Research on Personalized Education Recommendation Algorithm Based on Artificial Intelligence

• Published in: 2023 International Conference on Applied Physics and Computing (ICAPC) pp. 531 - 535
• Main Author: Lei, Liu
• Format: Conference Proceeding
• Language: English
• Published: IEEE 27.12.2023
• Subjects: artificial intelligence; Big Data; Collaboration; Deep learning; Education; educational research; Knowledge graphs; Learning (artificial intelligence); neural networks; recommendation systems; Semantics
• DOI: 10.1109/ICAPC61546.2023.00104

With the continuous development of artificial intelligence technology, the field of education has also ushered in a new era of personalized education. With the theme of "Research on Personalized education Recommendation Algorithm based on Artificial Intelligence", this study aims to explore how to use advanced artificial intelligence technology to provide students with more personalized and accurate education recommendation services. Firstly, we deeply analyze the problems existing in the traditional education system, including the one-size-fits-all general teaching mode, the single presentation of subject knowledge and the neglect of students' learning interests. To solve these problems, we propose a solution to introduce personalized education recommendation algorithm to better meet the diversified learning needs of students. In terms of research methods, we have adopted artificial intelligence technologies such as deep learning and combined with big data analysis to build an intelligent education recommendation system. Based on students' subject interests, learning history, learning style and other multi-dimensional information, the system uses recommendation algorithms to customize teaching content for each student, and provides personalized learning paths and resource recommendations. The experimental results show that the personalized education recommendation algorithm based on artificial intelligence has achieved remarkable results in improving students' learning effect and stimulating their learning interest. After using the personalized recommendation system, students were not only more actively engaged in learning, but also improved their academic performance and satisfaction. In addition, the degree of intelligence of the recommendation system allows it to continuously learn and optimize, adapting to changes in students' subject areas and learning habits.