A Study on AI-FML Robotic Agent for Student Learning Behavior Ontology Construction
This is an incremental application of existing AI and robotic methods to enhance student learning behavior analysis in a specific educational domain.
The authors proposed an AI-FML robotic agent to construct an ontology for student learning behavior in English speaking and listening, deploying it on a robot and cloud with DNN, and tested it in Taiwan and Japan, showing it can be used in human-machine co-learning for education.
In this paper, we propose an AI-FML robotic agent for student learning behavior ontology construction which can be applied in English speaking and listening domain. The AI-FML robotic agent with the ontology contains the perception intelligence, computational intelligence, and cognition intelligence for analyzing student learning behavior. In addition, there are three intelligent agents, including a perception agent, a computational agent, and a cognition agent in the AI-FML robotic agent. We deploy the perception agent and the cognition agent on the robot Kebbi Air. Moreover, the computational agent with the Deep Neural Network (DNN) model is performed in the cloud and can communicate with the perception agent and cognition agent via the Internet. The proposed AI-FML robotic agent is applied in Taiwan and tested in Japan. The experimental results show that the agents can be utilized in the human and machine co-learning model for the future education.