The DOI matches its Crossref record; 29 of the 29 references it cites were found in a registry or an index.
The false positives and false negatives of generative AI detection tools in education and academic research: The case of ChatGPT
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“The literature is also silent on the potential consequences, harmful or beneficial, of ChatGPT usage (Dalalah & Dalalah, 2023; Paul et al., 2023) despite a ban in many institutions across the globe.”
From Is it harmful or helpful? Examining the causes and consequences of generative AI usage among university students (2024) · cites this paper as Dalalah & Dalalah, 2023
In the citing paper
“Since the use of ChatGPT for academic activities is usually considered as an unethical mean (Dalalah et al., 2023; Dwivedi et al., 2023)”
From Is it harmful or helpful? Examining the causes and consequences of generative AI usage among university students (2024) · cites this paper as Dalalah et al., 2023
In the citing paper
“there has been a notable upswing in discussions among scholars and academicians regarding the transformative potential of generative AI, particularly ChatGPT, in higher education and the risks associated with it (Dalalah & Dalalah, 2023; Meyer et al., 2023; Peters et al., 2023; Yilmaz & Yilmaz, 2023a).”
From Is it harmful or helpful? Examining the causes and consequences of generative AI usage among university students (2024) · cites this paper as Dalalah & Dalalah, 2023
Citations
Cites
Language Models are Few-Shot Learners
Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al. (2020) · arXiv · doi:10.48550/arxiv.2005.14165 · Cited by 23,372
Dwivedi, Y. K., Kshetri, N., Hughes, L., Slade, E. L., Jeyaraj, A., Kar, A. K., Baabdullah, A. M., Koohang, A., Raghavan, V., Ahuja, M., et al. (2023) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2023.102642 · Cited by 4,584
Kung, T. H., Cheatham, M., Medenilla, A., Sillos, C., De Leon, L., Elepaño, C., Madriaga, M., Aggabao, R., Diaz-Candido, G., Maningo, J., et al. (2023) · PLOS Digital Health · doi:10.1371/journal.pdig.0000198 · Cited by 4,115
ChatGPT is fun, but not an author
Thorp, H. H. (2023) · Science · doi:10.1126/science.adg7879 · Cited by 1,437
Lim, W. M., Gunasekara, A., Pallant, J. L., Pallant, J. I., & Pechenkina, E. (2023) · The International Journal of Management Education · doi:10.1016/j.ijme.2023.100790 · Cited by 1,261
Gao, C. A., Howard, F. M., Markov, N. S., Dyer, E. C., Ramesh, S., Luo, Y., & Pearson, A. T. (2022) · doi:10.1101/2022.12.23.521610 · Cited by 1,099
Gao, C. A., Howard, F. M., Markov, N. S., Dyer, E. C., Ramesh, S., Luo, Y., & Pearson, A. T. (2023) · npj Digital Medicine · doi:10.1038/s41746-023-00819-6 · Cited by 1,099
ChatGPT listed as author on research papers: many scientists disapprove
Stokel-Walker, C. (2023) · Nature · doi:10.1038/d41586-023-00107-z · Cited by 868
Artificial intelligence for the metaverse: A survey
Huynh‐The, T., Pham, Q., Pham, X., Nguyen, T. T., Han, Z., & Kim, D. (2022) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2022.105581 · Cited by 832
Towards a Human-like Open-Domain Chatbot
Adiwardana, D., Luong, M.-T., So, D. R., Hall, J., Fiedel, N., Thoppilan, R., Yang, Z., Kulshreshtha, A., Nemade, G., Lu, Y., et al. (2020) · arXiv · doi:10.48550/arxiv.2001.09977 · Cited by 614
A Conversation on Artificial Intelligence, Chatbots, and Plagiarism in Higher Education
King, M. R., & chatGPT (2023) · Cellular and Molecular Bioengineering · doi:10.1007/s12195-022-00754-8 · Cited by 602
Open artificial intelligence platforms in nursing education: Tools for academic progress or abuse?
O’Connor, S., & ChatGPT (2023) · Nurse Education in Practice · doi:10.1016/j.nepr.2022.103537 · Cited by 585
ChatGPT and the rise of generative AI: Threat to academic integrity?
Eke, D. O. (2023) · Journal of Responsible Technology · doi:10.1016/j.jrt.2023.100060 · Cited by 532
Hitsuwari, J., Ueda, Y., Yun, W., & Nomura, M. (2023) · Computers in Human Behavior · doi:10.1016/j.chb.2022.107502 · Cited by 267
Introduction to statistical quality control
Freeman, K. P., Cook, J. R., & Hooijberg, E. H. (2021) · Journal of the American Veterinary Medical Association · doi:10.2460/javma.258.7.733 · Cited by 251
Could AI help you to write your next paper?
Hutson, M. (2022) · Nature · doi:10.1038/d41586-022-03479-w · Cited by 236
Rapamycin in the context of Pascal's Wager: generative pre-trained transformer perspective
Generative Pre-trained Transformer, C., & Zhavoronkov, A. (2022) · Oncoscience · doi:10.18632/oncoscience.571 · Cited by 203
Henestrosa, A. L., Greving, H., & Kimmerle, J. (2022) · Computers in Human Behavior · doi:10.1016/j.chb.2022.107445 · Cited by 153
AI et al.: Machines Are About to Change Scientific Publishing Forever
Grimaldi, G., & Ehrler, B. (2023) · ACS Energy Letters · doi:10.1021/acsenergylett.2c02828 · Cited by 103
ChatGPT in Research: Balancing Ethics, Transparency and Advancement
Graf, A., & Bernardi, R. E. (2023) · Neuroscience · doi:10.1016/j.neuroscience.2023.02.008 · Cited by 98
Chat Generative Pre-trained Transformer: why we should embrace this technology
Chavez, M. R., Butler, T. S., Rekawek, P., Heo, H., & Kinzler, W. L. (2023) · American Journal of Obstetrics and Gynecology · doi:10.1016/j.ajog.2023.03.010 · Cited by 83
Deep learning applications in manufacturing operations: a review of trends and ways forward
Sahoo, S., Kumar, S., Abedin, M. Z., Lim, W. M., & Jakhar, S. K. (2022) · Journal of Enterprise Information Management · doi:10.1108/jeim-01-2022-0025 · Cited by 81
A Survey on Supervised and Unsupervised Learning Techniques
Meena, K., & Suriya, S. (2020) · Springer eBooks · doi:10.1007/978-3-030-24051-6_58 · Cited by 73
Henestrosa, A. L., Greving, H., & Kimmerle, J. (2022) · doi:10.31234/osf.io/m9exr · Cited by 14
Weiss, S. T., Veach, L. J., McGill, W., & Brent, J. A. (2022) · Clinical Toxicology · doi:10.1080/15563650.2022.2117052 · Cited by 13
Ji, Y., & Gao, S. (2023) · DROPS (Schloss Dagstuhl – Leibniz Center for Informatics) · doi:10.4230/lipics.giscience.2023.43 · Cited by 10
The Impact of False Positive COVID-19 Result
Alsheikh, S., Ibrahim, K., & Alfaraj, D. (2021) · Cureus · doi:10.7759/cureus.20375 · Cited by 8
Incidental findings on multimodel comparison, hypothesis testing error, and its solution
Lim, W. M., & Mandrinos, S. (2020) · AJIS. Australasian journal of information systems/AJIS. Australian journal of information systems/Australian journal of information systems · doi:10.3127/ajis.v24i0.2555 · Cited by 5
Son, H., Choi, H., Kim, W., Youn, B. D., & Lee, G. (2022) · Structural and Multidisciplinary Optimization · doi:10.1007/s00158-021-03127-3 · Cited by 5
Research works printed but not linked: 5
- Beamon, 2002, False-negative urine pregnancy test: A case report, Journal of Reproductive Medicine, 47, 952
- Collier, 2013, False positives in airport security screening, Journal of Policy Analysis and Management, 32, 961
- Mina, 2021, Impact of false-positive and false-negative COVID-19 test results on individual and population-level outcomes: A systematic review and meta-analysis, JAMA, 326, 1827
- Rothwell, 2019, False positives and false negatives in statistical models, Circulation Research, 124, 1235
- Zhang, 2021, False-positive results in cancer screening: A narrative review, Journal of Cancer, 12, 994
Cited by
Sorted by citations
Zhai, C., Wibowo, S., & Li, L. D. (2024) · Smart Learning Environments · doi:10.1186/s40561-024-00316-7 · Cited by 1,567
Chan, C. K. Y., & Lee, K. K. W. (2023) · Smart Learning Environments · doi:10.1186/s40561-023-00269-3 · Cited by 634
Abbas, M., Jam, F. A., & Khan, T. I. (2024) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-024-00444-7 · Cited by 531
“For the same reasons, scholars have called for future research to delve deeper into the positives and negatives of ChatGPT in higher education (Bahroun et al., 2023; Chaudhry et al., 2023; Dalalah & Dalalah, 2023).”
BackgroundInfluentialGenerative AI tools and assessment: Guidelines of the world's top-ranking universities
Moorhouse, B. L., Yeo, M. A., & Wan, Y. (2023) · Computers and Education Open · doi:10.1016/j.caeo.2023.100151 · Cited by 461
McDonald, N., Johri, A., Ali, A., & Collier, A. H. (2025) · Computers in Human Behavior: Artificial Humans · doi:10.1016/j.chbah.2025.100121 · Cited by 682
Metadata
- Authors
- Dalalah, D., & Dalalah, O. M. A.
- Affiliations
- Jordan University of Science and Technology (JO)
- Jadara University (JO)
- Corresponding author
- Dalalah, Doraid
- ORCID
- Dalalah, Doraid
- Venue
- The International Journal of Management Education
- Publisher
- Elsevier BV
- Published
- 2023-07
- Volume
- 21
- Issue
- 2
- Pages
- 100822
- ISSN
- 1472-8117 (ISSN-L)
- OpenAlex
- W4378448767
- Cited by
- 218
- References
- 27
- Citations by year
- 2026: 40 · 2025: 82 · 2024: 80 · 2023: 16
- FWCI
- 54.55
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- The International Journal of Management Education (publishedVersion)
- Topics
- Social Sciences › Education › Academic integrity and plagiarism
- Physical Sciences › Computer Science › Artificial Intelligence › Authorship Attribution and Profiling
- Social Sciences › Education › Artificial Intelligence in Education
- Keywords
- generative AI detection, ChatGPT, AI-generated text, false positives, false-negative results, type I and type II errors, academic integrity, AI detection tools, abstracts, research ethics, generative pre-trained transformer
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 ›
Are you an author, or do you see a problem with this page? Request a review