Clues and cortices: A pilot study of efficient, low‐cost neuroanatomy escape room design using generative AI

Abstract

Neuroanatomy is notoriously challenging due to its complexity and abstract spatial relationships, often evoking anxiety and frustration among learners. Educational escape rooms, a form of game‐based learning (GBL), can promote engagement and learning but typically require significant faculty time and resources to design. This study describes an efficient, low‐cost approach for creating a neuroanatomy escape room using generative artificial intelligence (AI) and evaluates its impact on learner engagement and knowledge. Existing course‐aligned materials were transformed by ChatGPT 4.0 into riddles and crossword puzzles, which formed the basis of a four‐station escape room for a graduate‐level neuroanatomy course. Prior to and after the escape room, students completed pre‐ and post‐questionnaires containing perception items on a 5‐point Likert scale (5 = strongly agree, 1 = strongly disagree) and a 10‐item neuroanatomy knowledge quiz. A total of 14 students completed the AI‐generated escape room, with 11 providing matched pre‐ and post‐questionnaire data. Participants reported high engagement (Median = 5, IQR 5–5), high satisfaction (Median = 5, IQR 5–5), and would recommend it to their peers (Median = 5, IQR 5–5), fulfilling Level 1 (Reaction) of Kirkpatrick's training evaluation framework. Although students believed the escape room improved their understanding of neuroanatomy (Median = 4, IQR 4–5), average quiz scores did not change significantly (both pre and post = 6.4 ± 2.2, p  = 0.67), indicating limited knowledge gains. While cognitive gains were not demonstrated, this pilot study suggests that AI‐supported escape room development is a feasible, low‐burden approach to GBL that warrants further evaluation.