Actionable companion · Self-regulation & metacognition
Learning with agency: AI-assisted case-based learning and flipped classroom to improve clinical decision-making: a randomized controlled trial in reproductive medicine
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:
Abstract Background Efficient training of reproductive medicine clinicians is critical in the context of declining global fertility and increasing infertility. Traditional lecture‑based instruction often fails to sufficiently develop clinical decision‑making skills within limited residency rotations. Innovative strategies that integrate artificial intelligence (AI) with case‑based learning (CBL) and flipped classroom (FC) formats may enhance clinical reasoning, however rigorous evidence in reproductive medicine education remains limited. Methods We conducted a randomized controlled trial involving 50 obstetrics and gynecology residents at the First Hospital of Jilin University. Participants were randomly assigned to an AI‑assisted CBL+FC group or a traditional lecture control group. The AI‑assisted CBL+FC group completed pre‑class interactive case work with virtual standardized patients on the DoctorU platform and case analyses on the Superstar Learning platform, followed by interactive in‑class discussions. Primary outcomes included post‑course theoretical knowledge tests, Mini‑Clinical Evaluation Exercise (Mini‑CEX), and Objective Structured Clinical Examination (OSCE) scores. Secondary outcomes assessed learner motivation, clinical thinking, self‑directed learning, and perceived course effectiveness using a 5‑point Likert scale. Results Baseline characteristics were comparable between groups. After the intervention, the AI‑assisted CBL+FC group achieved significantly higher theoretical test scores than the control group. The AI‑assisted CBL+FC group also demonstrated superior overall clinical competence in Mini‑CEX assessments and higher OSCE total scores. Participants in the AI‑assisted CBL+FC group reported greater improvements in learning motivation, clinical reasoning, self‑directed learning, and perceived course effectiveness. Conclusions The AI‑assisted CBL+FC instructional model significantly enhances theoretical knowledge, clinical decision‑making skills, and learner engagement among reproductive medicine residents. This blended learning model offers an efficacious and generalizable methodology for training practitioners to address the evolving clinical requirements within contemporary fertility care.
What kind of evidence is this?
Supporting AI learning science · Evidence synthesis
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 randomised controlled trial was conducted between September 2025 and December 2025, in the Department of Obstetrics and gynaecologist, First Hospital of Jilin University. Eligible participants were resident physicians who had completed theoretical and clinical rotations in physiology, histology and embryology, internal medicine, surgery, obstetrics and gynaecologist, and paediatrics, in accordance with the national standardised residency training curriculum. All participants were actively enroled in the standard residency programme in Obstetrics and gynaecologist at the time of study initiation. Participants were randomly allocated by lot drawing into either an AI assistied CBL + FC group or a control group.
Limitations and uncertainty · source passage
In conclusion, AI-assisted CBL integrated with the FC model demonstrates clear advantages in enhancing clinical decision-making capabilities, learner engagement, and educational efficiency among physicians in assisted reproductive medicine. This blended, technology-enabled strategy not only addresses key limitations of conventional teaching, but also offers a scalable blueprint for innovation across other medical specialties. With continual refinement and broader implementation, this approach holds considerable promise for shaping the next generation of reproductive medicine specialists, ensuring they are equipped with the cognitive flexibility, ethical awareness, and interdisciplinary expertise required to meet the complex demands of contemporary fertility care.
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 plan–attempt–check–reflect prompts and compare confidence with actual performance. Keep learner choice and reflection visible.
- Measure engagement separately from achievement. Check whether novelty-driven participation persists and whether it translates to independent performance.
- 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
Qi Wang; Cong Hu; Yiyang Li; Ting Zhang; Songling Zhang. AI-assisted case-based learning and flipped classroom to improve clinical decision-making: a randomized controlled trial in reproductive medicine. Medical Education Online (2026-05-27). https://doi.org/10.1080/10872981.2026.2670047
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.