Mixed Reality Engineering Labs
Interactive MR simulations for technical education with AI-powered tutoring. Deployed on MR devices for students.
Impact
Students gained practical understanding of systems that are otherwise inaccessible or difficult to visualize physically. The AI tutor enabled deeper learning by providing contextual explanations during exploration.
Overview
This project focused on enabling hands-on learning for complex mechanical systems, including jet engines, using mixed reality and AI assistance. Students can explore intricate engineering systems with LLM-powered contextual explanations.
The Challenge
Engineering institutions face significant barriers in providing hands-on training with complex equipment. Systems like jet engines are expensive, dangerous, and difficult to access. The solution needed to:
- Simulate complex machinery like jet engines accurately
- Enable safe hands-on interaction through mixed reality
- Provide intelligent AI tutoring for contextual explanations
- Support intuitive exploration of components and workflows
- Make inaccessible systems visualizable and interactive
Objectives
- Build interactive MR models of engineering systems
- Integrate LLM-powered tutor for contextual explanations
- Enable intuitive exploration of components and workflows
- Provide practical understanding of otherwise inaccessible systems
- Support self-paced learning with AI assistance
Our Approach
We created high-fidelity MR simulations using Meta Quest with passthrough capabilities, allowing students to interact with complex engineering systems in their real environment. An AI tutor powered by OpenAI and Gemini provides real-time contextual explanations as students explore components and workflows.
Solution Delivered
- Interactive MR Models -High-fidelity simulations of engineering systems including jet engines.
- LLM-Powered AI Tutor -Contextual explanations powered by OpenAI and Gemini for on-demand learning support.
- Passthrough Mixed Reality -Real-world environment integration using Meta Quest passthrough.
- Component Exploration -Intuitive interaction with individual parts and workflows.
- Self-Paced Learning -Students explore at their own pace with AI guidance.
Technologies Used
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