Cognitive Computing with Large Language Models for Student Assessment Feedback

Effective student feedback is fundamental to enhancing learning outcomes in higher education. While traditional assessment methods emphasise both achievements and development areas, the process remains time-intensive for educators. This research explores the application of cognitive computing, specifically open-source Large Language Models (LLMs) Mistral-7B and CodeLlama-7B, to streamline feedback generation for student reports containing both Python programming elements and English narrative content. The findings indicate that these models can provide contextually appropriate feedback on both technical Python coding and English specification and documentation. They effectively identified coding weaknesses and provided constructive suggestions for improvement, as well as insightful feedback on English language quality, structure, and clarity in report writing. These results contribute to the growing body of knowledge on automated assessment feedback in higher education, offering practical insights for institutions considering the implementation of open-source LLMs in their workflows. There are around 22 thousand assessment submissions per year in the School of Computer Science, which is one of eight schools in the Faculty of Engineering and Physical Sciences, which is one of seven faculties in the University of Leeds, which is one of one hundred and sixty-six universities in the UK, so there is clear potential for our methods to scale up to millions of assessment submissions. This study also examines the limitations of current approaches and proposes potential enhancements. The findings support a hybrid system where cognitive computing manages routine tasks and educators focus on complex, personalised evaluations, enhancing feedback quality, consistency, and efficiency in educational settings.