LLMs in Personalized Education: Adaptive Learning Models

Abstract

Personalised education systems that use Large Language Models (LLMs) are a game-changer in the field of contemporary pedagogy. Adaptive learning models that cater to the unique requirements of each student are made possible by LLMs thanks to their ability to comprehend spoken language, provide replies that seem human, and adjust to user inputs. In order to personalise instructional information and feedback in real-time, these models dynamically evaluate the learner's skill, style, and speed. This study delves into the design and execution of adaptive learning systems driven by LLM, showcasing its benefits in engaging students, enhancing retention, and tackling various learning obstacles. It goes on to talk about how using these models in schools might lead to biases, data privacy issues, and ethical dilemmas. The study highlights the potential of LLMs to democratise education and improve learning outcomes through scalable, personalised support across a broad range of courses and learner demographics. It does this through case examples and recent breakthroughs.