MORAD ESCAPE, a novel research-based escape room approach for evaluating research competencies of health professions students

Abstract

This study introduces MORAD ESCAPE, an innovative research-based escape room methodology designed to assess research competencies among health professions students. Traditional assessment methods for research skills often fail to capture the practical, applied nature of research conduct in real-world scenarios. MORAD ESCAPE addresses this gap by creating an immersive, gamified environment where students must apply research knowledge and skills to solve challenges and progress through the escape room. The approach evaluates multiple dimensions of research competency including literature review, study design, data analysis, ethical considerations, and scientific communication. Through puzzle-solving and collaborative tasks, students demonstrate their ability to integrate theoretical knowledge with practical research application. Pilot testing with health professions students showed high engagement levels and provided valuable insights into students’ research competency profiles. The escape room format offers a novel, engaging alternative to traditional written examinations while providing educators with rich, behavioral data on students’ research capabilities. MORAD ESCAPE represents a scalable approach to research competency assessment applicable across various health professions education contexts.

Publication
BMC Medical Education (Springer Nature)

Highlights

  • Innovative Assessment: Novel escape room approach for evaluating research competencies
  • Gamified Learning: Immersive environment increasing student engagement
  • Comprehensive Evaluation: Assesses multiple dimensions of research skills
  • Practical Application: Bridges theoretical knowledge with real-world research scenarios
  • Scalable Method: Applicable across various health professions education contexts

Status

Published/Accepted - BMC Medical Education, 2025

Key Competencies Assessed

  • Literature Review and Information Synthesis
  • Research Design and Methodology
  • Data Analysis and Interpretation
  • Research Ethics and Integrity
  • Scientific Communication
  • Collaborative Research Skills
Arman Asgharpoor Golroudbari
Arman Asgharpoor Golroudbari
Machine Learning Engineer

Machine Learning Engineer with 5+ years of experience in LLMs, transformer architectures, computer vision systems, and autonomous robotics.