A Study on AI-FML Robotic Agent for Student Learning Behavior Ontology Construction

TL;DR

This paper introduces an AI-FML robotic agent designed for constructing student learning behavior ontologies in English language learning, integrating perception, computation, and cognition intelligence, and tested across Taiwan and Japan.

Contribution

It presents a novel AI-FML robotic agent architecture with cloud-based DNN computation for ontology construction in language learning environments.

Findings

Agents effectively analyze student learning behaviors.

Successful deployment on robot Kebbi Air in real-world settings.

Potential for enhancing human-machine co-learning in education.

Abstract

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.