A Transformer-Based Framework for Automated Content Retrieval and Dynamic Response Generation: A Pedagogical Advancement

The paper introduces an intelligent system for educational enhancement that integrates two key modules: a Question and Answer (QnA) module and a novel Feedback Generation module. We create a robust automatic content retrieval and response generation framework using Retrieval Augmented Generation (RAG) and transformer-based models, specifically OpenAI GPT-3.5. The QnA module dynamically retrieves relevant content from documents through cosine similarity and produces answers aligned with educational material. Meanwhile, the Feedback Generation module is designed to handle subjective responses and evaluate students' answers by comparing them with LLM-generated responses via cosine similarity. This comparison yields a performance score for the student's response, supplemented by specific feedback that highlights strengths and areas for improvement. Our approach bridges the gap in current automated grading systems providing a scalable and adaptable solution for personalized learning in diverse educational contexts. This system is particularly beneficial for institutions managing extensive student cohorts, offering real-time, individualized feedback to enhance student engagement and learning outcomes. Results demonstrate the effectiveness of our system with the QnA module achieving high cosine similarity scores of 0.87 for theory questions and 0.81 for numerical when compared with a solution manual. The Feedback Generation module exhibited a strong correlation (r = 0.92) with professor-assigned marks validating its alignment with human evaluations, this empirical validation involved 150 student responses across diverse problem types in the Computer Architecture course. These results highlight the robustness and accuracy of our approach in real-world educational scenarios.