Scholarlink

This paper explores the integration of Large Language Models (LLMs) with Artificial Intelligence in Education (AIED) to create personalized learning experiences. It examines how LLMs can adapt educational content and delivery methods according to individual student needs and learning styles, aligning with the Felder-Silverman Learning Style Model. The research details how AIED can leverage LLMs and Retrieval-Augmented Generation (RAG) technology to provide adaptive learning experiences and automated assessments. It helps to show how the RAG system enhances the capabilities of LLMs by enabling access to knowledge sources that thus ensure accurate and up-to-date information delivery. The paper addresses traditional education challenges in personalizing education for students and proposes LLM-based solutions to create individualized learning paths while maintaining educational quality.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Developing Self LLM to Enhance Student Knowledge Based on AIED for Personalized Learning Pathways

  • 会议论文

  • 发表日期：

    2025年

查看全文
  • Karim Elia Fraoua,
  • Amos David

摘要

This paper explores the integration of Large Language Models (LLMs) with Artificial Intelligence in Education (AIED) to create personalized learning experiences. It examines how LLMs can adapt educational content and delivery methods according to individual student needs and learning styles, aligning with the Felder-Silverman Learning Style Model. The research details how AIED can leverage LLMs and Retrieval-Augmented Generation (RAG) technology to provide adaptive learning experiences and automated assessments. It helps to show how the RAG system enhances the capabilities of LLMs by enabling access to knowledge sources that thus ensure accurate and up-to-date information delivery. The paper addresses traditional education challenges in personalizing education for students and proposes LLM-based solutions to create individualized learning paths while maintaining educational quality.