The death of the short-form physics essay in the coming AI revolution
Open accessAbstract
The latest AI language modules can produce original, high quality full short-form (300-word) Physics essays within seconds. These technologies such as ChatGPT and davinci-003 are freely available to anyone with an internet connection. In this work, we present evidence of AI generated short-form essays achieving First-Class grades on an essay writing assessment from an accredited, current university Physics module. The assessment requires students answer five open-ended questions with a short, 300-word essay each. Fifty AI answers were generated to create ten submissions that were independently marked by five separate markers. The AI generated submissions achieved an average mark of 71 ± 2 % , in strong agreement with the current module average of 71 ± 5 % . A typical AI submission would therefore most-likely be awarded a First Class, the highest classification available at UK universities. Plagiarism detection software returned a plagiarism score between 2 ± 1 % (Grammarly) and 7 ± 2 % (TurnitIn). We argue that these results indicate that current natural language processing AI represent a significant threat to the fidelity of short-form essays as an assessment method in Physics courses.
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Lexical density is an indicator of information richness and compactness.
“Lexical density is an indicator of information richness and compactness. 57,58,59”
From Style, sentiment, and quality of undergraduate writing in the AI era: A cross-sectional and longitudinal analysis of 4,820 authentic empirical reports (2025) · cites this paper as [58]
The Overview (section 3.1) discusses perplexity as a measure used in AI-text detection, while Written quality (section 3.2) discusses readability and editing. Searches for “lexical density,” “information richness,” and the proportion of content words found no passage defining lexical density or linking it to information richness and compactness. (LLM, conf=0.75)
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Lindell, T. L., & Modén, M. U. (2025) · Learning Media and Technology · doi:10.1080/17439884.2024.2438933 · Cited by 11 · see citing works
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Large Language Models are Catalyzing Chemistry EducationPreprint
Du, Y., Duan, C., Bran, A. M., Sotnikova, A., Qu, Y., Kulik, H. J., Bosselut, A., Xu, J., & Schwaller, P. (2024) · ChemRxiv · doi:10.26434/chemrxiv-2024-h722v · Cited by 10 · see citing works
- Authors
- Yeadon, W., Inyang, O.-O., Mizouri, A., Peach, A., & Testrow, C. P.
- Affiliations
- Durham University (GB)
- Corresponding author
- Yeadon, Will
- Venue
- Physics Education
- Publisher
- IOP Publishing
- Published
- 2023-04-05
- Volume
- 58
- Issue
- 3
- Pages
- 035027
- ISSN
- 0031-9120 (ISSN-L)
- 1361-6552
- OpenAlex
- W4362551436
- Cited by
- 199
- References
- 13
- Citations by year
- 2026: 23 · 2025: 54 · 2024: 68 · 2023: 54
- FWCI
- 46.70
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Physics Education (publishedVersion, cc-by)
- Durham Research Online (Durham University) (publishedVersion, other-oa)
- arXiv (Cornell University) (submittedVersion)
- Durham Research Online (Durham University) (submittedVersion, cc-by)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Social Sciences › Safety Research › Academic integrity and plagiarism
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- AI-generated text, ChatGPT, physics assessment, plagiarism detection, academic integrity, natural language processing, university physics education
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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