Generative AI · Education leadership

Actionable companion · Assessment and evidence of learning

From evidence to district action: Can students identify AI? – A cross-sectional quantitative study about AI recognition in tablet-based MCQ assessment among fifth-year undergraduate medical students at Saarland University, Germany

AI-assisted editorial implementation guide. The district actions are proposed applications, not findings or recommendations attributed to the original authors. No additional peer review is asserted.

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Evidence and context

Health-professions education context: test transfer to K–12 rather than assuming it.

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

Background Beyond technical feasibility of Artificial Intelligence (AI)-generated multiple-choice questions (MCQs), their educational value in assessment remains unclear. This study aims to evaluate whether students can distinguish between AI-MCQs and National Licensing Exam (NLE) questions in an exam setting and if they align with the curriculum. Methods In this cross-sectional study 119 year five medical students completed a Family Medicine MCQ tablet-based exam. Participants answered 30 AI-generated and 30 NLE MCQs. AI questions were generated from digital learning materials using ChatGPT-4o and Gemini 1.5 Pro. Experts accepted 82% of the questions, with minor edits to retained items and removing those needing major changes. During the exam, students were asked about the question source and whether each item aligned with the course curriculum. Statistical analyses were obtained using Jamovi 2.3.28.0. Results No significant difference in correct attribution of AI or NLE MCQs (t(29.6) = -1.24, p = .225; t(29.6) = 1.18, p = .246) was observed. No significant correlation was found between item difficulty and recognition (τ_b: p = .534). Distractor distributions did not differ across ChatGPT, Google Gemini and NLE (χ2(2) = 2.61, p = .271, U p > .05 for all comparisons). Item difficulty did not differ significantly between AI and NLE items; however, exploratory source-specific analysis showed an overall difference among ChatGPT, Google Gemini, and NLE items (χ²(2) = 6.71, p = .035), with a difference between Google Gemini and NLE items (p = .028). Student-perceived curricular alignment did not differ significantly between AI and NLE or among ChatGPT, Google Gemini, and NLE. Conclusions Students’ recognition did not differ between AI-generated MCQs and NLE MCQs. Easier MCQs are generally perceived as more aligned with the curriculum. AI-MCQs may provide a feasible approach to item drafting within a structured human-review process. Supplementary Information The online version contains supplementary material available at https://doi.org/10.1186/s12909-026-10410-8.

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. Redesign one low-stakes assessment: require a first attempt, an AI-assisted revision, a source-check log and a short oral explanation. Use the same rubric for the comparison task. 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. Blind-score reasoning, factual accuracy and unaided transfer on a fresh task. Record suspected misconduct separately from demonstrated evidence. 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

Philip Vogt; Nadine Wolf; Sandra Jordan; Sara Volz-Willems; Johannes Jäger; Fabian Dupont. Can students identify AI? – A cross-sectional quantitative study about AI recognition in tablet-based MCQ assessment among fifth-year undergraduate medical students at Saarland University, Germany. BMC Medical Education (2026-09-24). https://doi.org/10.1186/s12909-026-10410-8

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.