Agentic education · Learning science

Actionable companion · Feedback & scaffolding

Learning with agency: The digital transformation of clinical teaching: a review of AI and data-driven quality assurance systems in medical education

AI-assisted editorial implementation guide, separate from the original research. Proposed actions are not claims or recommendations attributed to the source authors. No independent peer review of this guide is asserted.

Read original paperSearch paper agentCC BY 4.0

Evidence and context

Title and abstract screening; stage labels describe reported settings, not verified numerical ages. Design labels require full-text appraisal. Reported stage(s): Adult & professional learning. Health-professions learning must be distinguished from K–12 instruction.

Original authors’ abstract, reproduced under the linked source license:

Abstract Background The use of artificial intelligence (AI) and other data-driven technologies is changing how medical education and clinical teaching are conducted. The traditional model of learning will not allow us to keep up with the sheer amount of new knowledge being produced, the move to competency-based education, or the need for learning that can be scaled and tailored to fit individuals. To examine how AI is being used in clinical education and evaluate its impact on teaching, learning, assessment, and quality assurance. Methods A review of the literature was conducted to examine the applications of AI in clinical education, including adaptive learning, intelligent tutoring, virtual patients, simulation, automated assessment, and data-driven quality assurance. Results AI has been shown to enhance the engagement of learners, create flexible learning opportunities, provide timely feedback for information on a learner's progress, and provide ongoing objective assessment of clinical competency. The use of learning analytics and predictive modeling enables institutions to monitor the progression of the learner, identify learners at risk, optimize the design of their curriculum, and make evidence-based academic decisions. Future innovations in medical training environments could be possible through the use of digital twins, immersive simulation, and explainable AI. However, there remain significant challenges to the widespread implementation of these technologies, such as bias in algorithms, lack of transparency, issues related to data privacy, unregulated use, and the limitations of current systems. Research to date has demonstrated great variation across studies and has not provided longitudinal data to support any conclusions about the long-term impact of using AI tools. Conclusions Despite the great advantages offered by using AI-based technologies in clinical education, Successful integration will require robust governance frameworks, faculty development, equitable access, rigorous validation, and continuous evaluation to ensure ethical, effective, and sustainable implementation. Keywords: artificial intelligence, clinical teaching, digital health education, learning analytics, medical education, quality assurance, simulation

What kind of evidence is this?

Intelligent tutoring & adaptive learning · Evidence synthesis

Stage: Adult & professional learning. Screening labels describe reported context and study-design language. They are not a risk-of-bias rating or proof of effectiveness for an age band.

Limitations and uncertainty · source passage

Artificial intelligence, along with data-driven quality assurance systems, is changing how we train our Clinical and Medical Education Workforce by improving personalized learning, simulation-based training, and objective assessment of learners. Additionally, these technologies allow for ongoing performance monitoring, as well as optimization of the curriculum. The ethical implications and barriers to implementation must continue to be a concern. Future research on their potential to provide for long-term validation, scalability, and a standardized framework to allow for their responsible use will be necessary.

Design the system before the pilot

Document the learner model, mastery rule, hint sequence and adaptation signal. Compare adaptive routing with the same content in a non-adaptive sequence. Check whether early errors lead to persistent low-expectation pathways.

  • Give feedback on the learner’s reasoning, then fade hints. Record whether the learner can complete the next task without that scaffold.
  • Measure engagement separately from achievement. Check whether novelty-driven participation persists and whether it translates to independent performance.
  • Use common rubrics, blind scoring where feasible and a fresh unaided reasoning task. Document permissible assistance and avoid treating AI-detector scores as proof of misconduct.

A 90-day district pilot

  1. Days 1–15 · Appraise. Have the curriculum lead and a teacher read the methods and limitations. Record the study design, sample, comparator, outcomes and evidence gaps. Define a local question that the study can help inform.
  2. Days 16–30 · Design. Use a bounded job-relevant simulation or professional-development task with an expert supervisor. Require a traceable rationale and verification before any real-world action. Name an accountable owner and set the baseline, success threshold and budget before implementation.
  3. Days 31–60 · Test. Start with a small voluntary cohort, preserve an accessible non-AI route and require teacher review. Use synthetic or de-identified examples; approve data handling and tool terms through district processes.
  4. Days 61–90 · Decide. Assess independent performance on an unfamiliar simulation, retention after a delay, error severity and total review time. Do not equate a faster AI-assisted response with durable professional competence. Compare with baseline, document uncertainty and stop or revise if learning worsens, access is unequal or critical errors persist.

The board decision brief

Report the learning problem, evidence quality, participating population, total cost including review time, measured benefit, errors, access differences and remaining uncertainties. Make a dated recommendation to stop, revise or expand. A positive perception survey alone does not establish learning improvement.

Attribution and reuse

Kexin Zhao; Ningning Wang; Zhicai Xie. The digital transformation of clinical teaching: a review of AI and data-driven quality assurance systems in medical education. Frontiers in Public Health (2026-08-17). https://doi.org/10.3389/fpubh.2026.1915158

Companion prepared 2026-10-30. Original authorship and DOI are preserved. The abstract is an attributed source extract; the implementation plan is new AI-assisted editorial material, offered under the same CC BY 4.0 license. This guide has not undergone independent peer review.