Personalizing homemade bots with plug & play AI for STEAM education

In this study, we propose a new framework for hands-on educational modules to introduce ideas in AI and robotics casually, quickly, and effectively in one package for beginners of all ages in STEAM fields. Today, courses on introductory robotics are found everywhere, from K-12 summer camps to adult continuing education. However, most of them are limited to learning basic skills on sensor-actuator interactions due to their limited time and can rarely introduce what recent exciting AI can do, such as image recognition. As a case study to demonstrate the idea of the framework, an educational module to create a toy car with a camera controlled by Raspberry Pi is introduced. Our approach uses both physical and digital environments. Participants experience running their toy cars on a physical track using a convolutional neural network (CNN) trained based on how participants drive cars in a virtual game. The tested idea can be extensible as a framework to many other examples of robotics projects and can make ideas of AI and robotics more accessible to everyone. A proposed AI model is trained to assimilate the participant's game-play style in a VR environment which will be later re-enacted by the physical robot assembled by participants. Through this approach, we intend to demonstrate the AI's ability to personalize things and hope to stimulate participants' curiosity and motivation to learn.