Research on the Design and Implementation of Intelligent Tutoring System Based on AI Big Model

• Published in: Proceedings of ... IEEE International Conference on Unmanned Systems (Online) pp. 1 - 6
• Main Authors: Shi, Wenjing, Nie, Zhuming, Shi, Yuhan
• Format: Conference Proceeding
• Language: English
• Published: IEEE 13.10.2023
• Subjects: AI big model; Analytical models; Computational modeling; Data models; Deep learning; Education; intelligent tutoring system; personalized learning; Real-time systems; Scalability
• DOI: 10.1109/ICUS58632.2023.10318499

This research aims to design and implement an intelligent tutoring system based on artificial intelligence (AI) big models to provide personalized tutoring and guidance to help students better understand and apply their knowledge in various subjects. The system uses AI big model technology to simulate the role of a teacher and provide customized learning support for each student by analyzing their learning data and behavioral patterns. For the system design, we use the deep learning algorithm of AI big model to build a student learning model by analyzing and processing student data. The model can provide personalized learning paths and teaching resource recommendations for students based on their learning characteristics and progress. In addition, the system provides real-time learning feedback and assessment to help students understand their learning progress and provide corresponding suggestions for improvement. In terms of system implementation, we adopt advanced AI big model technology and computing platforms to ensure the efficiency and scalability of the system. We also develop a user-friendly interface so that students and teachers can easily use the system and get the required tutoring and guidance. In summary, the intelligent tutoring system based on the AI grand model has an important potential for application in the field of education. Through personalized tutoring and guidance, the system can help students better understand and apply their knowledge in various subjects and improve their learning effectiveness and motivation. Future research can further explore the optimization and improvement of the system to meet the learning needs and teaching goals of different students.