Actionable companion · Assessment & reasoning

Learning with agency: Examining Learning Resources Used by Year 5 Medical Students for UKMLA Preparation: A Descriptive Case Study

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): Higher education. Health-professions learning must be distinguished from K–12 instruction.

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

Introduction The UK Medical Licensing Assessment (UKMLA), introduced in 2024/25, is a national requirement for all final-year UK medical students. This Case Study explores how Year 5 students at Kent and Medway Medical School (KMMS) prepared for both components of the UKMLA, the Applied Knowledge Test (AKT) and the Clinical and Professional Skills Assessment (CPSA). Methods An electronic survey containing Likert-scale, multiple-choice, and open-text questions was distributed to the Year 5 cohort (n = 64) after the AKT and before the CPSA. Thirty-one students (48%) responded. Resource use was ranked using weighted scores based on frequency. Fisher’s Exact Test was used to explore associations between demographic variables and resource preferences. Results Students relied heavily on third-party resources for AKT preparation, with PassMedicine achieving the highest weighted score (121). Other commonly used tools included Anki, Geeky Medics, and Zero to Finals. KMMS in-house materials were more commonly used for CPSA preparation. On average, students spent 80% of their preparation time using external resources. Demographic analysis showed older students more frequently used Essential Primary Care (p = .016) and Pastest (p = .030). Female students used internet-based resources more than males (p = .043), while younger students favoured Geeky Medics Flashcards (p = .041). ChatGPT was also used for explanations and personalised support. Conclusion Third-party tools dominate AKT preparation. Age and gender influence resource choices, and AI-based tools are emerging as study aids. To promote equitable access, medical schools should consider providing key third-party resources. Further research should evaluate how resource combinations impact UKMLA performance.

What kind of evidence is this?

Supporting AI learning science · Survey / observational study

Stage: Higher education. 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

This was a descriptive case study, using an eSurvey. It was conducted in April 2025, following the Year 5 students’ sitting of the UKMLA-AKT, but before they undertook their UKMLA Clinical and Professional Skill Assessment (CPSA). The survey was administered between the two UKMLA assessments, because more responses were expected than after the CPSA, when students would have moved on to a different part of the curriculum.

Limitations and uncertainty · source passage

The small sample size within a single institution limits generalisability of the results, however, the clear alignment with current literature on preparation for national licensing exams such as the USMLE, indicates that these results could be relevant for other UK-based medical schools.

Design the system before the pilot

Use this study to inform a learning mechanism or implementation question. It is not direct evidence that autonomous agents improve learning. Keep the proposed agent behavior separate from what the source actually tested.

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

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.
Days 16–30 · Design. Choose one disciplinary task. Require a first attempt, a documented AI consultation, a critique of the output and an independent explanation. Align the comparison group and scoring rubric with the same objective. Name an accountable owner and set the baseline, success threshold and budget before implementation.
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.
Days 61–90 · Decide. Blind-score reasoning and accuracy on a new task without AI. Distinguish model examination scores, student perceptions and actual learner gains; only the last directly tests educational benefit. 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

Soban Sadiq; Manfred Gschwandtner; Zachary Hollenberg; Claire Parkin. Examining Learning Resources Used by Year 5 Medical Students for UKMLA Preparation: A Descriptive Case Study. Medical Science Educator (2026-02-25). https://doi.org/10.1007/s40670-026-02665-z

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.