Development of a Retrieval Model based Backend of a Tutoring Agent

Abstract

In today's rapidly evolving educational landscape, the need for interactive, accessible, and efficient learning tools has become increasingly paramount. Traditional methods of instruction often face limitations in providing personalized and immediate assistance to students, which can hinder their learning experience. This study proposes the development of an interactive web-based system designed to assist students by answering their questions and providing emotional engagement through an avatar interface. The system, implemented using Python, combines the functionalities of two primary models: the ChatterBot model for generating replies and the TextBlob model for performing sentiment analysis. The ChatterBot model was trained to respond accurately to a wide range of student inquiries, while the TextBlob model was utilized to detect and interpret the emotional tone of the students' inputs. A web application was built, and the sentiment analysis capability allows an avatar in the web page of the web application to display appropriate emotions, enhancing the interactive experience and making the system more relatable and engaging for students. The system was able to generate relevant and coherent replies based on student inputs and effectively perform sentiment analysis. The implementation of this system shows significant promise in the educational field by offering a resource for students to resolve their queries independently, thereby reducing reliance on teacher intervention and fostering a more autonomous learning environment. The successful development and deployment of this interactive educational system confirm that the objectives of the study have been achieved. Datei-Upload durch TIB