Designing Educational Escape Rooms with Generative AI: A Framework and ChatGPT Prompt Engineering Guide

This work introduces Room2Educ8, a design-thinking framework for creating educational escape rooms with the help of ChatGPT. Its central thesis is that structured prompting can lower the time and cognitive demands of designing these learning games, particularly for educators who lack game design experience. The framework moves through design stages from Empathise through Evaluate, with each stage paired with sample prompts, and it is illustrated through a worked example: a periodic table escape room aimed at first-year chemistry students. The authors position the approach as filling a gap in the literature, since no prior framework had combined large language models with educational escape room design.

The central claim is qualitative. The authors argue that ChatGPT reduces the burden of content creation, freeing educators to concentrate on learning objectives and user experience, while acknowledging that the model's outputs require substantial human refinement and judgment. Importantly, the study contains no empirical evaluation with students, and the authors identify this absence as a principal limitation, committing to address it in future research. Readers should therefore treat the framework's benefits as demonstrated in principle and illustration rather than validated through measured learning or engagement outcomes.

The scope is focused on the intersection of educational escape rooms, generative AI, and prompt engineering, with an emphasis on undergraduate science instruction as the illustrative context. The supporting literature spans escape rooms in education, the use of ChatGPT in teaching, and prompt engineering methods, though the reference base itself offers no original data or findings.