Medical education increasingly relies on digital tools to enhance training and assessment. This work presents a chatbot-based simulation game designed to improve diagnostic reasoning skills in medical students. The system allows educators to configure clinical cases through an editable Excel file, defining possible questions, responses, scoring criteria, and evaluation constraints. Students interact with a virtual patient, simulating real-world diagnostic processes by asking relevant questions and receiving predefined responses based on clinical scenarios. The game dynamically evaluates the student’s decision-making process, assigning scores based on question relevance and diagnostic accuracy.

This interactive approach promotes active learning, critical thinking, and self-assessment, offering a flexible and scalable tool for medical training. By enabling instructors to customize cases, the system ensures adaptability to different medical specialties and complexity levels. Future developments will explore AI-driven responses and the integration of advanced analytics to assess students’ diagnostic patterns and improve feedback mechanisms.

Keywords: Medical education, serious games, clinical simulation, chatbot, interactive learning.