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

2025-07-25

AI Summary

This study addresses the limitations of conventional learning systems—namely, insufficient personalization and delayed pedagogical responsiveness—by proposing an LLM-driven adaptive learning framework. The framework integrates large language models (LLMs) with real-time behavioral analytics to construct a dynamic affective and cognitive sensing module, enabling millisecond-level learner state assessment. It further employs a multi-granularity knowledge graph and context-aware recommendation algorithms to generate interpretable, personalized learning pathways and resource sequences. Its key innovation lies in leveraging the LLM as the core cognitive modeling engine—replacing traditional rule-based or statistical models—to support continuous policy optimization and alignment with instructional intent. Empirical evaluation across K–12 and higher education settings demonstrates statistically significant improvements: a 37.2% increase in learning engagement and a 29.5% improvement in knowledge retention (p < 0.01), both substantially outperforming baseline approaches.

Technology Category

• Cognitive Modeling & Cognitive Systems: Adaptive Behavior
• Machine Learning: Large Multimodal Models (LMMs)
• Search and Optimization: Learning to Search

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information

Abstract

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.

Problem

• Personalizing curriculum design using LLM analytics for adaptive learning
• Enhancing student engagement and retention via customized learning pathways
• Transforming traditional education into adaptive, student-centered models

Innovation

• LLM-powered analytics for personalized curriculum design
• Real-time data analysis for adaptive learning pathways
• Continuous student assessment for relevant instructional strategies