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The false positives and false negatives of generative AI detection tools in education and academic research: The case of ChatGPT
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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
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Language Models are Few-Shot Learners
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“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).”
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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 ›
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