This Master’s thesis aims to enhance the educational platform Artemis by implementing automatic formative feedback for students and improving semi-automatic assessment for tutors. Building on the foundational work of Athena and CoFee, which use NLP and LLMs for text-based feedback, this project focuses on refining these techniques for greater accuracy, reliability, and consistency.
The initial phase will prioritize developing immediate feedback mechanisms to support students before deadlines. Following this, we will explore advanced LLM techniques like RAG, CoT prompting, self-consistency, and fine-tuning models like Llama 3 and GPT-4 with historical feedback data. Each iteration will be evaluated for accuracy, efficiency, and educational impact to ensure continuous improvement. By adopting an agile approach, we aim to deliver automated, personalized feedback that enhances student learning and reduces tutors’ workload, creating a more efficient and supportive educational environment.