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In this paper, we discuss how the advancement of LLM maximizes its role as a tool to provide individual learning experiences and how LLM delivers learning content that can be altered in response to individual learner’s requirements. As advanced NLP and generative models, LLMs allow educators to gain insights from the data of quizzes they create using Google Forms, which in turn, helps to individualize education. This study selects on algorithm course students of Mu’tah University. This present research investigate how LLMs can constructively create lessons, build the methodology for custom-made instruction based on student deficit, and facilitate instruction in accordance with quiz results. Test quizzes before class, and the Gemini model to deliver adaptive content were used to improve perception of algorithmic concepts from the students. In a survey conducted among 40 students, the researchers observed variations of gaps observed in knowledge among the students ranging from basics of the algorithm even up to the concept of recursion and complexity analysis. The main positive impacts of this paper are as follows: Detailed feedback and iterative content refinement positively impacted learning outcomes. Also, the study considers issues of accuracy, bias, and ethical concern and outlines directions for more effective integration of LLMs in learning environments for enhancing the overall learning process among LLM students.

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Harnessing Large Language Models for Personalized Learning: A Case Study in Algorithm Education at Mu’tah University

  • 会议论文

  • 发表日期：

    2025年

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  • Nadia Salem,
  • Loai Alnemer,
  • Khawla M. Al-tarawneh,
  • Hamza Salem,
  • Manuel Mazzara,
  • Asma Salem

摘要

In this paper, we discuss how the advancement of LLM maximizes its role as a tool to provide individual learning experiences and how LLM delivers learning content that can be altered in response to individual learner’s requirements. As advanced NLP and generative models, LLMs allow educators to gain insights from the data of quizzes they create using Google Forms, which in turn, helps to individualize education. This study selects on algorithm course students of Mu’tah University. This present research investigate how LLMs can constructively create lessons, build the methodology for custom-made instruction based on student deficit, and facilitate instruction in accordance with quiz results. Test quizzes before class, and the Gemini model to deliver adaptive content were used to improve perception of algorithmic concepts from the students. In a survey conducted among 40 students, the researchers observed variations of gaps observed in knowledge among the students ranging from basics of the algorithm even up to the concept of recursion and complexity analysis. The main positive impacts of this paper are as follows: Detailed feedback and iterative content refinement positively impacted learning outcomes. Also, the study considers issues of accuracy, bias, and ethical concern and outlines directions for more effective integration of LLMs in learning environments for enhancing the overall learning process among LLM students.