Papers › Exploring Durham University Physics exams with Large Language Models
Exploring Durham University Physics exams with Large Language Models
Will Yeadon, Douglas P. Halliday
The archive published only this paper's code-link row. Authors, date and abstract are from arXiv's metadata (CC0), read from the Kaggle arXiv metadata snapshot of 2026-09-12 where its title matched the archive's; the title is the archive's.
The emergence of advanced Natural Language Processing (NLP) models like ChatGPT has raised concerns among universities regarding AI-driven exam completion. This paper provides a comprehensive evaluation of the proficiency of GPT-4 and GPT-3.5 in answering a set of 42 exam papers derived from 10 distinct physics courses, administered at Durham University over the span of 2018 to 2022, totalling 593 questions and 2504 available marks. These exams, spanning both undergraduate and postgraduate levels, include traditional pre-COVID and adaptive COVID-era formats. Questions from the years 2018-2020 were designed for pre-COVID in person adjudicated examinations whereas the 2021-2022 exams were set for varying COVID-adapted conditions including open-book conditions. To ensure a fair evaluation of AI performances, the exams completed by AI were assessed by the original exam markers. However, due to staffing constraints, only the aforementioned 593 out of the total 1280 questions were marked. GPT-4 and GPT-3.5 scored an average of 49.4\% and 38.6\%, respectively, suggesting only the weaker students would potential improve their marks if using AI. For exams from the pre-COVID era, the average scores for GPT-4 and GPT-3.5 were 50.8\% and 41.6\%, respectively. However, post-COVID, these dropped to 47.5\% and 33.6\%. Thus contrary to expectations, the change to less fact-based questions in the COVID era did not significantly impact AI performance for the state-of-the-art models such as GPT-4. These findings suggest that while current AI models struggle with university-level Physics questions, an improving trend is observable. The code used for automated AI completion is made publicly available for further research.
In Syntology Open this paper in Syntology's Atlas, the map of the papers in Syntology's graph and their citations.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Results from the paper archive 2025-07-28
No leaderboard rows for this paper in the archive.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections