This master’s thesis aims to enhance educational outcomes on Artemis by reducing tutor workload and improving the student learning experience. We’ll build on Athena’s preliminary work that deployed Large Language Models (LLMs) like GPT-4 to provide automatic feedback on text-based exercises. The focus is to employ more advanced LLM techniques such as Retrieval Augmented Generation (RAG) and fine-tuning Llama 3 with historical feedback data. The project will proceed in two phases: refining the feedback system for more context-sensitive, automated responses, and then evaluating and comparing the system’s accuracy, efficiency, and educational impact. This approach seeks to streamline the assessment process, providing students with immediate, personalized feedback to support their learning progress.