Implementation of Artificial Intelligence in engineering teaching and learning

Abstract

This paper is concerned with increasing the efficiency of teaching and learning in engineering by applying artificial intelligence (AI) techniques and technologies. An approach to realize the personalized intelligent tutorial system (ITS ) is propose, which would be more oriented to the specifics of engineering teaching. The key-idea is to create a subsystem for generating personalized tasks to students in the form of a decision module based on binary and fuzzy logic rules. Intelligent tutoring systems are characterized by storing three types of knowledge base: a) domain knowledge, b) learner knowledge, and c) pedagogical knowledge. It is these types of knowledge that also determine the three main parts of the ITS architecture: a) domain knowledge creation/development applications, learner knowledge assessment modules and pedagogical knowledge creation/development modules. The scheme of an ITS with the information flow in this system is proposed, in which the key component of the ITS is the intelligent generator of personalized tasks for each student. This module is a dedicated tool that generates recommended tasks for each student on what they need to do next. The ITS recommends topics and learning resources based on previous long-term performance and the student's psychological profile.