Detecting LLM-Generated Text in Computing Education: Comparative Study for ChatGPT Cases
Abstract
Due to the recent improvements and wide availability of Large Language Models (LLMs), they have posed a serious threat to academic integrity in education. Modern LLM-generated text detectors attempt to combat the problem by offering educators with services to assess whether some text is LLM-generated. In this work, we have collected 124 submissions from computer science students before the creation of ChatGPT. We then generated 40 ChatGPT submissions. We used this data to evaluate eight publicly-available LLM-generated text detectors through the measures of accuracy, false positives, and resilience. Our results find that Copy Leaks is the most accurate LLM-generated text detector, G PTKit is the best LLM-generated text detector to reduce false positives, and GLTR is the most resilient LLM-generated text detector. We note that all LLM-generated text detectors are less accurate with code, other languages (aside from English), and after the use of paraphrasing tools.
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A number of studies investigated the effectiveness of AI tools in producing solutions that could bypass detection tools.
“A number of studies investigated the effectiveness of AI tools in producing solutions that could bypass detection tools (e.g., [49, 104, 256, 267, 303, 354]).”
From Navigating the Ethical and Societal Impacts of Generative AI in Higher Computing Education (2025) · cites this paper as [256]
Orenstrakh et al. conclude that while detectors achieve reasonable accuracy, they are still prone to flaws and are not yet suitable for dealing with academic integrity.
“The authors conclude: that while detectors manage to achieve a reasonable accuracy, they are still prone to flaws and can be challenging to interpret and that they are not yet suitable for dealing with academic integrity.”
From Bob or Bot: Exploring ChatGPT's Answers to University Computer Science Assessment (2024) · cites this paper as [38]
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A Unified Framework for Evaluating Mathematical Problem Solving Ability of Large Language Models
Quan, L., & Sun, C. (2025) · International Conference on Intelligent Education and Intelligent Research (IEIR) · doi:10.1109/ieir67391.2025.11491027
Detecting AI Generated Text Using Support Vector Machine
Widjaya, R., & Iskandar, K. (2026) · International Symposium on Computational and Business Intelligence (ISCBI) · doi:10.1109/iscbi69404.2026.11495803
Use of a Retrieval-Augmented Generation (RAG) Chatbot for First-Year Engineering Education at ESPRIT
Maalaoui, H., & Samet, Y. (2026) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-032-16635-7_28 · Cited by 1 · see citing works
Domain Specific Large Language Models: Potential, Challenges and Future Directions
Iqbal, S. (2026) · IEEE International Conference on Computing and Machine Intelligence (ICMI) · doi:10.1109/icmi68585.2026.11539778
Layton, S., Medeiros, B. B. P., Butler, K., & Traynor, P. (2026) · IEEE Symposium on Security and Privacy (S&P) · doi:10.1109/sp63933.2026.00088
Comparative Analysis of Neural Architectures and Hybrid Approach for AI-Generated Text Detection
Assylkhan, A., & Biloshchytska, S. (2026) · IEEE International Conference on Smart Information Systems and Technologies (SIST) · doi:10.1109/sist61674.2026.11596221
Karnalim, O. (2026) · Journal of Educational Technology Systems · doi:10.1177/00472395261472143
Self-citationTeaching Programming in the Age of AI: Pedagogical Strategies for Computational Sociology
Ramos, M. C., & Liang, F. (2026) · Teaching Sociology · doi:10.1177/0092055x261469125
How Unique and Accurate Is GenAI Generated Code in Introductory Programming?
Setiawan, Y. D., Karnalim, O., & Ayub, M. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-20381-6_50
Self-citationYang, S., Li, X., Li, Y., Zhao, Y., Xu, S., Yan, L., Huang, S., & Zhou, Z. (2026) · International Conference on Computer Science and Technologies in Education (CSTE) · doi:10.1109/cste69562.2026.11649758
Ajithkumar, S., Ramesh, V., Devi, S. M. R., Deepan, S., Dhanasekar, S., & Kathir, S. (2026) · 2026 International Conference on Intelligent and Sustainable Electronics & Computing Technologies (INSECT) · doi:10.1109/insect68872.2026.11663863
The Robots are Here: Navigating the Generative AI Revolution in Computing Education
Prather, J., Denny, P., Leinonen, J., Becker, B. A., Albluwi, I., Craig, M., Keuning, H., Kiesler, N., Kohn, T., Luxton-Reilly, A., et al. (2023) · arXiv · doi:10.48550/arxiv.2310.00658 · Cited by 9 · see citing works
- Authors
- Orenstrakh, M. S., Karnalim, O., Suárez, C. A., & Liut, M.
- Affiliations
- University of Toronto (CA)
- Maranatha Christian University (ID)
- Escuela Superior Politecnica del Litoral (EC)
- Venue
- 2024 IEEE 48th Annual Computers, Software, and Applications Conference (COMPSAC)
- Publisher
- IEEE
- Published
- 2024-07-02
- Pages
- 121-126
- ISSN
- 0730-3157 (ISSN-L)
- OpenAlex
- W4401880391
- Cited by
- 79
- References
- 26
- Citations by year
- 2026: 14 · 2025: 33 · 2024: 23 · 2023: 9
- FWCI
- 31.94
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE Annual International Computer Software and Applications Conference (COMPSAC) (publishedVersion)
- Topics
- Social Sciences › Safety Research › Academic integrity and plagiarism
- Physical Sciences › Computer Science › Artificial Intelligence › Authorship Attribution and Profiling
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Keywords
- LLM-generated text detection, ChatGPT, large language models, academic integrity, computing education, code generation, plagiarism detection, false positives
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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