We are developing a compact, AI-powered robot tutor designed for personalized and emotionally intelligent coding education. The robot integrates a multi-modal large language model (LLM) with voice, vision, and behavior recognition capabilities, allowing it to adapt to students’ learning styles and emotional states in real time. Featuring a modular 3D-printed structure, the robot is lightweight, portable, and customizable. It is paired with an online learning management platform and coding environment, creating an interactive, hands-on education ecosystem. This solution aims to enhance student engagement, promote practical problem-solving skills, and make coding education more accessible and personalized.
How it is being developed
Robot Hardware: Custom-designed, 3D-printed modular structure based on lightweight materials.
Actuation: Compact 30 mm brushless servo motors with integrated encoders and CAN communication.
Computing Core: Embedded system using Raspberry Pi / Jetson Nano for edge AI processing.
AI Integration: Multi-modal large language model (LLM) fine-tuned for real-time dialogue and emotional recognition.
Sensors: IMU, microphone, and camera modules for multi-sensory interaction.
Learning Platform: Web-based coding environment with adaptive learning management system (LMS).
Brief Introduction
We are developing a compact, AI-powered robot tutor designed for personalized and emotionally intelligent coding education. The robot integrates a multi-modal large language model (LLM) with voice, vision, and behavior recognition capabilities, allowing it to adapt to students’ learning styles and emotional states in real time. Featuring a modular 3D-printed structure, the robot is lightweight, portable, and customizable. It is paired with an online learning management platform and coding environment, creating an interactive, hands-on education ecosystem. This solution aims to enhance student engagement, promote practical problem-solving skills, and make coding education more accessible and personalized.
How it is being developed
Robot Hardware: Custom-designed, 3D-printed modular structure based on lightweight materials.
Actuation: Compact 30 mm brushless servo motors with integrated encoders and CAN communication.
Computing Core: Embedded system using Raspberry Pi / Jetson Nano for edge AI processing.
AI Integration: Multi-modal large language model (LLM) fine-tuned for real-time dialogue and emotional recognition.
Sensors: IMU, microphone, and camera modules for multi-sensory interaction.
Learning Platform: Web-based coding environment with adaptive learning management system (LMS).