The DOI matches its Crossref record; 31 of the 31 references it cites were found in a registry or an index.
Can linguists distinguish between ChatGPT/AI and human writing?: A study of research ethics and academic publishing
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In the citing paper
“Previous studies suggested that human users often fail to recognise AI-generated text and are generally overconfident in their ability to do so (Casal & Kessler, 2023;Fleckenstein et al., 2024).”
From “ ChatGPT can make mistakes. Check important info.” Epistemic beliefs and metacognitive accuracy in students' integration of ChatGPT content into academic writing (2025) · cites this paper as Casal & Kessler, 2023
In the citing paper
“This is heightened by the fact that even experts, such as professional linguists and teachers, are unable to effectively distinguish between human-written and AI-generated text (Casal & Kessler, 2023;Fleckenstein et al., 2024).”
From “ ChatGPT can make mistakes. Check important info.” Epistemic beliefs and metacognitive accuracy in students' integration of ChatGPT content into academic writing (2025) · cites this paper as Casal & Kessler, 2023
In the citing paper
“Large language models (LLMs) have achieved a level of fluency and coherence that makes their outputs increasingly indistinguishable from those by humans.1,2”
In the citing paper
“By adopting similar designs with objective linguistic criteria, we found that both the reviewer success rate and answer alternation reasons (“Suspicious wording” and “Both not mentioning critical features”) were associated with the writing style (“Familiarity and voice” and “Specificity or vagueness of details”) rather than the sentence-level writing quality and coherence34.”
From Towards a holistic framework for multimodal LLM in 3D brain CT radiology report generation (2025) · cites this paper as [34]
In the citing paper
“traits of “Familiarity and voice” (29%), “Specificity or vagueness of details” (37.60%), “Continuity and Coherence” (33.33%), and “Writing quality at sentence level” (33.33%) were significant factors when medical experts attribute a human origin behind a radiology report”
From Towards a holistic framework for multimodal LLM in 3D brain CT radiology report generation (2025) · cites this paper as [34]
Citations
Cites
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ChatGPT for Good? On Opportunities and Challenges of Large Language Models for Education
Kasneci, E., Seßler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., Gasser, U., Groh, G., Günnemann, S., Hüllermeier, E., et al. (2023) · doi:10.35542/osf.io/5er8f · Cited by 7,552
ChatGPT for good? On opportunities and challenges of large language models for education
Kasneci, E., Sessler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., Gasser, U., Groh, G., Günnemann, S., Hüllermeier, E., et al. (2023) · Learning and Individual Differences · doi:10.1016/j.lindif.2023.102274 · Cited by 7,326
On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? 🦜
Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021) · Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency · doi:10.1145/3442188.3445922 · Cited by 6,988
The global landscape of AI ethics guidelines
Jobin, A., Ienca, M., & Vayena, E. (2019) · Nature Machine Intelligence · doi:10.1038/s42256-019-0088-2 · Cited by 6,220
The Ethics of AI Ethics: An Evaluation of Guidelines
Hagendorff, T. (2020) · Minds and Machines · doi:10.1007/s11023-020-09517-8 · Cited by 1,911
The Ethics of AI Ethics -- An Evaluation of Guidelines
Hagendorff, T. (2019) · arXiv · doi:10.48550/arxiv.1903.03425 · Cited by 1,907
Principles alone cannot guarantee ethical AI
Mittelstadt, B. (2019) · Nature Machine Intelligence · doi:10.1038/s42256-019-0114-4 · Cited by 1,886
ChatGPT for Language Teaching and Learning
Kohnke, L., Moorhouse, B. L., & Zou, D. (2023) · RELC Journal · doi:10.1177/00336882231162868 · Cited by 1,371
ChatGPT and other large language models are double-edged swords
Shen, Y., Heacock, L., Elias, J., Hentel, K. D., Reig, B., Shih, G., & Moy, L. (2023) · Radiology · doi:10.1148/radiol.230163 · Cited by 1,008
Research works printed but not linked: 2
- Marino, F., Liska, D., & Kessler, M. (In press). Ethical considerations for research involving computer-assisted language learning, social media, and online environments. In P. I. De Costa, A. Rabie-Ahmed, & C. Cinaglia (Eds.), Ethical issues in applied linguistics scholarship. John Benjamins.
- Spilioti, 2017, Ethics of online research methods in applied linguistics, Applied Linguistics Review, 8
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Metadata
- Authors
- Casal, J. E., & Kessler, M.
- Affiliations
- University of Memphis (US)
- University of South Florida (US)
- Corresponding author
- Casal, J. Elliott
- Venue
- Research Methods in Applied Linguistics
- Publisher
- Elsevier BV
- Published
- 2023-12
- Volume
- 2
- Issue
- 3
- Pages
- 100068
- ISSN
- 2772-7661 (ISSN-L)
- OpenAlex
- W4385628481
- Semantic Scholar
- 678298191a35694083bd01d4fac2f9a229dab98b
- Cited by
- 261
- References
- 28
- Citations by year
- 2026: 69 · 2025: 97 · 2024: 83 · 2023: 12
- FWCI
- 65.31
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Research Methods in Applied Linguistics (publishedVersion)
- Topics
- Social Sciences › Education › Academic integrity and plagiarism
- Physical Sciences › Computer Science › Artificial Intelligence › Authorship Attribution and Profiling
- Social Sciences › Library and Information Sciences › Academic Writing and Publishing
- Keywords
- ChatGPT, large language models, AI-generated text detection, peer review, journal editors, research ethics, scholarly publishing, artificial intelligence in research, applied linguistics journals
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