Location
https://www.kennesaw.edu/ccse/events/computing-showcase/sp24-cday-program.php
Document Type
Event
Start Date
25-4-2024 4:00 PM
Description
The educational domain is undergoing transformation due to the incorporation of Artificial Intelligence (AI), Large Language Models (LLMs), and generative AI technologies, raising the need for educators to integrate cutting-edge technological advancements and methodologies into their teaching approaches. Pedagogical Design Patterns (PDPs) have become prominent for their role in sharing effective educational practices and narrowing the divide between academic research and actual teaching methods. Despite their potential, the lack of widely accessible resources and the scattered nature of publishing outlets pose significant barriers to the broad application of PDPS. To address this issue, we propose the application of large language models to recommend educational strategies based on existing PDPs. Our approach employs a locally sourced knowledge database and the Retrieval Augmented Generation (RAG) framework to formulate context for LLM queries. Preliminary results are encouraging, demonstrating an accuracy rate of 0.83 and a strong relevance of the recommended pedagogical practices to the posed queries. This paper details the initial outcomes of our project, which paves the way for further refinement of the model. The goal is to equip new educators with seasoned insights, to enhance their instructional methods.
Included in
GPR-103 Personalized Pedagogy through a LLM-based Recommender System
https://www.kennesaw.edu/ccse/events/computing-showcase/sp24-cday-program.php
The educational domain is undergoing transformation due to the incorporation of Artificial Intelligence (AI), Large Language Models (LLMs), and generative AI technologies, raising the need for educators to integrate cutting-edge technological advancements and methodologies into their teaching approaches. Pedagogical Design Patterns (PDPs) have become prominent for their role in sharing effective educational practices and narrowing the divide between academic research and actual teaching methods. Despite their potential, the lack of widely accessible resources and the scattered nature of publishing outlets pose significant barriers to the broad application of PDPS. To address this issue, we propose the application of large language models to recommend educational strategies based on existing PDPs. Our approach employs a locally sourced knowledge database and the Retrieval Augmented Generation (RAG) framework to formulate context for LLM queries. Preliminary results are encouraging, demonstrating an accuracy rate of 0.83 and a strong relevance of the recommended pedagogical practices to the posed queries. This paper details the initial outcomes of our project, which paves the way for further refinement of the model. The goal is to equip new educators with seasoned insights, to enhance their instructional methods.