Adaptive Learning Systems: Personalized Curriculum Design Using LLM-Powered Analytics
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866908466553028608 |
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| author | Li, Yongjie Nong, Ruilin Liu, Jianan Evans, Lucas |
| author_facet | Li, Yongjie Nong, Ruilin Liu, Jianan Evans, Lucas |
| contents | Large language models (LLMs) are revolutionizing the field of education by enabling personalized learning experiences tailored to individual student needs. In this paper, we introduce a framework for Adaptive Learning Systems that leverages LLM-powered analytics for personalized curriculum design. This innovative approach uses advanced machine learning to analyze real-time data, allowing the system to adapt learning pathways and recommend resources that align with each learner's progress. By continuously assessing students, our framework enhances instructional strategies, ensuring that the materials presented are relevant and engaging. Experimental results indicate a marked improvement in both learner engagement and knowledge retention when using a customized curriculum. Evaluations conducted across varied educational environments demonstrate the framework's flexibility and positive influence on learning outcomes, potentially reshaping conventional educational practices into a more adaptive and student-centered model. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_18949 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Adaptive Learning Systems: Personalized Curriculum Design Using LLM-Powered Analytics Li, Yongjie Nong, Ruilin Liu, Jianan Evans, Lucas Computers and Society Computation and Language Large language models (LLMs) are revolutionizing the field of education by enabling personalized learning experiences tailored to individual student needs. In this paper, we introduce a framework for Adaptive Learning Systems that leverages LLM-powered analytics for personalized curriculum design. This innovative approach uses advanced machine learning to analyze real-time data, allowing the system to adapt learning pathways and recommend resources that align with each learner's progress. By continuously assessing students, our framework enhances instructional strategies, ensuring that the materials presented are relevant and engaging. Experimental results indicate a marked improvement in both learner engagement and knowledge retention when using a customized curriculum. Evaluations conducted across varied educational environments demonstrate the framework's flexibility and positive influence on learning outcomes, potentially reshaping conventional educational practices into a more adaptive and student-centered model. |
| title | Adaptive Learning Systems: Personalized Curriculum Design Using LLM-Powered Analytics |
| topic | Computers and Society Computation and Language |
| url | https://arxiv.org/abs/2507.18949 |