From leadership research to district action: The digital transformation of medical education collaborative governance in the era of smart education: an innovative management restructuring of medical talent cultivation systems

AI-assisted editorial implementation guide. Proposed district actions are separate from the original authors’ findings and recommendations. Independent peer review of this guide is not asserted.

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Evidence and transfer

Education-related source: verify which participants, institutions and outcomes were actually studied before district adoption. Source publication type: review-article. License and identity checks do not establish study quality or causality.

Original authors’ abstract, reproduced under the source license:

Abstract Smart education, supported by artificial intelligence (AI), big data, and virtual simulation, is reshaping medical education toward more flexible and collaborative models. However, persistent barriers—fragmented governance, limited data interoperability, and uneven resource distribution—continue to constrain effective collaboration between medical schools and teaching hospitals. This narrative review synthesizes recent developments in digital governance for medical education and proposes a four-layer framework comprising institutional foundations, cross-organizational mechanisms, platform-based technological support, and data-driven quality assurance. The review also highlights key implementation considerations, including interoperability standards for integrating educational and clinical data, staged technology adoption across different resource settings, and governance-oriented evaluation criteria for AI-enabled assessment (e.g., effectiveness, reliability, fairness, explainability, and human oversight). Finally, it discusses privacy, security, and algorithmic accountability challenges under different regulatory contexts. Overall, smart education may facilitate a transition toward more data-informed governance, while requiring context-sensitive implementation and robust ethical safeguards. Keywords: digital governance, intelligent assessment, learning analytics, medical education management, medical-education collaboration, smart education

Study methods

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Limitations

This study has several limitations. First, as a narrative review, the literature selection process may be subject to potential selection bias and does not follow a fully systematic review methodology. Second, the four-layer digital governance framework proposed in this study is conceptual in nature and has not yet been empirically validated through case studies or quantitative analyses. Future research could examine its applicability through empirical investigations in different institutional contexts. Third, the literature discussed in this review primarily reflects experiences from several representative regions, and governance models may vary across countries with different policy environments and levels of digital maturity. Therefore, the conclusions should be interpreted with consideration of regional differences.

A 90-day district inquiry

Days 1–15 · Appraise. Read the source methods, recruitment, comparator, outcomes and limitations. Record how the study setting differs from your district and which claim the design can support.
Days 16–30 · Design. Select one recurring district decision. Publish the decision owner, evidence requirements, consultation process and appeal route; test the process on a bounded low-risk decision. Agree participation, privacy protections, baseline and stopping rules before starting.
Days 31–60 · Observe. Collect the pre-agreed measures, keep a log of implementation changes and invite confidential voluntary feedback. Preserve existing support for staff and learners.
Days 61–90 · Decide. Track decision time, unresolved objections, documented reasons, appeals and access differences. Review whether the process changes who can participate and who carries the burden. Make a dated decision to stop, revise or extend the inquiry.

Board decision brief

Present the local problem, source setting, evidence limits, intervention, responsible owner, total cost, measured changes and uncertainty. Separate staff perceptions, implementation measures and student learning outcomes.

Attribution and reuse

Xiaze Zhang; Pei Shi. The digital transformation of medical education collaborative governance in the era of smart education: an innovative management restructuring of medical talent cultivation systems. Frontiers in Medicine (2026-04-24). https://doi.org/10.3389/fmed.2026.1781836

Prepared 2026-11-07. Source authorship and DOI are preserved. The abstract and source passages are attributed extracts; this editorial guide is offered under the same CC BY 4.0 license.