Personalized Engineering Education Model Based on Artificial Intelligence for Learning Programming
Abhiraj Singh Rathore, Adarsh Sharma, Massoud Massoudi
Abstract
Background: Personalisation is a critical element in the learning environment of students. This is one area that our educational system falls short. Students study at varying rates and in varying contexts, and this should be considered, which most institutions do not. We need a structure that enables students of diverse background to research and learn in their own unique manner, at their own speed, in order to grasp concepts and solve problems. Today, there is something that is gaining a lot of popularity. The future is artificial intelligence, and we agree that science holds the secret to resolving the majority of the world's problems. Result: This article provides a novel model of a system that utilizes artificial intelligence and machine learning algorithms to assist students in learning to program and creates a customized system for them. The model classifies students into beginner, intermediate, and proficient ranks using Bayesian networks. Students are helped to understand ideas by the use of tools such as flowcharts. Each level of students is provided with unique instruments and materials, and the goal is to raise the comprehension level of beginner and intermediate students to a point that they can compete with proficient students, who are provided with practice questions to further their learning. Additionally, skilled students have the ability to work in industry through the model's industry-academia partnership module. Conclusion: This paper proposes a technique that helps students in customized learning as well as in improving their critical thinking capacities using a multi-agent-based flowchart development tool. It serves as an absolute and a complete tutoring aid for students learning programming.