Agentic education · Learning science

Actionable companion · Motivation & engagement

From AI evidence to responsible practice: Case-based Learning and Artificial Intelligence-based Gamification to Improve Undergraduate Students’ Motivation for One Health and Climate Change: A Pilot Study

AI-assisted editorial implementation guide, separate from the original research. Proposed actions are not findings 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. Verify the participants, intervention and comparator before applying this study in a new setting.

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

The One Health approach (OHA) which entails the interconnectedness of human, animal, and environmental health is a critical concept for medical students. In this pilot study, we report the feasibility of a new format using a large language model, gamification elements and case-based learning (CBL) to teach medical undergraduates environmental medicine, including the OHA and climate change, and its effectiveness on students’ motivation using the intrinsic motivation inventory (IMI) scale. We found higher scores for the CBL format across all IMI subscales. Ordinal logistic regression analysis yielded that having the CBL format was significantly associated with higher IMI scores in every item (odds ratios, OR, between 1.65 and 2.69). Supplementary Information The online version contains supplementary material available at 10.1007/s40670-026-02706-7.

What kind of evidence is this?

Supporting AI learning science · Design requires full-text appraisal

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

A total of 382 IMI forms were collected during 30 out of a possible of 42 seminar modules, of which 215 forms were from 14 CBL modules (mean 15.4 forms per module) and 167 forms from 16 non-CBL modules (mean 10.4 per module). The gender of the participants was indicated as women on 232 forms (60.7%) and as men on 123 forms (32.2%), while 27 forms had no information regarding gender (7.1%). The mean age indicated on the forms was 22.8 years (SD 2.9).

Limitations and uncertainty · source passage

Nevertheless, there are also limitations to our study. Social desirability may have biased some students to indicate higher scores on the questionnaires after the CBL sessions compared to the non-CBL sessions, while novelty bias may have exerted a similar effect. Furthermore, we did not compare the CBL-format to a non-CBL format within the same topical sessions, but rather to other topics. Another bias may have been introduced by the authors of the cases being involved in the delivery of the specific modules. Furthermore, we obtained motivation at the end of the sessions without data on the long-term trajectory, learning outcomes and behavior, with students potentially completing multiple forms, depending on the number of non-CBL courses they took.

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.

  • Measure engagement separately from achievement. Check whether novelty-driven participation persists and whether it translates to independent performance.

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. 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.
  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 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

Cihan Papan; Esther Sib; Christiane Schreiber; Alexander D. Wollkopf; Marek Landsberg; Steffen Engelhart; Katharina Last; Timo Falkenberg; Carsten Felder; Dogus Darici; Tobias Raupach; Nico T. Mutters. Case-based Learning and Artificial Intelligence-based Gamification to Improve Undergraduate Students’ Motivation for One Health and Climate Change: A Pilot Study. Medical Science Educator (2026-04-06). https://doi.org/10.1007/s40670-026-02706-7

Companion prepared 2026-11-01. 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.