Critical thinking gamification in medical education
Bibliographic record
Abstract
BACKGROUND: Advancements in health and medical education are key to a promising future, but sustaining their exponential pace requires innovative approaches. Gamification offers a powerful tool to foster engagement and enhance the educational journey of medical professionals. By integrating interactive and motivational elements into training, gamification not only boosts knowledge acquisition but also addresses the monotony of traditional methods, encouraging deeper cognitive engagement and reducing errors in practice. Critical thinking is essential for accurate and timely medical decisions, particularly in diagnosing new patients at emergency department. Enhancing these skills can significantly reduce errors and improve patient outcomes, including lowering mortality rates. METHODS: This project introduces a critical thinking game designed to improve the clinical reasoning skills of pediatric students before they enter the field. The game simulates realistic clinical cases, where students draw randomized cards presenting patient profiles, symptoms, and test results, then race against the clock to deliver most relevant diagnoses and actions. The game employs a Case-Based Morning Report format designed to provide hands-on learning experiences. The gameplay involves three cards presented to students, each containing different types of medical information: a patient's description, their symptoms, and their examination results. Based on this data, students must create a diagnostic scenario and discuss it with a licensed pediatrician, who serves as the moderator. The moderator ultimately determines the best scenario based on logical reasoning and medical accuracy. The moderator is always an expert in the domain and a university professor, which already ensures a high level of reliability. In addition, we conducted a single-arm, exploratory pilot study with undergraduate medical students (n = 100) from first to fourth year (Med1-Med4) at the Holy Spirit University of Kaslik (USEK). Participants engaged with the DMRCT platform during scheduled sessions. After gameplay, they completed a structured Likert-scale survey measuring perceived critical thinking improvement, engagement, interface usability, and competitive pressure. Objective gameplay metrics (reaction time, diagnostic accuracy, number of errors) were recorded automatically by the platform's backend. This problem-based learning approach fosters excitement, interactivity, and competitiveness, transforming critical training into an engaging experience. RESULTS: Our multiplayer educational game is hosted on a web-based platform, enabling students to engage in critical thinking exercises remotely. The participants in each session consist of two opposing teams of medical students, who analyze and discuss the case information before presenting their scenarios. The moderator evaluates the scenarios and awards points based on the accuracy and reasoning presented. Our critical thinking game was tested with 100 medical students (Med1-Med4) through randomized clinical scenarios. Feedback revealed that students viewed the game as a valuable complement to traditional morning rounds, enhancing diagnostic synthesis, problem-solving, and decision-making, particularly for advanced learners. Students reported increased motivation, teamwork, and the ability to practice decision-making in a low-risk environment. Technical evaluations confirmed the platform's reliability, real-time scoring, and analytics integration, with pilot sessions showing active participation, multiple valid diagnoses, and meaningful moderator-student interactions that deepened clinical reasoning skills. CONCLUSION: By immersing students in dynamic, field-relevant scenarios, our critical thinking game enriches analytical reasoning, problem-solving abilities, and clinical judgment. Transforming education into an interactive, game-based experience cultivates skilled, confident practitioners prepared to meet the complex challenges of real-world pediatric care. The pilot evaluation among medical students revealed high engagement, improved diagnostic accuracy, and a positive perception of clinical reasoning development.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".