Agentic education · Learning science

Actionable companion · Feedback & scaffolding

Learning with agency: Artificial Intelligence and Surgical Education in the UK: A Systematic Review of Current Use, Evidence Gaps and Future Directions

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.

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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): Age / stage not established. Verify the participant population and task before transferring the findings.

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

Artificial intelligence (AI) offers new opportunities to enhance surgical training through automated performance assessment, adaptive learning platforms, and AI-enabled virtual or augmented reality (VR/AR) simulation. Although global literature is expanding, the UK context differs in governance, procurement, and training structures. National initiatives, such as the Royal College of Surgeons of England (RCS) Future of Surgery (FOS) programme, have highlighted AI and extended reality as priorities for modernising surgical education. However, UK peer-reviewed evidence remains limited, with most work consisting of pilot studies and early feasibility assessments. This narrative review synthesises UK-specific applications of AI in surgical training, identifies current gaps, and proposes priorities for future research. Two independent reviewers conducted a focused search of peer-reviewed and grey literature, including RCS policy documents, to identify UK-based uses of AI in surgical training. Databases searched included PubMed, the Excerpta Medica database (Embase), Scopus, and Web of Science, supplemented by targeted screening of UK policy sources. Studies were included if they involved AI or AI-enabled technologies applied to surgical education, simulation, or assessment within the UK. Non-UK studies and articles focused solely on clinical (non-educational) AI applications were excluded. Data were synthesised on AI modality, educational outcomes, feasibility, and barriers. A Risk Of Bias In Non-randomized Studies - of Interventions (ROBINS-I)-aligned risk-of-bias assessment was performed. Results: UK literature comprises national policy reports and a small number of empirical pilot studies exploring AI-enhanced VR/AR simulation, AI-driven performance analytics, and early AI components within robotic training curricula. RCS policy documents consistently identify AI as a key element of future training reform. Empirical studies report feasibility, trainee acceptability, and construct validity but provide limited evidence of improvements in operative performance or patient outcomes. Most work is single-centre and exploratory, and significant barriers, including cost, faculty training, data-governance requirements, and variability in access across deaneries, remain. Discussion: The UK is at an early yet promising stage of adopting AI within surgical education. National policy momentum and the expansion of robotic programmes provide opportunities for coordinated integration. Collaboration between NHS education bodies, simulation centres, and technology developers could support standardised metrics and equitable access. However, robust multi-centre evaluation frameworks are required to determine educational effectiveness. Ethical considerations, including data privacy, algorithmic transparency, and the impact of automated feedback on trainee development, require careful attention. Conclusions: AI use in UK surgical training is emerging but currently driven largely by pilot studies and policy direction rather than high-quality outcome evidence. Major gaps include multi-centre validation, curriculum integration, standardised assessment frameworks, and equitable access to AI-enabled systems. Future UK research should prioritise structured validation studies and national coordination to define effective, scalable, and safe AI tools that can enhance surgical education across the NHS.

What kind of evidence is this?

Intelligent tutoring & adaptive learning · Evidence synthesis

Stage: Age / stage not established. 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.

Study methods · source passage

A focused literature search of English-written studies was conducted across multiple publication libraries, including PubMed, the Excerpta Medica database (Embase), Scopus, Web of Science and Google Scholar, searching for any publications up to October 2025. Screening followed Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines (Figure 1). The following search terms were used and were adjusted for differing indexing systems, allowing us to reach as many studies that were likely to be relevant for UK studies: “Artificial intelligence” OR “machine learning” OR “deep learning” OR “virtual reality” OR “augmented reality” AND (“surgical training” OR “surgical education”) AND (“United Kingdom” OR “NHS” OR “Royal College of Surgeons”.

Limitations and uncertainty · source passage

This review was limited by the small volume of published UK-specific data, with most evidence derived from pilot studies and policy reports rather than randomised control trials (RCTs). This potentially limits the conclusions of this review, as more data and research are needed. In particular, RCTs on a national scale across multiple deaneries would supply excellent evidence on a national scale of the feasibility of the integration of AI into current UK training curricula. The search was restricted to English-language sources and, therefore, may have missed non-English-based technologies and studies, pilot studies, and review studies, and therefore may result in the lack of comprehensiveness of the review. With respect to the included studies in this review, due to their heterogeneous nature, a direct comparison between studies may not be feasible and may therefore potentially weaken the conclusions of this review.

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.
  • Limit simultaneous instructions and unnecessary dialogue. Compare task performance and a suitable mental-effort measure; easier-feeling work is not automatically better learning.
  • 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. Treat the paper as background evidence until its population is verified in the full text. Do not assign an age-specific intervention or extend adult results to children. 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. Extract the participant ages or education stage, recruitment, task and comparator before selecting a local pilot. If these cannot be established, retain the paper for design discussion rather than age-band efficacy claims. 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

Ranj Bhakar; James Miller; Amelia Simenacz. Artificial Intelligence and Surgical Education in the UK: A Systematic Review of Current Use, Evidence Gaps and Future Directions. Cureus (2025-12-01). https://doi.org/10.7759/cureus.98231

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.