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Adaptive Learning Systems: Personalized Curriculum Design Using LLM-Powered Analytics

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arxiv 2507.18949 v1 pith:H5T7RXHQ submitted 2025-07-25 cs.CY cs.CL

Adaptive Learning Systems: Personalized Curriculum Design Using LLM-Powered Analytics

Yongjie Li , Ruilin Nong , Jianan Liu , Lucas Evans This is my paper
classification cs.CY cs.CL
keywords learningadaptivecurriculumframeworkpersonalizedanalyticsdesigneducational
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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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.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. LLM Harms: A Taxonomy and Discussion

    cs.CY 2025-12 unverdicted novelty 3.0

    This paper proposes a taxonomy of LLM harms in five categories and suggests mitigation strategies plus a dynamic auditing system for responsible development.

  2. LLM Harms: A Taxonomy and Discussion

    cs.CY 2025-12 reject novelty 3.0

    Proposes a five-bucket taxonomy of LLM harms and calls for dynamic auditing, but the systematic review behind it is not reproducible and contains mismatched citations.