Personalized Education System
Nishtha, Kaustubh Srivastava, Sandeep Kumar
Content Filtering, Large Language Models (LLM), Collaborative Filtering.
The concept of personalized education has witnessed a transformative evolution with the integration of Large Language Models (LLMs). This abstract explores the innovative synergy between personalized education and LLMs, focusing on their collaborative potential to revolutionize learning experiences. By harnessing the linguistic capabilities of LLMs, educators can tailor instructional content, adapt feedback mechanisms, and facilitate dynamic interactions that cater to individual students' unique needs and preferences. The abstract delves into the process of student profiling, where LLMs analyze diverse data facets to generate comprehensive learner profiles. These profiles then serve as the foundation for designing personalized learning pathways, where LLM-generated content recommendations and real-time feedback enhance engagement and foster autonomous learning. Collaboration is nurtured through LLM-facilitated peer interactions and group projects, fostering a sense of community within personalized educational environments. Ethical considerations are paramount, with robust data privacy measures safeguarding sensitive information. Continuous evaluation and refinement ensure the efficacy of the personalized education system, leveraging LLM-driven insights to adapt and optimize instructional strategies. In conclusion, the abstract underscores the transformative potential of integrating LLMs within personalized education, emphasizing the enhancement of engagement, comprehension, and holistic student development within a technologically enriched educational landscape.
Article Details
Unique Paper ID: 163208

Publication Volume & Issue: Volume 10, Issue 11

Page(s): 2020 - 2029
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