Actionable companion · Assessment & reasoning
Learning with agency: Uptake of Large Language Models by London Medical Students: Exploratory Qualitative Interview 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.
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:
Background The popularity of large language models (LLMs) has grown exponentially across health care. Despite the wealth of literature on proposed applications in medical education, there remains a critical gap regarding their real-world use, benefits, and challenges as experienced by medical students themselves. Objective We aimed to explore qualitatively and characterize the perceived benefits, facilitators, and barriers associated with the use of LLMs among a cohort of London-based medical students. Methods Semistructured interviews were conducted with 15 medical students from preclinical and clinical stages at London-based medical schools. Guided by the technology acceptance model, interview transcripts underwent an inductive thematic analysis to identify themes on actual system use, perceived usefulness, ease of use, and attitudes toward LLMs. Results All participants reported frequent use of ChatGPT for concise topic summarization, clarification of complex concepts, generation of examination-style questions, and summarization of research. Students described LLMs as a complementary tool to traditional materials, valuing their immediacy (“Instead of getting a textbook, I can ask ChatGPT to summarise something in X words and read it in under a minute”) and ease of use. Peer demonstration and device-agnostic accessibility emerged as key facilitators. Of note, wider applications such as simulating clinical interviews were discovered through peers rather than through formal teaching. Significant barriers were reported. Hallucinations, fabricated references, and outdated information led to loss of trust, with more junior students finding inaccurate outputs difficult to detect (“I stopped using it because I found it to be inaccurate, and I don’t want to be learning the wrong things”). Half of the participants interviewed reported a sense of overreliance, defaulting to its use for answers with a perceived loss of critical thinking ability. Students noted inequalities in access to advanced features and voiced concerns about privacy when using LLMs in clinical scenarios. Conclusions LLMs have been widely adopted by medical students. While students perceived the efficiency, flexibility, and conversational interface of LLMs as beneficial, substantial reservations remain regarding their reliability, potential de-skilling, and the loss of academic integrity. These findings underpin the urgent need for curricula to both support safe LLM use and also adapt assessment and teaching strategies for artificial intelligence–augmented student practice. Future research should broaden geographical representation, investigate applications in low-resource settings, and integrate educators’ perspectives to establish future curricular guidance in an artificial intelligence era.
What kind of evidence is this?
Supporting AI learning science · Qualitative / mixed methods
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
We selected semistructured interviews for data collection as they allowed for in-depth exploration of participants’ attitudes, beliefs, and behaviors regarding LLM use. These are objectives not easily achieved using structured interviews. The flexibility of the semistructured format enabled the interviewer to probe participant responses, clarify ambiguities, and follow lines of inquiry, therefore allowing for richer and more context-dependent responses. The semistructured interview approach is particularly well suited to exploring complex or emerging phenomena, such as the adoption of novel technologies in education, and is endorsed by qualitative research best practices for trustworthiness and depth [15]. A limitation of this approach is the potential for reduced consistency across interviews and increased reliance on interviewer skill, which we mitigated through a standardized topic guide (Multimedia Appendix 1).
Limitations and uncertainty · source passage
This study has several limitations. First, all participants were recruited from only 2 medical schools located in London, and therefore, the findings of this study should be interpreted as locally specific and exploratory. Due to the convenience sample and restriction to a single metropolitan area, our results cannot be generalized beyond the surveyed London cohort, and we acknowledge that claims of saturation may be limited to this cohort.
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.
- Co-design accommodations with learners and specialists. Check access and error burden across groups without inferring subgroup effectiveness from small or unreported samples.
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
Maya Alazzawi; Kyle Lam. Uptake of Large Language Models by London Medical Students: Exploratory Qualitative Interview Study. JMIR Formative Research (2026-01-19). https://doi.org/10.2196/82828
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.