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The research investigates the design and development of a serious game to teach green building design and energy literacy in rural middle schools in the United States. The paper presents a pilot study, education mini-game development integrated with parametric BIM and energy simulations. The game scenario was built on the developed science curriculum modules in our funded research, teaching building energy technologies such as daylighting, artificial lighting, window configurations, building materials, solar panels, etc. The mini-game, Illumi’s World, presents a baseline science lab and a media library of typical public schools in the United States. The players have the opportunity to improve energy literacy in several ways: manipulating the building configurations and the energy options, reviewing energy costs and emission level changes, and monitoring the performance from the game dashboards. This paper presents background theory, curriculum design, the mini-game development framework, methods and tools for energy simulation and BIM visualization, and the findings and challenges.
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This content will become publicly available on March 26, 2026
Green Building Design Studio Game Development with Parametric Simulation and ML Prediction for Green Building Education in Rural Middle Schools
Abstract. Green building education prioritizes workforce development to promote high-performing and net zero building adoptions. However, the concept and principles of net zero and building energy have rarely been reflected in the curriculum and instruction of K-12 science education in the United States. This research investigates the design and development of simulation game development paired with a science curriculum to teach green building design and energy principles in rural middle schools. This paper presents our education game development aligned with the newly developed curriculum unit that will be distributed to science classrooms. Green Building Design Studio game was developed from the following research phases: (i) Game scenario design, (ii) Energy simulation module creation, (iii) ML-prediction model development, and (iv) Cost estimation module creation. In ML prediction, the XGBoost algorithm demonstrated reliable performance and accuracy. The game was tested in a 3-day science immersion summer camp with twenty-seven middle school students in Missouri. The research team observed that the game enabled students to iterate de sign changes and promptly see the updated results from the dashboard. This paper describes the game development framework, methods and tools for energy simulation, ML prediction, and game development, as well as the findings and challenges.
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- Award ID(s):
- 2201204
- PAR ID:
- 10599179
- Editor(s):
- CAADRIA
- Publisher / Repository:
- ARCHITECTURAL INFORMATICS, Proceedings of the 30th International Conference of the Association for Computer-Aided Architectural Design Research in Asia (CAADRIA) 2025, Volume 1, 479-488. © 2025 and published by the Association for Computer-Aided Architectural Design Research in Asia (CAADRIA), Hong Kong
- Date Published:
- Subject(s) / Keyword(s):
- Green building education game-based learning energy simulation ML prediction
- Format(s):
- Medium: X
- Location:
- https://www.caadria2025.org/paper-sessions
- Sponsoring Org:
- National Science Foundation
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