Can linguists distinguish between ChatGPT/AI and human writing?: A study of research ethics and academic publishing

Casal, J. Elliott; Kessler, Matt

doi:10.1016/j.rmal.2023.100068

Cited by 262Academic integrity and plagiarismAuthorship Attribution and ProfilingAcademic Writing and Publishing

Can linguists distinguish between ChatGPT/AI and human writing?: A study of research ethics and academic publishing

Casal, J. Elliott, Kessler, Matt·2023·Research Methods in Applied Linguistics

DOI 10.1016/j.rmal.2023.100068

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Human users often fail to recognise AI-generated text and are generally overconfident in their ability to do so.
“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 “ <scp>ChatGPT</scp> can make mistakes. Check important info.” Epistemic beliefs and metacognitive accuracy in students' integration of <scp>ChatGPT</scp> content into academic writing (2025) · cites this paper as Casal & Kessler, 2023
Even experts such as professional linguists and teachers are unable to effectively distinguish between human-written and AI-generated text.
“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 “ <scp>ChatGPT</scp> can make mistakes. Check important info.” Epistemic beliefs and metacognitive accuracy in students' integration of <scp>ChatGPT</scp> content into academic writing (2025) · cites this paper as Casal & Kessler, 2023
Large language models have achieved a level of fluency and coherence that makes their outputs increasingly indistinguishable from those by humans.
“Large language models (LLMs) have achieved a level of fluency and coherence that makes their outputs increasingly indistinguishable from those by humans.1,2”
From Open-source versus proprietary large language models for academic writing in medicine: A controlled evaluation of DeepSeek-R1 and ChatGPT-o1 in neuroradiology (2026) · cites this paper as [1]
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 coherence.
“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]
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.
“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]

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Authors: Casal, J. E., & Kessler, M.
Affiliations: University of Memphis (US)
University of South Florida (US)
Corresponding author: Casal, J. Elliott
ORCID: Casal, J. Elliott
Kessler, Matt
Venue: Research Methods in Applied Linguistics
Publisher: Elsevier BV
Published: 2023-12
Volume: 2
Issue: 3
Pages: 100068
ISSN: 2772-7661 (ISSN-L)
DOI: 10.1016/j.rmal.2023.100068
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: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/legal/tdmrep-license (tdm)
doi.org/10.15223/policy-017 (stm-asf)
doi.org/10.15223/policy-037 (stm-asf)
doi.org/10.15223/policy-012 (stm-asf)
doi.org/10.15223/policy-029 (stm-asf)
doi.org/10.15223/policy-004 (stm-asf)
Locations: Research Methods in Applied Linguistics (publishedVersion)
Topics: Social Sciences › Safety Research › Academic integrity and plagiarism
Physical Sciences › Computer Science › Artificial Intelligence › Authorship Attribution and Profiling
Social Sciences › Arts and Humanities › History and Philosophy of Science › 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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