Personalized Pedagogy Through a LLM-Based Recommender System

The educational landscape is evolving with the integration of AI, large language models (LLMs), and generative AI, requiring educators to adopt state-of-the-art technologies and strategies in their pedagogical practices. Pedagogical Design Patterns (PDPs) have garnered attention for disseminating best practices and bridging the gap between research and practice. However, their widespread adoption is hindered by limited publicly available resources and fragmented publishing platforms. To address this, we propose leveraging LLMs to recommend pedagogical practices, drawing from existing PDPs. Our model utilizes a local knowledge base and the Retrieval Augmented Generation (RAG) framework to create query contexts for LLM prompts. Initial findings show promise, with an accuracy score of 0.83 and high relevance of recommendations to input queries. This study presents early results of our ongoing project, supporting further development of the model. The proposed system aims to empower novice educators by providing expert wisdom to enrich their teaching methodologies.