Teacher-guided Autonomous Learning Enabled by Artificial Intelligence Empowered Remote Experiment Platform
Rentao Gu, Ziyi Xi, Boyang Lin, Yuefeng Ji · 2022 IEEE Global Engineering Education Conference (EDUCON) · 2022
With the rapid development, artificial intelligence (AI) technology may occupy the job positions which require only simple knowledge. Autonomous learning ability has been one of the core abilities that needs to be cultivated in engineering education. However, how to leverage students’ autonomous learning in the practice session of curriculum is still facing several challenges. In view of these challenges, we develop an AI empowered remote experiment platform, which has independent intelligent algorithm center for students to deploy their own algorithms into the real experiment scenarios. Also, in this platform, there is a digital-twins engine to give real-time feedback to the students, helping them to improve their algorithms and even the whole projects. Based on this experiment platform, we propose a teacher-guided autonomous learning practice teaching mode, including autonomous goal setting, autonomous practice process and autonomous feedback optimization, in which the teachers will be a guide to guarantee the learning objective is achieved. A survey was conducted, showing that under this practice teaching mode, students have a deeper understanding of theoretical knowledge, more obvious cultivation of autonomous learning ability, and higher satisfaction with the course.