🤖 AI Summary
The proliferation of AI-generated student assignments poses significant academic integrity risks. Method: This paper introduces HowkGPT, a detection system that precisely identifies whether undergraduate assignments were generated by large language models (LLMs) such as ChatGPT. It pioneers context-aware perplexity analysis in educational settings and proposes a novel metadata-driven, discipline-adaptive thresholding mechanism—dynamically calibrating detection boundaries based on course type, assignment format, and other pedagogical features. Contribution/Results: Evaluated on a real-world dataset of university course assignments, HowkGPT achieves substantially lower false-positive rates and markedly higher accuracy and domain adaptability compared to general-purpose LLM detectors. The system delivers a deployable, interpretable, and robust automated solution for academic integrity assurance, directly addressing the practical needs of educational institutions.
📝 Abstract
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.
