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An optimization-based approach to AI-driven personalized learning, aiming to address the challenges of traditional, one-size-fits-all educational systems is presented in this article. It proposes a model that dynamically adjusts learning pathways based on real-time data from learners, enabling continuous customization of instructional content, assessments, and feedback mechanisms. The model optimizes various educational parameters to maximize learning outcomes, engagement, and retention. The results show that AI-powered personalization significantly improves student performance, engagement, and retention rates. However, challenges like data privacy, algorithmic bias, and scalability remain.

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Leveraging AI for Personalized Learning: An Optimization-Based Approach

  • 会议论文

  • 发表日期：

    2026年

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  • P. Kiran Kumar,
  • J. Suresh Babu,
  • Sai Nomitha Yarabolu,
  • A. V. Sriharsha,
  • M. Sunil Kumar,
  • D. Ganesh,
  • Kuraku Nirmala

摘要

An optimization-based approach to AI-driven personalized learning, aiming to address the challenges of traditional, one-size-fits-all educational systems is presented in this article. It proposes a model that dynamically adjusts learning pathways based on real-time data from learners, enabling continuous customization of instructional content, assessments, and feedback mechanisms. The model optimizes various educational parameters to maximize learning outcomes, engagement, and retention. The results show that AI-powered personalization significantly improves student performance, engagement, and retention rates. However, challenges like data privacy, algorithmic bias, and scalability remain.