Educational Robotics
AI Robotics Research — Child-safe, school-local robotic teaching assistants powered by sovereign AI models. Every interaction stays inside the school district.
Research Focus
This research area investigates how locally-deployed AI models can power robotic teaching assistants in K-12 classrooms. The core challenge: robotic educational companions must interact safely with children, align with curriculum standards, and operate entirely within school infrastructure — with no student interaction data ever leaving the district network.
Key Research Questions
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Child-Safe Robotic Interaction
Can AI models running on robotic hardware maintain child-safe interaction boundaries in real-time, including content filtering, conversation scope enforcement, and emotional state detection?
- Real-time content filtering on embedded hardware
- Conversation scope enforcement (educational topics only)
- Emotional state detection (frustration, confusion, distress)
- Teacher alert system for intervention triggers
- Hard-coded safety layer (AI cannot override)
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Curriculum-Aligned Autonomous Learning
Can locally-trained AI models guide students through curriculum-aligned learning pathways, adapting to individual pace and learning style without cloud-based personalization?
- Local fine-tuning on district curriculum materials
- Socratic guidance patterns (not direct answers)
- Multi-modal learning style adaptation
- Progress tracking with explainable recommendations for teachers
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School-Local Robotic Deployment
Can educational robotics AI systems be deployed and maintained within typical school district IT infrastructure constraints — limited budget, limited IT staff, potentially unreliable connectivity?
- Single-server deployment for small districts
- Offline-capable operation (no internet required)
- Automated model updates via local pipeline
- Low-resource quantized models for embedded robotics compute
- Integration with existing LMS platforms
Technical Approach
- Model training: Curriculum-specific LoRA fine-tuning on NVIDIA CUDA GPUs
- Model deployment: Quantized models on embedded robotics compute (target <4GB VRAM)
- Safety architecture: Hard-coded safety boundary layer separate from AI model — the AI can recommend, but cannot override safety constraints
- Teacher control: Real-time monitoring dashboard with pause/resume/redirect capabilities
- Data sovereignty: All student interaction data stays on school-district servers. Zero external transmission.
Alignment with Parent Division
This robotics research is a sub-section of the K-12 Educational AI division. It extends the division's research into physical robotics — translating child-safe AI models into classroom robotic teaching assistants.
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