One challenge is the potential for cognitive overload

The gap

Evidence profile

Sourced from the stated challenges and limitations and future work and inline gaps of the source papers, classified as general, drawn from work published between 2025 and 2026, spanning 7 journals. Those papers have been cited 27 times in total.

Supporting evidence — 8 representative gaps

• From AI use to critical thinking among medical students: a moderated mediation perspective on cognitive load and self-regulated learning (2026) · Frontiers in Psychology · doi

  One challenge is the potential for cognitive overload when using AI-based educational technology. Another challenge is the need to develop effective strategies for integrating AI literacy and self-regulated learning into medical education. A third challenge is the potential for individual differences in self-regulated learning abilities to influence the effectiveness of AI-based educational technology.

  generalstated challenges
  Keywords: one challenge potential cognitive overload using ai-based educational
• Artificial Intelligence in Health Professions Education: Qualitative Study of Student Experiences (2026) · Journal of Medical Internet Research · doi

  One of the limitations of this study is the small sample size and the use of self-reported data, and the fact that the data were collected within a single establishment, which limits the generalization of the results to other institu- tions. This may have introduced biases related to memoriza- tion accuracy or social desirability. Although the research team assessed the questionnaire for relevance and clarity, psychometric procedures were not sufficiently applied to assess it. Therefore, interpretation biases may arise, particu- larly regarding the understanding of porous concepts such as “learning” or “learning support.” Furthermore, health professions students were the focus of this study; the application of AI in training may vary by discipline. In addition, this transition in data collection, from semistruc- tured interviews to online surveys, may have reduced the depth and richness of some responses, which could affect the identification of more subtle themes. Therefore, future research could address these limitations by conducting larger-scale studies with more diverse samples and using objective measures of AI usage, such as usage logs or performance data. Additionally, future research could explore how AI is used in other fields of study and whether the patterns observed in this study are generalizable to different educational contexts. Moreover, as AI tools continue to evolve, it will be important to track how students’ perceptions and utilization habits change over time.

  generallimitationsevidence 5/5
  Keywords: limitations biases learning students future usage small sample size self reported fact collected within single
• 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
• 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.

  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.

  generalfuture workevidence 5/5
  Keywords: education https medical philosophy future medicine intelligence artificial developing nation context tong long application problem
• A Structural Design Model for an AI-Centered Convergent Education Platform under a Glocal University Strategy (2026) · Asia-pacific Journal of Convergent Research Interchange · doi

  Contextual design input The analytical procedure consisted of six stages. First, institutional data were reviewed to identify the Glocal University strategy, AI-medical convergence direction, institutional resources, and faculty capacity. Second, internal faculty consultation was conducted to examine curriculum restructuring, interdepartmental coordination, syllabus workload, and faculty readiness. Third, external advisory input was reviewed to identify field applicability, healthcare needs, industry expectations, and technology relevance. Fourth, literature and theory were reviewed to map AI education, digital health competency, platform theory, digital transformation, learning sciences, knowledge management, and micro-credentials to design elements. Fifth, the framework was developed by deriving the AI Core Model, AI Core Platform, Domain-Specific Tracks, Artifact Repository, and Evidence-Based Certification. Sixth, the framework was theoretically and institutionally refined through institutional alignment, curricular coherence, competency relevance, strategic viability, and feedback-based refinement. This terminology was used to avoid implying empirical validation. The refinement process was conceptual and institutional in nature; it did not include Delphi review, pilot implementation, expert evaluation, or statistical outcome testing. [Fig. 1] Six-Stage Methodological Flow of the Conceptual Case-Based Design Study [Fig. 1] illustrates the six-stage methodological flow of this conceptual case-based design study. The final stage was revised as theoretical and institutional refinement to clarify that the framework was refined through theoretical coherence and institutional fit, not through empirical validation. 3.4 Design Principles and Refinement Criteria Based on the materials and analytical procedure, six design principles were derived. These principles functioned as refinement criteria for the proposed platform, not as empirical validation criteria. [Table 3] presents the design principles and refinement criteria used in this study. Common structuring means that students from different disciplines do not need identical AI content but need a shared AI problem-solving structure. Preservation of disciplinary autonomy means that each discipline 46 Copyright ⓒ 2026 KCTRS A Structural Design Model for an AI-Centered Convergent Education Platform under a Glocal University Strategy maintains its expertise while connecting to a common platform. Artifact-based accumulation means that learning outcomes are stored as reusable artifacts. Evidence-based certification means that competency is certified through portfolios and rubrics rather than simple course completion. Fieldoriented linkage means that education should connect to hospitals, industry, local communities, and digital health practice. Sustainable institutionalization means that the platform should be embedded in academic structure, repository systems, evaluation mechanisms, and certification pathways.

  generalfuture workevidence 5/5
  Keywords: design based refinement institutional platform means principles criteria reviewed faculty education digital competency framework certification
• 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

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