Design and Implementation of a Personalized Learning Path Recommendation System Driven by AI and Big Data

DOI: https://doi.org/10.62517/jbdc.202601110

Author(s)

Liu Jiaxin, Long Yanbin*

Affiliation(s)

Liaoning University of Science and Technology, Anshan, China *Corresponding Author

Abstract

With the rapid development of artificial intelligence (AI) and big data technologies, personalized learning path recommendation systems have become a research hotspot in the field of education. This paper aims to explore the application of AI and big data in personalized learning path recommendation systems, designing and implementing a system that can dynamically generate personalized learning paths based on data such as students' learning behavior, interests, and knowledge mastery. Through deep learning algorithms, collaborative filtering recommendation algorithms, and sentiment computing technology, the system can analyze students' learning status in real time and provide accurate learning resource recommendations and path planning. Experimental results show that the system can significantly improve students' learning interest and efficiency, and promote the improvement of educational equity and quality.

Keywords

AI Technology; Big Data Analysis; Personalized Learning Path; Recommendation System; Deep Learning; Collaborative Filtering

References

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