Strategies for Optimizing Personalized Learning Pathways with Artificial Intelligence Assistance

Abstract

With the deepening application of artificial intelligence (AI) in the field of education, Personalized Learning Pathways (PLPs) as a strategy to revolutionize traditional educational models have garnered widespread attention. This paper aims to explore strategies for optimizing PLPs with the aid of AI, in order to enhance learning efficiency, stimulate students' interest in learning, and foster their holistic development. The background section discusses the "one-size-fits-all" teaching methods prevalent in traditional education models and the importance and necessity of PLPs. Following this, the study delves into the limitations of existing methods for optimizing PLPs, especially in terms of dynamic adaptability and real-time feedback mechanisms. The paper consists of two main parts: the first part constructs a dynamic model to simulate the impact of PLP design features on the student learning process; the second part proposes a dynamic PLP resource recommendation algorithm based on incremental learning. By updating students' abilities, preferences, and knowledge states in real-time, the algorithm can provide more precise learning resource recommendations. The experimental results demonstrate that the proposed dynamic PLP resource recommendation algorithm based on incremental learning exhibits significant effects in optimizing PLP design. It can improve the accuracy of the recommendation system and positively influence the long-term learning state transition of students. This proves the potential and practical application value of dynamic models in the field of personalized education. The methods and findings of this study not only enrich the theoretical foundation of the field of personalized learning but also offer robust technical support for practical educational practices, holding significant academic and practical value.

Topics & Concepts