Critical thinking gamification in medical education
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,005 | 0,013 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,003 |
| Communication savante | 0,004 | 0,002 |
| Science ouverte | 0,001 | 0,004 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,010 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».