Personalized Hands-on Training Via a Hybrid Intelligent Teacher System

Abstract

In this paper, we propose a student-centered hybrid intelligent teacher system, and show how this system advances in a case study in teaching robotics. The key innovation of this system is to focus on the combination of a human teacher and a cloud teacher, which is a textual-based conversational agent for answering the questions from students in Mechanical Engineering via machine learning technologies. The basic underlying idea is to train the agent by utilizing open-source artificial intelligence tools from Google’s DeepMind, such that the agent can understand and answer the questions raised by students with an acceptable confidence value. Specifically, it is expected to customize a training database combining VEX hardware assembling, Auduino and Solidworks programming. Moreover, Big Data Analytics can be conducted accordingly based on students’ historical questions. An Android-based mobile application is produced. As a result, students can raise their questions conveniently and get instantaneous feedback with a 24-7 service. The method has demonstrated its effectiveness in a university general education course (UGEB2303 Robots in Action).