectiveness of AI in improving clinical reasoning, debriefing, competence, or entrustment in anaesthesia education. The paper identifies the need for research on the full scaffolding cycle, including answer-generating AI, contingent Socratic AI, and matched non-AI tutoring.

  generalstated research gapevidence 5/5
  Keywords: field needs studies isolate incremental effect rather testing

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