  As the use of Large Language Models (LLMs) in text generation tasks
proliferates, concerns arise over their potential to compromise academic
integrity. The education sector currently tussles with distinguishing
student-authored homework assignments from AI-generated ones. This paper
addresses the challenge by introducing HowkGPT, designed to identify homework
assignments generated by AI. HowkGPT is built upon a dataset of academic
assignments and accompanying metadata [17] and employs a pretrained LLM to
compute perplexity scores for student-authored and ChatGPT-generated responses.
These scores then assist in establishing a threshold for discerning the origin
of a submitted assignment. Given the specificity and contextual nature of
academic work, HowkGPT further refines its analysis by defining
category-specific thresholds derived from the metadata, enhancing the precision
of the detection. This study emphasizes the critical need for effective
strategies to uphold academic integrity amidst the growing influence of LLMs
and provides an approach to ensuring fair and accurate grading in educational
institutions.
