AI-assisted case-based learning and flipped classroom to improve clinical decision-making: a randomized controlled trial in reproductive medicine.
AI-assisted case-based learning and flipped classroom models may enhance clinical decision-making skills and learner engagement in reproductive medicine training.
Where it sits
this study against the rest of the cerebrolysin corpusSummary and findings
This randomized controlled trial evaluated the impact of an AI-assisted case-based learning and flipped classroom model on clinical decision-making in 50 obstetrics and gynecology residents. The intervention group showed significantly higher theoretical test scores, Mini-CEX assessments, and OSCE scores compared to the traditional lecture group. Participants also reported improvements in motivation, clinical reasoning, and self-directed learning.
Abstract
<h4>Background</h4>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.<h4>Methods</h4>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.<h4>Results</h4>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.<h4>Conclusions</h4>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.
Background
The study addresses the need for improved clinical decision-making training in reproductive medicine due to declining global fertility and increasing infertility rates. Traditional lecture-based instruction often falls short in developing these skills, prompting exploration of innovative educational strategies. This research investigates the efficacy of integrating AI with case-based learning and flipped classroom formats to enhance clinical reasoning.
Methods
A randomized controlled trial was conducted with 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 intervention included pre-class interactive case work and in-class discussions. Primary outcomes were post-course theoretical knowledge tests, Mini-CEX, and OSCE scores. Secondary outcomes included learner motivation, clinical thinking, self-directed learning, and perceived course effectiveness.
Results
The AI-assisted CBL+FC group achieved significantly higher theoretical test scores than the control group. They also demonstrated superior overall clinical competence in Mini-CEX assessments and higher OSCE total scores. Participants reported greater improvements in learning motivation, clinical reasoning, self-directed learning, and perceived course effectiveness.
Interpretation
The findings suggest that the AI-assisted CBL+FC model may offer a more effective approach to training reproductive medicine residents compared to traditional lectures. The statistically significant improvements in test scores and self-reported outcomes indicate potential clinical relevance, although the small sample size and single-site design limit generalizability. Further research is needed to confirm these results across diverse settings.
Key findings
- 50 obstetrics and gynecology residents participated.
- AI-assisted CBL+FC group had higher theoretical test scores than control.
- AI-assisted CBL+FC group showed superior Mini-CEX and OSCE scores.
- Greater improvements in learner motivation and clinical reasoning reported.
- 5-point Likert scale used for secondary outcomes.
Limitations
- small n=50
- single-site study
- short-term educational outcomes
- self-reported secondary outcomes