Academic Integrity considerations of AI Large Language Models in the post-pandemic era: ChatGPT and beyond
Open accessAbstract
This paper explores the academic integrity considerations of students’ use of Artificial Intelligence (AI) tools using Large Language Models (LLMs) such as ChatGPT in formal assessments. We examine the evolution of these tools, and highlight the potential ways that LLMs can support in the education of students in digital writing and beyond, including the teaching of writing and composition, the possibilities of co-creation between humans and AI, supporting EFL learners, and improving Automated Writing Evaluations (AWE). We describe and demonstrate the potential that these tools have in creating original, coherent text that can avoid detection by existing technological methods of detection and trained academic staff alike, demonstrating a major academic integrity concern related to the use of these tools by students. Analysing the various issues related to academic integrity that LLMs raise for both Higher Education Institutions (HEIs) and students, we conclude that it is not the student use of any AI tools that defines whether plagiarism or a breach of academic integrity has occurred, but whether any use is made clear by the student. Deciding whether any particular use of LLMs by students can be defined as academic misconduct is determined by the academic integrity policies of any given HEI, which must be updated to consider how these tools will be used in future educational environments.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
29 of 60 cited sources checked · 15 supported · 0 flagged · accuracy not yet measured
- Premisecitation§ Limitations and newer LLMs · p. 9
LLMs have the potential to produce text that lacks semantic coherence.
“there is the potential for LLMs to produce text that lacks semantic coherence (Dale, 2021)”
Quote in the paperNot a substantial claim1 of 1 sources checked- cites Dale, 2021 source
- Premisecitation§ Introduction · p. 3
The pandemic resulted in increases in student or academic staff perceptions of academic dishonesty.
“as well as increases in student or academic staff perceptions of AD (Amzalag et al., 2021; Reedy et al., 2021; Walsh et al., 2021) occurring.”
Quote in the paperNot a substantial claim3 of 3 sources checked- cites Amzalag et al., 2021 source
The Findings (page unavailable) report students’ and lecturers’ perceptions of academic dishonesty during pandemic-era online exams, including lecturers’ concern about copying. They compare the two groups, not perceptions before and during the pandemic, so they do not establish that the pandemic increased those perceptions. (LLM, conf=0.86)
Experimental — accuracy not yet measured - cites Reedy et al., 2021 source
- cites Walsh et al., 2021 source
- Interpretationcitation§ Student uses of LLMs · p. 10
Student use of an LLM to support work, including directly copied output, without stating AI use, could be seen as a relatively clear case of plagiarism.
“given the misrepresentation that have been made, usage of this type could be seen as a relatively clear case of plagiarism by the student”
Quote in the paperSubstantial claim1 of 1 sources checked- cites Roe & Perkins, 2022 source
In “Types of APTs and their use in academic work” (page unavailable), the authors call using automated paraphrasing tools to rework copied source material academic dishonesty and paraphrasing plagiarism. That supports the general concern about presenting machine-assisted work as one’s own, but the paper does not address LLM-generated work or establish that directly copying undisclosed LLM output is a clear case of plagiarism. (LLM, conf=0.85)
Experimental — accuracy not yet measured
- Premisecitation§ Concerns for academic integrity · p. 9
Use of LLM-created text by students is unlikely to be spotted by existing text-matching software tools used by HEIs.
“current research suggests that use of the created text by students is unlikely to be spotted by existing text-matching software tools used by HEIs”
Quote in the paperSubstantial claim1 of 1 sources checked- cites Wahle, Ruas, Kirstein, et al., 2022 source
In Q5, the paper reports that PlagScan could not reliably identify machine-paraphrased text and that large language models made text matches harder to find. Its tests concern LLM-generated paraphrases and one commercial plagiarism-detection product, not all LLM-created text or the range of text-matching tools used by higher education institutions. (LLM, conf=0.86)
Experimental — accuracy not yet measured
- Rests on an assumption: LLM-generated text is uniquely created based on the inputs provided.
- Premisecitation§ Automated Writing Evaluation (AWE) · p. 14
Use of LLMs in AWE tools can reduce the burden on EFL instructors.
“the use of LLMs in these tools can particularly reduce the burden on EFL instructors (Godwin-Jones, 2022).”
Quote in the paperNot a substantial claim1 of 1 sources checked- cites Godwin-Jones, 2022 source
The Ecological Perspectives section (page unavailable) says AWE can free teachers’ time from correcting language mechanics so they can address higher-order writing issues. The Introduction describes large language models as enabling language services, but the paper does not specifically say that LLMs in AWE tools reduce instructors’ workload, or that the benefit is particular to EFL instructors. (LLM, conf=0.84)
Experimental — accuracy not yet measured
- Premisecitation§ Concerns for academic integrity · p. 9
Current generation LLMs can produce output which humans struggle to identify as being machine created.
“emerging research has suggested that existing LLMs can produce output which humans struggle to identify as being machine created”
Quote in the paperSubstantial claim1 of 3 sources checked - Interpretationcitation§ Cognitive offloading · p. 11
It is highly likely that LLMs will be integrated more deeply into suites of products that are often provided to students by HEIs in the future.
“it is highly likely that LLMs will be integrated more deeply into suites of products that are often provided to students by HEIs in the future”
Quote in the paperSubstantial claim1 of 1 sources checked- cites Dale & Viethen, 2021 source
In the concluding “Copy Shark” passage (page unavailable), the paper predicts that GPT-3-based writing capabilities may soon appear in Microsoft Word. That supports future LLM integration into a widely used product, but the paper does not say that such integration is highly likely across product suites provided to students by higher education institutions. (LLM, conf=0.86)
Experimental — accuracy not yet measured
- Premisecitation§ Supporting EFL learners · p. 13
An LLM tool based on GPT-2 can be helpful for EFL students writing English by suggesting predictions that expand on more basic support offered by existing writing tools.
“Gayed et al., (2022) demonstrate how an LLM tool based on GPT-2 can be helpful for EFL students writing English by suggesting predictions which expands on the more basic support offered by existing writing tools.”
Quote in the paperNot a substantial claim1 of 1 sources checked- cites Gayed et al., (2022) source
- Premisecitation§ GPT-3 studies · p. 14
In Clark et al. (2021), evaluators identified GPT-2 produced text at an accuracy rate of 57.9%, but GPT-3 at a rate of only 49.9%.
“Their results showed that evaluators were able to identify GPT-2 produced text at an accuracy rate of 57.9%, but GPT-3 at a rate of only 49.9%.”
Quote in the paperSubstantial claim1 of 1 sources checked- cites Clark et al. (2021) source
- Premisecitation§ Conclusion · p. 16
The COVID-19 pandemic caused increases in student or academic staff perceptions of academic dishonesty occurring.
“as well as increases in student or academic staff perceptions of academic dishonesty occurring (Amzalag et al., 2021; Reedy et al., 2021; Walsh et al., 2021).”
Quote in the paperNot a substantial claim3 of 3 sources checked- cites Amzalag et al., 2021 source
The Findings (page unavailable) report lecturers’ concern about copying and examine students’ and lecturers’ perceptions of dishonesty during pandemic-era online exams. The paper does not report that either group’s perception of dishonesty increased relative to before the pandemic, so the claimed increase is unsupported. (LLM, conf=0.82)
Experimental — accuracy not yet measured - cites Reedy et al., 2021 source
- cites Walsh et al., 2021 source
- Premisecitation§ GPT-3 studies · p. 14
Providing training to participants using examples of LLM generated text marginally increased their ability to correctly identify text as machine created.
“Providing training to participants using examples of LLM generated text (specifically to demonstrate the ability of LLMs to produce 'creative' output) marginally increased the ability of participants to correctly identify text as machine created.”
Quote in the paperNot a substantial claim1 of 1 sources checked- cites Clark et al. (2021) source
Section 3.2 reports a modest, statistically significant overall improvement with Examples training, though no individual domain showed a significant improvement. Section 3.1.2 says the practice examples included both human- and machine-authored text and were intended to set expectations about text quality. The suggestion in §3.2 that they helped overcome doubts about machines producing creative text is offered as a possible explanation, not the training's stated specific purpose. (LLM, conf=0.86)
Experimental — accuracy not yet measured
- Premisecitation§ Technological methods of detection · p. 15
Using GLTR can improve the ability of participants to correctly identify machine generated content from 54% to 74%.
“This study demonstrates that using GLTR can improve the ability of participants to correctly identify detection of machine generated content from 54% to 74%.”
Quote in the paperNot a substantial claim1 of 1 sources checked- cites Gehrmann et al. (2019) source
The Results report that participants’ accuracy improved from 54.2% without the GLTR interface to 72.3% with it. The claimed improvement to 74% is not the figure reported in our copy. (LLM, conf=0.94)
Experimental — accuracy not yet measured
- Premisecitation§ Conclusion · p. 16
The COVID-19 pandemic caused an increase in the detected number of academic dishonesty cases.
“the COVID-19 pandemic has caused an increase in both the detected number of academic dishonesty cases (Henderson et al., 2022; Jenkins et al., 2022; Lancaster & Cotarlan, 2021)”
Quote in the paperNot a substantial claim1 of 3 sources checked- cites Henderson et al., 2022 source
- cites Jenkins et al., 2022 source
- cites Lancaster & Cotarlan, 2021 source
The Results and discussion report a substantial rise in questions posted to Chegg after the pandemic began, and the Conclusion says academic integrity breaches became more common as a result of COVID-19. But the paper measures requests on one file-sharing site in five STEM subjects, not the detected number of academic dishonesty cases generally. (LLM, conf=0.86)
Experimental — accuracy not yet measured
- Findingreasoning§ Practitioner Notes · p. 2
Falsified references in LLM output may hint at their use if unchanged by students.
“The output created by these LLMs is coherent enough for it not to be detected by academic staff members, or traditional text-matching software used to detect plagiarism, but falsified references may hint at their use if unchanged by students.”
- Also stated in: § References: · p. 7 · plain
“Although in text citations and references were provided (including references from the input text), these were all fabricated, with titles, URLs, and journal names being incorrect.”
Quote in the paperSubstantial claim- Rests on an assumption: The ChatGPT output generated in the paper's demonstration is representative of LLM output generally.
- Also stated in: § References: · p. 7 · plain
- Findingnone given§ Abstract · p. 2
Large Language Models (LLMs) such as ChatGPT can create original, coherent text that can avoid detection by existing technological methods of detection and trained academic staff alike.
“We describe and demonstrate the potential that these tools have in creating original, coherent text that can avoid detection by existing technological methods of detection and trained academic staff alike, demonstrating a major academic integrity concern related to the use of these tools by students.”
- Also stated in: § Practitioner Notes · p. 2 · plain
“The output created by these LLMs is coherent enough for it not to be detected by academic staff members, or traditional text-matching software used to detect plagiarism, but falsified references may hint at their use if unchanged by students.”
Quote in the paperNot a substantial claim- Rests on an assumption: The ChatGPT output generated in the paper's demonstration is representative of LLM output generally.
- Also stated in: § Practitioner Notes · p. 2 · plain
- Findingreasoning§ Conclusion · p. 16
LLMs have already progressed to the point that neither trained academic staff nor technological tools can consistently determine whether text is generated by an LLM or by a human.
“conclude that LLMs have already progressed to the point that neither trained academic staff or technological tools can consistently determine whether text is generated by an LLM or by a human.”
Quote in the paperNot a substantial claim - Findingnone given§ Conclusion · p. 16
LLMs can produce coherent, original text that students may potentially use in assessments.
“We have identified how LLMs can produce coherent, original text that students may potentially use in assessments”
Quote in the paperNot a substantial claim - Premisecitation§ Cognitive offloading · p. 11
LLMs can be considered another potential tool for reducing the cognitive demands required by a task (cognitive offloading).
“we may consider LLMs as another potential tool for reducing the cognitive demands required by a task, described by Risko and Gilbert (2016) as a process called cognitive offloading.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Risko and Gilbert (2016) source
- Premisecitation§ Limitations and newer LLMs · p. 9
GPT-3 can be trained to state how confident it is in producing an answer that is factually correct.
“recent research has shown that GPT-3 can be trained to state how confident it is in producing an answer that is factually correct (Lin et al., 2022)”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Lin et al., 2022 source
- Premisecitation§ Automated Writing Evaluation (AWE) · p. 14
ChatGPT can provide effective critique on creative writing equivalent to that of human reviewers.
“The new capabilities of ChatGPT to provide effective critique on creative writing, equivalent to that of human reviewers (Furze, 2022)”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Furze, 2022
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Premisereasoning§ Limitations and newer LLMs · p. 9
LLMs are unable to answer questions or complete tasks which relate to very recent events.
“Given that the development of these LLMs require pretraining before they are released, they are unable to answer questions or complete tasks which relate to very recent events.”
Quote in the paperNot a substantial claim - Premisecitation§ Supporting EFL learners · p. 13
Students with lower English language abilities have increased risks of academic misconduct.
“increased risks of academic misconduct occurring in students with lower English language abilities (Perkins et al., 2018).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Perkins et al., 2018 source
- Interpretationreasoning§ Technological methods of detection · p. 16
The inability to detect machine generated text presents a clear threat to academic integrity for HEIs.
“this presents a clear threat to academic integrity for HEIs, requiring a range of adjustments to be made in both practice and policy.”
Quote in the paperNot a substantial claim - Study design and conductreasoning§ Methodological concerns · p. 15
Experimental studies on detection require participants to be aware that some text they encounter may be machine generated before participating.
“An unavoidable methodological issue with these experimental studies is that in order to determine whether participants can accurately identify text as machine or human generated, participants need to be aware that some of the text they are about to encounter may be machine generated before participating in an experiment.”
Quote in the paperSubstantial claim - Interpretationreasoning§ Methodological concerns · p. 15
It is likely that even experienced academic staff are not aware of the capabilities of LLMs.
“Given the novelty of these tools, it is likely that even experienced academic staff are simply not aware of the capabilities that these tools have”
Quote in the paperSubstantial claim - Interpretationcitation§ GPT-3 studies · p. 14
LLMs have rapid improvements to create text that cannot be easily identified by study participants as machine created.
“These studies highlight the rapid improvements of LLMs to create text that cannot be easily identified by study participants as machine created.”
Quote in the paperSubstantial claim - Premisecitation§ GPT-2 studies · p. 14
In Abd-Elaal et al. (2022), participants correctly identified sample text as generated by either a human or an LLM at an average rate of 59.5%, barely higher than chance.
“Their results indicate that on average, participants were able to correctly identify sample text as generated by either a human or an LLM at a rate of 59.5%, barely higher than chance alone.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Abd-Elaal et al., (2022) source
- Interpretationreasoning§ Methodological concerns · p. 15
Academic staff's ability to identify LLM-produced output in situ when evaluating work may be even lower than demonstrated in an experimental design.
“This may result in their ability to identify any LLM produced output 'in situ' when evaluating work being even lower than demonstrated in an experimental design.”
Quote in the paperSubstantial claim - Interpretationcitation§ GPT-3 studies · p. 14
As more complex LLMs are used, the ability of humans to detect material drops even further.
“Studies assessing the ability of GPT-3 produced output show that as more complex LLMs are used, the ability of humans to detect material drops even further.”
Quote in the paperSubstantial claim - Interpretationcitation§ Automated Writing Evaluation (AWE) · p. 14
The shortcomings of present AWE tools may be addressed by LLM-based tools.
“suggests that these shortcomings may be addressed by LLM based tools.”
Quote in the paperNot a substantial claim - Interpretationcitation§ Technological methods of detection · p. 15
Tools using more advanced LLMs may be even less detectable by technological means.
“This suggest that tools using more advanced LLMs may be even less detectable by technological means.”
Quote in the paperSubstantial claim - Interpretationreasoning§ Academic integrity policy adjustments · p. 16
A blanket ban of LLM based tools is likely unenforceable because academic staff are unlikely to be able to accurately identify the usage of LLMs in student work.
“Given that existing research has demonstrated that academic staff are unlikely to be able to accurately identify the usage of LLMs in student work, a blanket ban of these tools is likely unenforceable.”
Quote in the paperNot a substantial claim - Premisecitation§ Technological methods of detection · p. 15
More advanced models (GPT-J) can fool machine detection programmes such as MOSS.
“Bidermann and Raff (2022) have already demonstrated how more advanced models (GPT-J) can fool machine detection programmes such as MOSS.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Bidermann and Raff (2022)
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Premisecitation§ GPT-2 studies · p. 14
Poem continuations could not always be correctly identified by professionals with a literature-specific background as machine or human written.
“Gunser et al. (2021) found that poem continuations could not always be correctly identified by professionals with a literature-specific background as machine or human written.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Gunser et al. (2021) source
- Classified as probably not a substantial claim. 47
- May combine two claims. 18
Probably not a substantial claim: may not pass S3 (load-bearing).
Premisecitation§ The emergence of Large Language Models · p. 5Transformer based machine learning models allow for improved performance on language based tasks when compared to previous machine learning techniques.
“These tools started to achieve prominence from 2017 with the emergence of a range of transformer based machine learning models which allow for improved performance on language based tasks when compared to previous machine learning techniques (Vaswani et al., 2017).”
Quote in the paperNot a substantial claim1 of 1 sources checked- cites Vaswani et al., 2017 source
Probably not a substantial claim: may not pass S3 (load-bearing).
Premisecitation§ The evolution of AI powered digital writing tools · p. 4Digital Writing Assistants (DWAs) such as Grammarly, WordTune and Perusall use Artificial Intelligence (AI) to improve student writing.
“Digital Writing Assistants (DWAs) such as Grammarly, WordTune and Perusall, use Artificial Intelligence (AI) to improve student writing (Fitria, 2021)”
Quote in the paperNot a substantial claim1 of 1 sources checked- cites Fitria, 2021 source
The Abstract (page not specified) describes Grammarly as an AI-powered writing assistant and reports improved writing performance after its use. The paper supports that part of the claim, but the passages found for WordTune and Perusall do not address either tool, so it does not substantiate the claim about all three. (LLM, conf=0.88)
Experimental — accuracy not yet measured
Probably not a substantial claim: may not pass S3 (load-bearing).
Premisecitation§ The evolution of AI powered digital writing tools · p. 4DWAs have demonstrated a particular ability to support English as a Foreign Language (EFL) writers in improving their skills at expressing ideas in written English.
“These tools have also demonstrated a particular ability to support English as a Foreign Language (EFL) writers in improving their skills at expressing ideas in written English (Gayed et al., 2022; Nazari et al., 2021; Zhao, 2022)”
Quote in the paperNot a substantial claim3 of 3 sources checked- cites Gayed et al., 2022 source
The Results section reports that adult EFL participants using AI KAKU produced sentences with significantly greater syntactic complexity, which the authors link to communicating complex ideas more fluently. But the study tested performance during a writing task, not lasting improvement in writing skills or a particular ability of digital writing aids generally. Differences in word production and lexical diversity were not statistically significant. (LLM, conf=0.86)
Experimental — accuracy not yet measured - cites Nazari et al., 2021 source
The Discussion says Grammarly can improve EFL writing skills, and the Conclusion describes it as an assistive tool for non-native postgraduate students writing in English. But the trial reports improvements in writing self-efficacy, engagement, and emotions—not a measured improvement in students’ ability to express ideas in writing. The Introduction’s reference to expressing ideas describes writing skills generally, not a demonstrated effect of the tool. (LLM, conf=0.84)
Experimental — accuracy not yet measured - cites Zhao, 2022 source
Probably not a substantial claim: may not pass S1 (asserted), S3 (load-bearing).
Premisecitation§ Limitations and newer LLMs · p. 9LLMs will generally always provide an answer or output, even if that is factually incorrect or misinterpreted.
“Although one concern levied against LLMs is that they will generally always provide an answer or output, even if that is factually incorrect or misinterpreted (Dale, 2021; Hutson, 2022)”
Quote in the paperNot a substantial claim1 of 2 sources checked- cites Dale, 2021 source
Section 3.3, “From editor to author” (page unavailable), says GPT-3 can generate convincing text that is sometimes nonsense, supporting the concern that an output can be unreliable. It does not say LLMs generally always provide an answer, or specifically discuss factual errors or misinterpretation. (LLM, conf=0.82)
Experimental — accuracy not yet measured - cites Hutson, 2022 source
Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Premisecitation§ Supporting EFL learners · p. 13The GPT-2 based tool could support improving lexical diversity of student work, but statistically significant results were not obtained.
“Their study suggested that this tool could support in improving the lexical diversity of student work, but statistically significant results were not obtained.”
Quote in the paperNot a substantial claim1 of 1 sources checked- cites Gayed et al., (2022) source
Probably not a substantial claim: may not pass S2 (checkable).
Premisecitation§ Practitioner Notes · p. 2There are legitimate uses of AI tools using LLMs in supporting the education of students.
“There are legitimate uses of these tools in supporting the education of students, meaning HEIs must carefully consider how policies dealing with student use of this software are created.”
Quote in the paperNot a substantial claim1 of 3 sources checked- cites Chen et al., 2015 source
- cites Park & Yang, 2020
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- cites Zhao, 2022 source
The Affordance for English Writing section (p. 4) says Wordtune offers learning opportunities for EFL writers and suggests that teachers encourage students to use it in writing activities. This supports an educational use of an AI tool, but our copy does not identify Wordtune as using an LLM. The sentence’s policy recommendation is the citing authors’ inference, not a point attributed to this source. (LLM, conf=0.79)
Experimental — accuracy not yet measured
Probably not a substantial claim: may not pass S3 (load-bearing).
Premisecitation§ Technological methods of detection · p. 15Detectors for machine-created text may potentially discriminate against EFL students by incorrectly identifying human created text as machine written.
“highlight the ethical challenges of deploying any such detectors which may potentially discriminate against EFL students by incorrectly identifying human created text as machine written”
Quote in the paperNot a substantial claim1 of 1 sources checked- cites Fröhling & Zubiaga (2021) source
May combine two claims.
Study design and conductcitation§ Technological methods of detection · p. 15GLTR was tested against GPT-2 produced output and used students rather than academic staff as participants.
“However, this tool was tested against GPT-2 produced output and used students rather than academic staff as their participants.”
Quote in the paperNot a substantial claim1 of 1 sources checked- cites Gehrmann et al. (2019) source
May combine two claims.
Premisecitation§ Technological methods of detection · p. 15Tools by Solaiman et al. (2019) and Ippolito et al. (2020) showed an encouraging ability in detecting machine created text, but were not tested against the latest generation of LLMs.
“Solaiman et al. (2019) and Ippolito et al.(2020) also both present tools which showed an encouraging ability in detecting machine created text, although these tools were not tested against the latest generation of LLMs.”
Quote in the paperNot a substantial claim2 of 2 sources checkedClassified as probably not a substantial claim.
Findingnone given§ References: · p. 7The in-text citations and references provided by ChatGPT in the example were all fabricated, with titles, URLs, and journal names being incorrect.
“Although in text citations and references were provided (including references from the input text), these were all fabricated, with titles, URLs, and journal names being incorrect.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Findingcitation§ References: · p. 8The references in the second ChatGPT output were falsified and there were some inaccuracies related to the claimed ability of Turnitin to detect AI generated content.
“Again, the references were falsified and there were some inaccuracies related to the claimed ability of Turnitin to detect AI generated content, but this fluent, five-paragraph output is typical of a response by ChatGPT.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing), S4 (based).
Findingnone given§ References: · p. 7A major benefit of ChatGPT as opposed to previous versions of the GPT-3 software is the iteration possible through natural language 'discussions' held with the software.
“A major benefit of ChatGPT as opposed to previous versions of the GPT-3 software is that of the iteration possible through natural language 'discussions' held with the software.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing), S4 (based). May combine two claims.
Findingnone given§ References: · p. 7The output retained the language style used in the input and expanded on the discussions by providing examples specifically related to education as requested.
“The output retained the language style used in the input and expanded on the discussions by providing examples specifically related to education as requested.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S4 (based).
Findingnone given§ Practitioner Notes · p. 2AI tools using LLMs can be used to create original written content that students may use in their assessments.
“These tools use Large Language Models (LLMs) and can be used to create original written content that students may use in their assessments.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing), S4 (based). May combine two claims.
Findingnone given§ Limitations and newer LLMs · p. 9ChatGPT can be challenged if the user believes there are incorrect elements in the output, and can adjust its response accordingly based on any corrections provided by the user.
“ChatGPT in particular can also be challenged if the user believes there are incorrect elements in the output, and can adjust its response accordingly based on any corrections provided by the user.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Findingcitation§ References: · p. 7The output created by ChatGPT in the paper's example was created in approximately 5 seconds at zero cost.
“This output was created in approximately 5 seconds at zero cost (ChatGPT is currently in a free research preview stage and costs are likely to change in the near future).”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing), S4 (based).
Findingnone given§ References: · p. 8The fluent, five-paragraph output is typical of a response by ChatGPT.
“but this fluent, five-paragraph output is typical of a response by ChatGPT.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S4 (based).
Interpretationnone given§ Abstract · p. 2Whether any particular use of LLMs by students can be defined as academic misconduct is determined by the academic integrity policies of any given Higher Education Institution (HEI).
“Deciding whether any particular use of LLMs by students can be defined as academic misconduct is determined by the academic integrity policies of any given HEI, which must be updated to consider how these tools will be used in future educational environments.”
- Also stated in: § Practitioner Notes · p. 2 · hedged
“however it may be a breach of academic integrity policies of any given Higher Education Institution (HEI).”
Quote in the paperNot a substantial claim- Also stated in: § Practitioner Notes · p. 2 · hedged
Probably not a substantial claim: may not pass S2 (checkable), S4 (based).
Interpretationnone given§ Abstract · p. 2Whether plagiarism or a breach of academic integrity has occurred is not defined by the student's use of any AI tools, but by whether the student makes any use clear.
“we conclude that it is not the student use of any AI tools that defines whether plagiarism or a breach of academic integrity has occurred, but whether any use is made clear by the student.”
- Also stated in: § Practitioner Notes · p. 2 · hedged
“The use of these tools may not necessarily be considered as plagiarism if students are transparent in how they have been used in any submission, however it may be a breach of academic integrity policies of any given Higher Education Institution (HEI).”
Quote in the paperNot a substantial claim- Also stated in: § Practitioner Notes · p. 2 · hedged
Probably not a substantial claim: may not pass S2 (checkable).
Interpretationreasoning§ Plagiarism or Academic Misconduct? · p. 11Use of LLMs or LLM-based tools does not constitute plagiarism or a breach of academic integrity if the use is stated clearly and made transparent by students.
“we conclude that the use of LLMs or LLM based tools should not be considered as plagiarism or a breach of academic integrity if how these tools have been used is stated clearly and made transparent by students.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Interpretationreasoning§ References: · p. 7Fabricated references open a potential avenue of detection by academic staff if there is a suspicion that these tools may have been used by students.
“This does open a potential avenue of detection by academic staff if there is a suspicion that these tools may have been used by students.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing), S4 (based).
Premisenone given§ Practitioner Notes · p. 2LLM-based tools can be accessed using commercial services built on this software, often targeted to students as a means of 'assisting' students with assessments.
“These tools can be accessed using commercial services built on this software, often targeted to students as a means of 'assisting' students with assessments.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing), S4 (based).
Premisenone given§ Integration of LLMs into commercial services · p. 6Many commercial tools that provide access to LLMs integrate features of DWAs and APTs, and market the product package together as one 'writing support tool'.
“Many of these tools integrate features of DWAs and APTs, and market the product package together as one 'writing support tool' or similar, available as both free and paid for versions.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing), S4 (based).
Interpretationreasoning§ Cognitive offloading · p. 11It is feasible that a student could very easily and inadvertently breach policies related to correct usage of LLMs, especially when LLMs become even more deeply integrated into DWAs.
“it is feasible to believe that a student could very easily and inadvertently breach any policies related to their 'correct' usage, especially when LLMs become even more deeply integrated into DWAs.”
Quote in the paperNot a substantial claim- Rests on an assumption: Many advanced DWAs have multiple features available to students which already claim to integrate AI technologies.
- Rests on an assumption: Write Full is a full-service DWA which has integrated paraphrasing tools that may encourage students to use the work of others as inputs, rather than their own writing.
Probably not a substantial claim: may not pass S2 (checkable). May combine two claims.
Interpretationreasoning§ Student uses of LLMs · p. 10Student use of an LLM to create an entire essay, report, or assignment without stating its origin, or with false citations to mislead, is clearly plagiarism and likely a broader breach of academic integrity principles.
“Student usage of an LLM or LLM based tool to create an entire essay, report, or other assignment without stating where this has come from, or one that is presented in a way that was trying to mislead the reader through false citations, would be even more clearly a case of plagiarism, and likely also a broader breach of academic integrity principles.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S4 (based).
Premisenone given§ The emergence of Large Language Models · p. 5Large Language Models (LLMs) are able to develop significant amounts of brand-new text based on short input requests.
“Tools using Artificial Intelligence (AI) to support in the creation of original text have emerged in recent years, in the form of Large Language Models (LLMs) which are able to develop significant amounts of brand-new text based on short input requests.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim.
Interpretationreasoning§ References: · p. 7Given that the authors and dates provided in the in-text citations are correct, fabricated references may go unnoticed unless thoroughly checked.
“However, given that the authors and dates provided in the intext citations are correct, this may go unnoticed unless thoroughly checked.”
Quote in the paperSubstantial claimClassified as probably not a substantial claim. May combine two claims.
Interpretationreasoning§ Student uses of LLMs · p. 10If a student's work has not been misrepresented, it does not fall under the strict definition of plagiarism, but it may be considered academic misconduct depending on specific HEI policies.
“If the work carried out by a student hasn't been misrepresented, then it does not fall under the strict definition of plagiarism, but it may be considered as academic misconduct depending on the specific policies and regulations in place at individual HEIs.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing), S4 (based). May combine two claims.
Premisenone given§ Integration of LLMs into commercial services · p. 6A subset of these tools is marketed specifically to students to support in the development of full length-assessments, which also include options to generate citations alongside the work.
“A subset of these tools is marketed specifically to students to support in the development of full length-assessments, which also include options to generate citations alongside the work.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Premisecitation§ The emergence of Large Language Models · p. 5ChatGPT achieved more than 1 million users within five days of launch.
“as well as achieving more than 1 million users within five days of launch (Brockman, 2022).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Brockman, 2022
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Probably not a substantial claim: may not pass S2 (checkable), S4 (based).
Interpretationreasoning§ Plagiarism or Academic Misconduct? · p. 11There is a potential danger of learning outcomes not being met if LLMs are used to develop content rather than content being created by students themselves.
“HEIs must be aware of the potential danger of learning outcomes not being met if LLMs are being used to develop content, rather than this being created by students themselves.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Premisecitation§ Creative outputs · p. 12Dall-E 2 can create and edit detailed digital artwork based on natural language prompts provided by users.
“Dall·E 2 AI system which is able to create and edit detailed digital artwork based on natural language prompts provided by users”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites OpenAi, n.d.
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Classified as probably not a substantial claim.
Interpretationreasoning§ References: · p. 8With some manipulation of the text through iteration using the tool, along with some minor editing or adjustments of references, a student intending to create large blocks of text to include in an assessment could rapidly complete certain tasks with limited effort and costs.
“It is evident that with some manipulation of the text through iteration using the tool, along with some minor editing or adjustments of references, a student intending to create large blocks of text to include in an assessment could rapidly complete certain tasks with limited effort and costs.”
Quote in the paperSubstantial claim- Rests on an assumption: The ChatGPT output generated in the paper's demonstration is representative of LLM output generally.
Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Premisereasoning§ Integration of LLMs into commercial services · p. 6LLMs can be accessed through commercial entities that use an Application Programming Interface (API) to connect to the LLMs, and these services are targeted at different groups of customers including students.
“an alternative method that students may use to access the LLMs is through commercial entities who use an Application Programming Interface (API) to connect to the LLMs. These services are targeted at different groups of customers including copywriters, marketers and students and allow user-friendly access to the LLMs.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S4 (based).
Premisenone given§ Practitioner Notes · p. 2Students now have easy access to advanced Artificial Intelligence based tools such as ChatGPT.
“Students now have easy access to advanced Artificial Intelligence based tools such as ChatGPT.”
Quote in the paperNot a substantial claimMay combine two claims.
Premisecitation§ Automated Writing Evaluation (AWE) · p. 14Present AWE tools have shortcomings: generic feedback and inability to focus feedback on how well a task has been addressed.
“Some of the recognised shortcomings of present AWE tools are the generic nature of the feedback provided (Fu et al., 2022) and an inability to focus feedback on how well a task has been addressed (Allen et al., 2016).”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites Fu et al., 2022
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- cites Allen et al., 2016
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
May combine two claims.
Interpretationreasoning§ Concerns for academic integrity · p. 9LLMs represent a clear potential threat to academic integrity because academic staff may be unable to correctly identify the amount of content produced by a student and provide an accurate evaluation of the student's comprehension and interpretation.
“LLMs therefore represent a clear potential threat to academic integrity as academic staff may be unable to correctly identify the amount of content produced by a student, and therefore provide an accurate evaluation of a student's comprehension and interpretation of the topic at hand.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Premisecitation§ Automated Writing Evaluation (AWE) · p. 13Software tools for automated writing evaluation (AWE) benefit writing instructors and students.
“Using software tools to support in AWE of written text and providing feedback to students has been demonstrated to be of benefit to writing instructors and students alike”
Quote in the paperNot a substantial claim0 of 3 sources checkedProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Premisecitation§ The emergence of Large Language Models · p. 5The broad release to the public in November 2022 of ChatGPT by OpenAI marked a significant increase in the ability of the underlying software to create new text using further refined models (GPT-3.5), coupled with a markedly improved user interface.
“The broad release to the public in November 2022 of ChatGPT by OpenAI marked a significant increase in the ability of the underlying software to create new text using further refined models (GPT-3.5), coupled with a markedly improved user interface (OpenAI, 2022).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites OpenAI, 2022
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Probably not a substantial claim: may not pass S3 (load-bearing).
Premisecitation§ Writing and composition · p. 12Using LLMs in the teaching of digital writing can support writers in engaging more deeply with a topic, rather than at a surface level.
“Using LLMs in the teaching of digital writing therefore becomes a method to support writers in engaging more deeply with a topic, rather than at a surface level.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites McKnight (2021) source
Probably not a substantial claim: may not pass S3 (load-bearing).
Premisecitation§ Introduction · p. 3During the pandemic, students sought out and adapted to new technologies.
“During the period of the pandemic, research has shown that students sought out and adapted to new technologies (Vargo et al., 2021)”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Vargo et al., 2021 source
Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing).
Premisecitation§ Conclusion · p. 17It is unlikely that AI will ever take over the traditional role of a teacher.
“Although it is unlikely that AI will ever take over the traditional role of a teacher (Cope et al., 2021)”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Cope et al., 2021 source
May combine two claims.
Premisecitation§ GPT-2 studies · p. 14Training academic staff increased their ability to correctly identify text sections, but the samples were generated using GPT-2 rather than GPT-3.
“Although it is encouraging to note that training of academic staff resulted in an increased ability of academic staff to correctly identify the text sections, the samples were generated using GPT-2 as opposed to GPT-3.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Abd-Elaal et al., (2022) source
May combine two claims.
Interpretationreasoning§ Conclusion · p. 17The likelihood of accurately detecting any usage of LLMs by students in their submissions will likely not improve and may even decrease further as new LLMs are developed.
“the likelihood of accurately detecting any usage of these tools by students in their submissions typical academic will likely not improve and may even decrease further as new LLMs are developed.”
Quote in the paperSubstantial claim- Rests on an assumption: New large language models will continue to be developed.
Probably not a substantial claim: may not pass S2 (checkable), S4 (based).
Interpretationreasoning§ Conclusion · p. 16Whether plagiarism or a breach of academic integrity has occurred is defined not by the use of the tools themselves but by whether any such use is made clear.
“it is not the use of the tools themselves that defines whether plagiarism or a breach of academic integrity has occurred, but whether any such use is made clear.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing).
Premisecitation§ Conclusion · p. 16What academic staff define as plagiarism is rapidly changing due to the social construction of the term.
“What we as academic staff define as plagiarism is rapidly changing due to the social construction of this term (Anson, 2022).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Anson, 2022
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).
Premisenone given§ Technological methods of detection · p. 15GPTZero and Crossplag AI detect show promise in being able to detect the use of AI generated text.
“These tools include GPTZero (https://gptzero.me/) and Crossplag AI detect (https://crossplag.com/ai-content-detector/) and do show promise in being able to detect the use of AI generated text.”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites GPTZero (https://gptzero.me/)
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- cites Crossplag AI detect (https://crossplag.com/ai-content-detector/)
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Probably not a substantial claim: may not pass S3 (load-bearing).
Premisecitation§ Conclusion · p. 16Plagiarism overall decreased between 1990 and 2020.
“Although evidence suggests that plagiarism overall has decreased between 1990 and 2020 (Curtis, 2022)”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Curtis, 2022
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Probably not a substantial claim: may not pass S2 (checkable). May combine two claims.
Interpretationreasoning§ Conclusion · p. 17A blanket ban of LLM-based tools at an institutional level is neither feasible nor enforceable.
“a blanket ban of these tools at an institutional level is neither feasible, nor enforceable.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S2 (checkable), S4 (based).
Interpretationnone given§ Conclusion · p. 16Whether any particular use of LLMs by students is defined as academic misconduct will be determined by the future policies of any given HEI.
“Deciding whether any particular use of LLMs by students may be defined as academic misconduct will be determined by the future policies of any given HEI”
Quote in the paperNot a substantial claim- Rests on an assumption: HEIs will set future policies governing LLM use.
May combine two claims.
Premisecitation§ GPT-3 studies · p. 14In Kumar et al. (2022), participants regardless of background found identifying GPT-3 text challenging, with a high likelihood of ascribing AI writing samples to humans.
“They found that regardless of background, participants found this to be a challenging task, with a high likelihood of ascribing the AI writing samples to humans.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Kumar et al. (2022)
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing).
Interpretationcitation§ Conclusion · p. 17The future development of LLMs and broader AI supported digital tools has a strong potential for improving the experiences of students and teachers alike in the next generation of HEI classrooms, both in writing instruction and beyond.
“the future development of LLMs and broader AI supported digital tools have a strong potential for improving the experiences of students and teachers alike in the next generation of HEI classrooms, both in writing instruction and beyond.”
Quote in the paperNot a substantial claimMay combine two claims.
Interpretationreasoning§ Technological methods of detection · p. 16Both academic staff and technological methods of detection are unable to accurately detect machine generated text and therefore student uses of LLM based tools.
“Given that both academic staff, as well as technological methods of detection are unable to accurately detect machine generated text and therefore student uses of LLM based tools”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S2 (checkable), S4 (based).
Interpretationreasoning§ Conclusion · p. 17The future integration of LLMs and other AI supported digital tools into the classroom environment is highly likely.
“we believe that the future integration of LLMs and other AI supported digital tools into the classroom environment is highly likely”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S4 (based).
Interpretationnone given§ Conclusion · p. 16The inability to accurately detect whether LLMs have been used presents a clear threat to the academic integrity of HEIs.
“the inability to accurately detect whether LLMs have been used presents a clear threat to the academic integrity of HEIs.”
Quote in the paperNot a substantial claim
The paper relies on this without support.
The ChatGPT output generated in the paper's demonstration is representative of LLM output generally.
“As an example of the fluency of the output that can be created using these tools, the two paragraphs of the section above titled: 'The emergence of Large Language Models' were used as an input to ChatGPT.”
The paper relies on this without support.
LLM-generated text is uniquely created based on the inputs provided.
“given that the text that is produced by LLMs is uniquely created based on the inputs provided”
The paper relies on this without support.
Many advanced DWAs have multiple features available to students which already claim to integrate AI technologies.
“Given that many of the more advanced DWAs have multiple features available to students which already claim to integrate AI technologies”
The paper relies on this without support.
Write Full is a full-service DWA which has integrated paraphrasing tools that may encourage students to use the work of others as inputs, rather than their own writing.
“This already appears to be the case in the example of one DWA called Write Full: a full-service DWA which has integrated paraphrasing tools which may encourage students to use the work of others as inputs, rather than their own writing.”
The paper relies on this without support.
New large language models will continue to be developed.
“as new LLMs are developed.”
The paper relies on this without support.
HEIs will set future policies governing LLM use.
“will be determined by the future policies of any given HEI”
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?
Universities have adopted varied approaches to generative AI: some have banned it or employed detection tools, while others permit and even encourage its use with appropriate acknowledgment.
“In response, universities have adopted varied approaches: some have banned generative AI or employed detection tools, while others permit and even encourage its use with appropriate acknowledgment (Perkins 2023; Sullivan, Kelly, and McLaughlan 2023; Xiao, Chen, and Bao 2023).”
From Addressing student non-compliance in AI use declarations: implications for academic integrity and assessment in higher education (2025) · cites this paper as Perkins 2023
The Practitioner Notes (p. 2) say disclosed AI use may not be plagiarism, and Academic integrity policy adjustments (p. 16) recommends a nuanced approach rather than a blanket ban. The paper discusses bans, detection, and disclosure as policy considerations, but the cited passages do not establish that universities have already adopted the varied approaches described. (LLM, conf=0.84)
Experimental — accuracy not yet measuredInitially, the use of GenAI tools in academic settings was largely viewed as a form of plagiarism or academic misconduct.
“Initially, the use of GenAI tools in academic settings was largely viewed as a form of plagiarism or academic misconduct (Cotton et al., 2023; Perkins, 2023).”
From The Artificial Intelligence Assessment Scale (AIAS): A Framework for Ethical Integration of Generative AI in Educational Assessment (2024) · cites this paper as Perkins, 2023
The Practitioner Notes (p. 2) identify potential plagiarism and academic-integrity breaches from student use of these tools, so the paper supports the concern underlying the claim. But the same passage says transparent use need not be plagiarism, and the discussion of acceptable use (p. 13) argues against a blanket ban. The paper does not establish that GenAI use was initially or largely viewed as misconduct. (LLM, conf=0.85)
Experimental — accuracy not yet measuredBanning or blocking GenAI tools has proven ineffective.
“Banning or blocking GenAI tools has proven ineffective, and punitive approaches ignore the potential benefits of these technologies.”
From The AI Assessment Scale (AIAS) in action: A pilot implementation of GenAI supported assessment (2024) · cites this paper as Perkins, 2023
The Academic integrity policy adjustments section (p. 16) says a blanket ban is likely unenforceable, and the Conclusion (p. 17) calls an institutional blanket ban neither feasible nor enforceable. These passages support the concern about banning, but they do not establish that bans or technical blocking have already proven ineffective. (LLM, conf=0.84)
Experimental — accuracy not yet measuredThis focus leads to a narrow perception of AI as only a source of academic malpractice.
“it leads to a narrow perception of AI as only a source of academic malpractice (Perkins, 2023).”
From Assessment-focused governance mode? Mapping Generative AI policies across 76 UK higher education institutions: Policy framework and recommendations (2026) · cites this paper as Perkins, 2023
The classroom-use section (p. 12) contrasts academic-integrity concerns with legitimate educational uses of AI, and the policy section (p. 16) calls for a nuanced approach that recognizes its benefits. These passages support the objection to viewing AI only as a source of malpractice, but they do not establish that a particular focus leads people to adopt that narrow perception. (LLM, conf=0.79)
Experimental — accuracy not yet measuredThe current study identified critical thinking, self-reflection, and data interpretation as areas where students could work collaboratively with generative AI tools without impinging on institutional academic integrity policies.
“The current study identified critical thinking, self-reflection, and data interpretation as areas where students could work collaboratively with generative AI tools without impinging on institutional academic integrity policies (Perkins, 2023).”
From Assessment design through co-creation (2025) · cites this paper as Perkins, 2023
The Abstract (p. 2) discusses human–AI co-creation, and Writing and composition (p. 12) describes collaborative writing as a possible use. The paper does not identify critical thinking, self-reflection, or data interpretation as the relevant areas. Practitioner Notes (p. 2) also cautions that even transparent AI use may breach an institution’s academic integrity policy, so collaboration is not necessarily policy-compliant. (LLM, conf=0.87)
Experimental — accuracy not yet measured
Unilateral deployment of AI detection software could breach students' due process rights, especially in ESL situations where language usage could be similar to AI-produced writing.
“The unilateral deployment of AI detection software could breach students' due process rights, especially in ESL situations where language usage could be similar to AI-produced writing [13].”
From Evaluating the Effectiveness and Ethical Implications of AI Detection Tools in Higher Education (2025) · cites this paper as [13]
In “Technological methods of detection” (p. 15), the paper warns that detectors may incorrectly label EFL students’ writing as machine-written and that untested tools could lead to inadvertent misconduct accusations. It does not address unilateral deployment or students’ due process rights, nor does it explain the false positives as a similarity between ESL language usage and AI writing. (LLM, conf=0.86)
Experimental — accuracy not yet measuredAI detectors offer scalable solutions but frequently produce false positives and lack transparency, especially for multilingual or non-native English speakers.
“While AI detectors offer scalable solutions, they frequently produce false positives and lack transparency, especially for multilingual or non-native English speakers.”
From Evaluating the Effectiveness and Ethical Implications of AI Detection Tools in Higher Education (2025) · cites this paper as [13]
In “Technological methods of detection” (p. 15), the paper warns that detectors may incorrectly label human writing as machine-generated, particularly for EFL students, and describes one promising low-cost model. It does not establish that false positives occur frequently or that detectors generally offer scalable solutions. Its discussion of limited transparency concerns LLMs, not detectors (“Limitations and newer LLMs,” p. 9). (LLM, conf=0.86)
Experimental — accuracy not yet measuredThe reliability and integration of AI detection tools into academic workflows remain inconsistent.
“However, as multiple studies have shown, their reliability and integration into academic workflows remain inconsistent [10,13].”
From Evaluating the Effectiveness and Ethical Implications of AI Detection Tools in Higher Education (2025) · cites this paper as [13]
The ‘Technological methods of detection’ section (p. 15) says detection tools’ accuracy and suitability for academic settings require further study, supporting concern about their reliability. It does not report that integration of detection tools into academic workflows is inconsistent; it calls for evaluation before such use. (LLM, conf=0.83)
Experimental — accuracy not yet measuredAs AI-generated content becomes more prevalent, institutions are revising academic integrity policies and assessment practices.
“As AI-generated content becomes more prevalent, institutions are revising academic integrity policies and assessment practices [13].”
From Evaluating the Effectiveness and Ethical Implications of AI Detection Tools in Higher Education (2025) · cites this paper as [13]
The Academic integrity policy adjustments section (p. 16) says institutions need to develop policies that clarify students’ use of LLM tools, and Technological methods of detection (p. 15) calls for adjustments in practice and policy. These are recommendations, not evidence that institutions are already revising their policies or assessment practices in response to AI-generated content. (LLM, conf=0.87)
Experimental — accuracy not yet measuredThese discrepancies confuse students and faculty, leading to uneven enforcement and potential legal risks for institutions that fail to maintain fair and consistent procedures.
“These discrepancies confuse students and faculty, leading to uneven enforcement and potential legal risks for institutions that fail to maintain fair and consistent procedures [13].”
From Evaluating the Effectiveness and Ethical Implications of AI Detection Tools in Higher Education (2025) · cites this paper as [13]
The Definitions section (p. 10) says that institutions define academic misconduct differently and that students may unintentionally breach policies. Academic integrity policy adjustments (p. 16) calls for unambiguous rules that students and staff can understand. These passages support the concern about confusion, but do not establish that policy discrepancies lead to uneven enforcement or legal risks. (LLM, conf=0.84)
Experimental — accuracy not yet measuredLarge language models excel at synthesizing domain-specific technical concepts and mimicking natural linguistic fluency, making traditional written tasks increasingly vulnerable to unearned authorship and cognitive shortcut.
“Because large language models (LLMs) excel at synthesizing domainspecific technical concepts and mimicking natural linguistic fluency, traditional written tasksuch as take-home assignments, reflective reports, or online language modules-are increasingly vulnerable to unearned authorship and cognitive shortcut (Cotton et al., 2023;Perkins, 2023).”
From Navigating Academic Integrity and Student Rationalisations of Generative AI in Vocational Online Learning Environments (2026) · cites this paper as Perkins, 2023
The Examples section (p. 6) demonstrates fluent, human-like academic prose and discusses generating papers for assignments; the Cognitive offloading section (p. 11) says LLMs can reduce a task’s cognitive demands. The paper also raises academic-integrity concerns about student submissions, but it does not establish that LLMs excel at synthesizing domain-specific technical concepts or address the particular task types listed in the citing sentence. (LLM, conf=0.83)
Experimental — accuracy not yet measuredThe effectiveness of conventional anti-plagiarism software, such as iThenticate and Turnitin, remains questionable against AI-generated content.
“Despite advancements in AI detection tools, the effectiveness of conventional anti-plagiarism software, such as iThenticate and Turnitin, remains questionable against AI-generated content (Perkins, 2023;Chaka, 2023Chaka, , 2024;;Hassoulas et al., 2023;Ifelebuegu, 2023;Mohammadkarimi et al., 2023;Sullivan et al., 2023).”
From Higher Education's Generative Artificial Intelligence Paradox: The Meaning of Chatbot Mania (2024) · cites this paper as Perkins, 2023
These updates aim to clarify what constitutes academic misconduct when AI is involved, drawing a line between legitimate support and dishonest automation.
“These updates aim to clarify what constitutes academic misconduct when AI is involved, drawing a line between legitimate support and dishonest automation [13].”
From Evaluating the Effectiveness and Ethical Implications of AI Detection Tools in Higher Education (2025) · cites this paper as [13]
AI-generated content challenges traditional definitions of authorship and originality.
“As Cotton, Cotton, & Shipway (2024) note, AI-generated content challenges traditional definitions of authorship and originality, a concern echoed by Perkins (2023).”
From Generative AI-Enhanced Oral Exams in Economics Education (2026) · cites this paper as Perkins (2023)
Experts in our study were aware of the limitations of LLMs and cited similar apprehensions to those raised in the literature.
“At the same time, experts in our study were aware of the limitations of LLMs and cited similar apprehensions to those raised in the literature (Dwivedi et al. 2023;OpenAI 2022;Perkins 2023).”
From Friend or foe? Exploring the implications of large language models on the science system (2025) · cites this paper as Perkins 2023
To date, scholars have primarily focused on the implications of LLMs on education, with limited attention to their impact on science and scientific practices.
“To date, scholars have primarily focused on the implications of LLMs on education (e.g., Perkins 2023; Fyfe 2023) with limited attention being paid to their impact on science and scientific practices (for exceptions, see Chubb et al. 2022;Ribeiro et al. 2023).”
From Friend or foe? Exploring the implications of large language models on the science system (2025) · cites this paper as Perkins 2023
This adds a new and fresh direction to the LLM discussion, which has focused heavily on the impact of this technology for individual academics and learners.
“This adds a new and fresh direction to the LLM discussion, which has focused heavily on the impact of this technology for individual academics and learners (Fyfe 2023;Perkins 2023).”
From Friend or foe? Exploring the implications of large language models on the science system (2025) · cites this paper as Perkins 2023
Chatbots present a substantial challenge concerning maintaining academic integrity and copyright violations, which are significant concerns in education.
“chatbots present a substantial challenge concerning maintaining academic integrity [20, 56] and copyright violations [83], which are significant concerns in education”
From A systematic literature review of empirical research on ChatGPT in education (2024) · cites this paper as [56]
LLMs provide an obvious potential danger to academic integrity because instructors may be unable to accurately determine the quantity of information generated by a student and hence offer an appropriate evaluation of a student's grasp and interpretation.
“Indicating that LLMs provide an obvious potential danger to academic integrity since instructors may be unable to accurately determine the quantity of information generated by a student and hence offer an appropriate evaluation of a student’s grasp and interpretation of the issue at hand.”
From ChatGPT Unveiled: Understanding Perceptions of Academic Integrity in Higher Education - A Qualitative Approach (2025) · cites this paper as Perkins (2023)
Cites
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017) · arXiv · doi:10.48550/arxiv.1706.03762 · Cited by 120,404 · see citing works
Jakubův, J., Chvalovský, K., Goertzel, Z., et al. (2023) · DROPS (Schloss Dagstuhl – Leibniz Center for Informatics) · doi:10.4230/lipics.itp.2023.19 · Cited by 76,648 · see citing works
On the Opportunities and Risks of Foundation Models
Bommasani, R., Hudson, D. A., Adeli, E., Altman, R., Arora, S., von Arx, S., Bernstein, M. S., Bohg, J., Bosselut, A., Brunskill, E., et al. (2021) · arXiv · doi:10.48550/arxiv.2108.07258 · Cited by 2,676 · see citing works
Risko, E. F., & Gilbert, S. J. (2016) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2016.07.002 · Cited by 1,502 · cited 1× · see citing works
“Therefore, we may consider LLMs as another potential tool for reducing the cognitive demands required by a task, described by Risko and Gilbert (2016) as a process called cognitive offloading.”
Balancing Technology, Pedagogy and the New Normal: Post-pandemic Challenges for Higher Education
Rapanta, C., Botturi, L., Goodyear, P., Ortiz, L. G., & Koole, M. L. (2021) · Postdigital Science and Education · doi:10.1007/s42438-021-00249-1 · Cited by 632 · see citing works
Multitask Prompted Training Enables Zero-Shot Task Generalization
Sanh, V., Webson, A., Raffel, C., Bach, S. H., Sutawika, L., Alyafeai, Z., Chaffin, A., Stiegler, A., Scao, T. L., Raja, A., et al. (2021) · arXiv · doi:10.48550/arxiv.2110.08207 · Cited by 612 · see citing works
Artificial intelligence for education: Knowledge and its assessment in AI-enabled learning ecologies
Cope, B., Kalantzis, M., & Searsmith, D. (2021) · Educational Philosophy and Theory · doi:10.1080/00131857.2020.1728732 · Cited by 518 · see citing works
GLTR: Statistical Detection and Visualization of Generated Text
Gehrmann, S., Strobelt, H., & Rush, A. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations · doi:10.18653/v1/p19-3019 · Cited by 470 · cited 2× · see citing works
“Technological methods to identify text generated by LLMs have been proposed by Gehrmann et al. (2019) who present the results of a study using a tool named GLTR.”
Dale, R. (2020) · Natural Language Engineering · doi:10.1017/s1351324920000601 · Cited by 461 · see citing works
GLTR: Statistical Detection and Visualization of Generated Text
Gehrmann, S., Strobelt, H., & Rush, A. M. (2019) · arXiv · doi:10.48550/arxiv.1906.04043 · Cited by 460
Digital technology use during <scp>COVID</scp> ‐19 pandemic: A rapid review
Vargo, D., Zhu, L., Benwell, B., & Yan, Z. (2021) · Human Behavior and Emerging Technologies · doi:10.1002/hbe2.242 · Cited by 459 · see citing works
Köbis, N., & Mossink, L. D. (2021) · Computers in Human Behavior · doi:10.1016/j.chb.2020.106553 · Cited by 406 · see citing works
Nazari, N., Shabbir, M. S., & Setiawan, R. (2021) · Heliyon · doi:10.1016/j.heliyon.2021.e07014 · Cited by 405 · see citing works
Lee, M., Liang, P., & Yang, Q. (2022) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3491102.3502030 · Cited by 345 · cited 1× · see citing works
“…AI based technologies, there is ongoing research exploring how the intricacies of co-creation may be further be codified and explored in the fields of creative writing (M. Lee et al., 2022; Y. Lee et al., 2022), software development (Biderman & Raff, 2022) and artistic outputs (Oppenlaender, 2022).”
Exploring an AI-based writing Assistant's impact on English language learners
Gayed, J. M., Carlon, M. K. J., Oriola, A. M., & Cross, J. S. (2022) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2022.100055 · Cited by 340 · see citing works
A taxonomy of prompt modifiers for text-to-image generation
Oppenlaender, J. (2023) · Behaviour and Information Technology · doi:10.1080/0144929x.2023.2286532 · Cited by 321 · see citing works
Contract cheating by STEM students through a file sharing website: a Covid-19 pandemic perspective
Lancaster, T., & Cotarlan, C. (2021) · International Journal for Educational Integrity · doi:10.1007/s40979-021-00070-0 · Cited by 312 · cited 2× · see citing works
“…the COVID-19 pandemic has caused an increase in both the detected number of academic dishonesty cases (Henderson et al., 2022; Jenkins et al., 2022; Lancaster & Cotarlan, 2021), as well as increases in student or academic staff perceptions of academic dishonesty occurring (Amzalag et al., 2021;…”
Digital support for academic writing: A review of technologies and pedagogies
Strobl, C., Ailhaud, E., Benetos, K., Devitt, A., Kruse, O., Proske, A., & Rapp, C. (2019) · Computers & Education · doi:10.1016/j.compedu.2018.12.005 · Cited by 298 · see citing works
Plagiarism in the age of massive generative pre-trained transformers (GPT-3)
Dehouche, N. (2021) · Ethics in Science and Environmental Politics · doi:10.3354/esep00195 · Cited by 296 · see citing works
Release Strategies and the Social Impacts of Language Models
Solaiman, I., Brundage, M., Clark, J., Askell, A., Herbert-Voss, A., Wu, J., Radford, A., Krueger, G., Kim, J. W., Kreps, S., et al. (2019) · arXiv · doi:10.48550/arxiv.1908.09203 · Cited by 292 · see citing works
All That’s ‘Human’ Is Not Gold: Evaluating Human Evaluation of Generated Text
Clark, E., August, T., Serrano, S., Haduong, N., Gururangan, S., & Smith, N. A. (2021) · Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) · doi:10.18653/v1/2021.acl-long.565 · Cited by 290 · see citing works
All That's 'Human' Is Not Gold: Evaluating Human Evaluation of Generated Text
Clark, E., August, T., Serrano, S., Haduong, N., Gururangan, S., & Smith, N. A. (2021) · arXiv · doi:10.48550/arxiv.2107.00061 · Cited by 290
Partnering with AI: Intelligent writing assistance and instructed language learning
Godwin-Jones, R. (2022) · Language Learning & Technology · doi:10.64152/10125/73474 · Cited by 285 · see citing works
Automatic Detection of Generated Text is Easiest when Humans are Fooled
Ippolito, D., Duckworth, D., Callison-Burch, C., & Eck, D. (2019) · arXiv · doi:10.48550/arxiv.1911.00650 · Cited by 264 · cited 1×
“Solaiman et al. (2019) and Ippolito et al.(2020) also both present tools which showed an encouraging ability in detecting machine created text, although these tools were not tested against the latest generation of LLMs. Fröhling & Zubiaga (2021) present a promising low cost detection model which is…”
Automatic Detection of Generated Text is Easiest when Humans are Fooled
Ippolito, D., Duckworth, D., Callison-Burch, C., & Eck, D. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2020.acl-main.164 · Cited by 264 · see citing works
Automated Writing Assessment in the Classroom
Warschauer, M., & Grimes, D. (2008) · Pedagogies: An International Journal · doi:10.1080/15544800701771580 · Cited by 243 · see citing works
Cheating in the Digital Age: Do Students Cheat More in Online Courses?.
Watson, G., & Sottile, J. (2010) · Online journal of distance learning administration · Cited by 242 · see citing works
Could AI help you to write your next paper?
Hutson, M. (2022) · Nature · doi:10.1038/d41586-022-03479-w · Cited by 236 · see citing works
Academic Honesty and Online Courses
Grijalva, T. C., Nowell, C., & Kerkvliet, J. R. (2006) · Cited by 222 · see citing works
Grammarly as AI-powered English Writing Assistant: Students' Alternative for Writing English
Fitria, T. N. (2021) · Metathesis: Journal of English Language, Literature, and Teaching · doi:10.31002/metathesis.v5i1.3519 · Cited by 221 · see citing works
Academic Integrity and Distance Learning
Lanier, M. M. (2006) · Journal of Criminal Justice Education · doi:10.1080/10511250600866166 · Cited by 210 · see citing works
Zhao, X. (2023) · RELC Journal · doi:10.1177/00336882221094089 · Cited by 192 · see citing works
The great Transformer: Examining the role of large language models in the political economy of AI
Luitse, D. M. R., & Denkena, W. (2021) · Big Data & Society · doi:10.1177/20539517211047734 · Cited by 191 · see citing works
Stop! Grammar time: University students’ perceptions of the automated feedback program Grammarly
ONeill, R., & Russell, A. (2019) · Australasian Journal of Educational Technology · doi:10.14742/ajet.3795 · Cited by 180 · see citing works
Using Internet based paraphrasing tools: Original work, patchwriting or facilitated plagiarism?
Rogerson, A. M., & McCarthy, G. (2017) · International Journal for Educational Integrity · doi:10.1007/s40979-016-0013-y · Cited by 179 · see citing works
A review of AWE feedback: types, learning outcomes, and implications
Fu, Q.-K., Zou, D., Xie, H., & Cheng, G. (2022) · Computer Assisted Language Learning · doi:10.1080/09588221.2022.2033787 · Cited by 173 · see citing works
Reedy, A., Pfitzner, D., Rook, L., & Ellis, L. (2021) · International Journal for Educational Integrity · doi:10.1007/s40979-021-00075-9 · Cited by 168 · see citing works
Point, Click, and Cheat: Frequency and Type of Academic Dishonesty in the Virtual Classroom
Stuber, D., Wiseley, P., & Hoggatt, S. (2009) · Online journal of distance learning administration · Cited by 147 · see citing works
Beyond "Gotcha!": Situating Plagiarism in Policy and Pedagogy
Price, M. (2002) · College Composition and Communication · doi:10.2307/1512103 · Cited by 118 · see citing works
Amzalag, M., Shapira, N., & Dolev, N. (2022) · Journal of Academic Ethics · doi:10.1007/s10805-021-09413-5 · Cited by 118 · see citing works
Biermann, O. C., Ma, N. F., & Yoon, D. (2022) · Designing Interactive Systems Conference · doi:10.1145/3532106.3533506 · Cited by 116 · see citing works
Feature-based detection of automated language models: tackling GPT-2, GPT-3 and Grover
Fröhling, L., & Zubiaga, A. (2021) · PeerJ Computer Science · doi:10.7717/peerj-cs.443 · Cited by 106 · see citing works
COVID‐19 as the tipping point for integrating e‐assessment in higher education practices
St‐Onge, C., Ouellet, K., Lakhal, S., Dubé, T. V., & Marceau, M. (2021) · British Journal of Educational Technology · doi:10.1111/bjet.13169 · Cited by 105 · see citing works
Paraphrasing tools, language translation tools and plagiarism: an exploratory study
Prentice, F. M., & Kinden, C. E. (2018) · International Journal for Educational Integrity · doi:10.1007/s40979-018-0036-7 · Cited by 99 · see citing works
When Opportunity Knocks: College Students' Cheating Amid the COVID-19 Pandemic
Jenkins, B. D., Golding, J. M., Le Grand, A. M., Levi, M. M., & Pals, A. M. (2023) · Teaching of Psychology · doi:10.1177/00986283211059067 · Cited by 85 · see citing works
Roe, J., & Perkins, M. (2022) · International Journal for Educational Integrity · doi:10.1007/s40979-022-00109-w · Cited by 83 · see citing works
Self-citationThe automated writing assistance landscape in 2021
Dale, R., & Viethen, J. (2021) · Natural Language Engineering · doi:10.1017/s1351324921000164 · Cited by 81 · see citing works
Re-imagining University Assessment in a Digital World
Bearman, M., Dawson, P., Ajjawi, R., Tai, J., & Boud, D. J. (2020) · The enabling power of assessment · doi:10.1007/978-3-030-41956-1 · Cited by 80 · see citing works
(2016) · Grantee Submission · Cited by 79 · see citing works
Fatemi, G., & Saito, E. (2019) · Journal of Further and Higher Education · doi:10.1080/0309877x.2019.1683521 · Cited by 78 · see citing works
Nobles, S., & Paganucci, L. (2015) · Computers & composition/Computers and composition · doi:10.1016/j.compcom.2015.09.001 · Cited by 76 · see citing works
(2016) · Journal of academic language and learning · Cited by 75 · see citing works
Developing a corpus-based paraphrase tool to improve EFL learners' writing skills
Chen, M.-H., Huang, S.-T., Chang, J. S., & Liou, H.-C. (2015) · Computer Assisted Language Learning · doi:10.1080/09588221.2013.783873 · Cited by 72 · see citing works
Assisting academics to identify computer generated writing
Abd-Elaal, E.-S., Gamage, S. H. P. W., & Mills, J. E. (2022) · European Journal of Engineering Education · doi:10.1080/03043797.2022.2046709 · Cited by 62 · see citing works
Walsh, L. L., Lichti, D. A., Zambrano-Varghese, C. M., Borgaonkar, A. D., Sodhi, J. S., Moon, S., Wester, E. R., & Callis-Duehl, K. L. (2021) · International Journal for Educational Integrity · doi:10.1007/s40979-021-00089-3 · Cited by 61 · see citing works
Teaching Models to Express Their Uncertainty in Words
Lin, S., Hilton, J., & Evans, O. (2022) · arXiv · doi:10.48550/arxiv.2205.14334 · Cited by 59 · see citing works
Identifying Machine-Paraphrased Plagiarism
Wahle, J. P., Ruas, T., Foltýnek, T., Meuschke, N., & Gipp, B. (2022) · Lecture notes in computer science · doi:10.1007/978-3-030-96957-8_34 · Cited by 52 · see citing works
Perkins, M., Gezgin, U. B., & Roe, J. (2018) · Journal of Academic Ethics · doi:10.1007/s10805-018-9311-8 · Cited by 47 · see citing works
Self-citationCognitive Offloading and Assessment
Dawson, P. (2020) · The enabling power of assessment · doi:10.1007/978-3-030-41956-1_4 · Cited by 46 · see citing works
Sun, Y.-C. (2013) · Journal of English for Academic Purposes · doi:10.1016/j.jeap.2013.07.002 · Cited by 45 · see citing works
Kaqinari, T., Makarova, E., Audran, J., Döring, A. K., Göbel, K., & Kern, D. (2021) · Journal of University Teaching and Learning Practice · doi:10.53761/1.18.5.10 · Cited by 44 · see citing works
Prompt Engineering for Text-Based Generative Art
Oppenlaender, J. (2022) · arXiv.org · doi:10.48550/arxiv.2204.13988 · Cited by 44
Factors associated with online examination cheating
Henderson, M., Chung, J., Awdry, R., Mundy, M., Bryant, M., Ashford, C., & Ryan, K. (2023) · Assessment & Evaluation in Higher Education · doi:10.1080/02602938.2022.2144802 · Cited by 43 · see citing works
Cheaters on Twitter: an analysis of engagement approaches of contract cheating services
Amigud, A. (2019) · Studies in Higher Education · doi:10.1080/03075079.2018.1564258 · Cited by 42 · see citing works
Fooling MOSS Detection with Pretrained Language Models
Biderman, S., & Raff, E. (2022) · arXiv · doi:10.48550/arxiv.2201.07406 · Cited by 37 · see citing works
Fooling MOSS Detection with Pretrained Language Models
Biderman, S., & Raff, E. (2022) · Proceedings of the 31st ACM International Conference on Information & Knowledge Management · doi:10.1145/3511808.3557079 · Cited by 37 · see citing works
Electric Sheep? Humans, Robots, Artificial Intelligence, and the Future of Writing
McKnight, L. (2021) · Changing English · doi:10.1080/1358684x.2021.1941768 · Cited by 36 · see citing works
How Large Language Models are Transforming Machine-Paraphrase Plagiarism
Wahle, J. P., Ruas, T., Kirstein, F., & Gipp, B. (2022) · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2022.emnlp-main.62 · Cited by 35 · see citing works
Computer-based writing tools and the cognitive needs of novice writers
Kozma, R. (1991) · Computers & composition/Computers and composition · doi:10.1016/8755-4615(91)80046-g · Cited by 30 · see citing works
Are Neural Language Models Good Plagiarists? A Benchmark for Neural Paraphrase Detection
Wahle, J. P., Ruas, T., Meuschke, N., & Gipp, B. (2021) · ACM/IEEE Joint Conference on Digital Libraries · doi:10.1109/jcdl52503.2021.00065 · Cited by 29 · see citing works
Gunser, V. E., Gottschling, S., Brucker, B., Richter, S., & Gerjets, P. (2021) · Communications in Computer and Information Science · doi:10.1007/978-3-030-78635-9_67 · Cited by 25 · see citing works
DSpin: Detecting Automatically Spun Content on the Web
Zhang, Q., Wang, D. Y., & Voelker, G. M. (2014) · Network and Distributed System Security Symposium · doi:10.14722/ndss.2014.23004 · Cited by 24 · see citing works
Interactive Children’s Story Rewriting Through Parent-Children Interaction
Lee, Y.-J., Kim, T. S., Chang, M., & Kim, J. (2022) · Workshop on Intelligent and Interactive Writing Assistants (In2Writing) · doi:10.18653/v1/2022.in2writing-1.9 · Cited by 22 · see citing works
Neural automated writing evaluation for Korean L2 writing
(2022) · doi:10.1017/s1351324922000298 · Cited by 15 · see citing works
How Large Language Models are Transforming Machine-Paraphrased PlagiarismPreprint
Wahle, J. P., Ruas, T., Kirstein, F., & Gipp, B. (2023) · doi:10.22541/au.167528149.95939897/v1 · Cited by 12 · see citing works
Parkinson, A. L., Hatje, E., Kynn, M., Kuballa, A., Donkin, R., & Reinke, N. B. (2022) · Assessment & Evaluation in Higher Education · doi:10.1080/02602938.2022.2040947 · Cited by 11 · see citing works
Plagiarism in higher education: classification, causes and controls
Perkins, M., Gezgin, U. B., & Gordon, R. D. (2019) · doi:10.13140/rg.2.2.20694.11841 · Cited by 9 · see citing works
Self-citationUtilizing an AI-Based Grammar Checker in an EFL Writing Classroom
Park, J., & Yang, I. Y. (2020) · 응용언어학 · doi:10.17154/kjal.2020.3.36.1.97 · Cited by 8 · see citing works
Information for a Better World: Shaping the Global Future
(2022) · Lecture notes in computer science · doi:10.1007/978-3-030-96957-8 · Cited by 7 · see citing works
Artificial Intelligence, Algorithmic Writing & Educational Ethics
(2021) · Cited by 4 · see citing works
HCI International 2021 - Posters
(2021) · Communications in computer and information science · doi:10.1007/978-3-030-78635-9 · Cited by 2 · see citing works
Model Agnostic Interpretability for Multiple Instance Learning
Early, J., Evers, C., & Ramchurn, S. D. (2022) · ePrints Soton (University of Southampton) · doi:10.48550/arxiv.2201.11701 · Cited by 2 · see citing works
OPT: Open Pre-trained Transformer Language Models
Zhang, S., Roller, S., Goyal, N., Artetxe, M., Chen, M., Chen, S., Dewan, C., Diab, M., Li, X., Lin, X. V., et al. (2022) · arXiv · doi:10.48550/arxiv.2205.01068
How Large Language Models are Transforming Machine-Paraphrased Plagiarism
Wahle, J. P., Ruas, T., Kirstein, F., et al. (2022) · arXiv · doi:10.48550/arxiv.2210.03568
Flagged first, then by citations
Research works printed but not linked: 6
- Curtis, G. J. (2022). Trends in plagiarism and cheating prevalence: 1990-2020 and beyond. In D. Rettinger & T. Bertram Gallant (Eds.), Cheating academic integrity (pp. 11-44). Jossey-Bass. https://www.wiley.com/enau/Cheating+Academic+Integrity%3A+Lessons+from+30+Years+of+Research-p9781119868170
- Dinneen, C. (2021). Students' use of digital translation and paraphrasing tools in written assignments on Direct Entry English Programs. English Australia Journal , 37 (1), 40-51. https://doi.org/10.3316/informit.748262877947586 · DOI
- Kumar, R., Mindzak, M., Eaton, S. E., & Morrison, R. (2022). AI & AI: Exploring the contemporary intersections of artificial intelligence and academic integrity . Canadian Society for the Study of Higher Education Annual Conference, Online. Werklund School of Education. https://tinyurl.com/ycknz8fd
- Kumar, R., Mindzak, M., & Racz, R. (2022). Who Wrote This? The Use of Artificial Intelligence in the Academy . http://hdl.handle.net/10464/16532
- Miller, A., & Young-Jones, A. D. (2012). Academic integrity: Online classes compared to face-toface classes. Journal of Instructional Psychology , 39 (3-4), Article 3-4. https://tinyurl.com/mrxnp9zy
- Wilder, N., Weßels, D., Gröpler, J., Klein, A., & Mundorf, M. (2021). Who is Responsible for Integrity in the Age of Artificial Intelligence? An Analysis Using the Example of Academic Writing. European Conference on Academic Integrity and Plagiarism 2021 , 179-181. https://tinyurl.com/2s4r5dyr
Cited by
Sorted by citations
What Is the Impact of ChatGPT on Education? A Rapid Review of the Literature
Lo, C. K. (2023) · Education Sciences · doi:10.3390/educsci13040410 · Cited by 2,024 · see citing works
New Era of Artificial Intelligence in Education: Towards a Sustainable Multifaceted Revolution
Kamalov, F., Santandreu Calonge, D., & Gurrib, I. (2023) · Sustainability · doi:10.3390/su151612451 · Cited by 1,089 · see citing works
“The new technology is “a clear threat to academic integrity for HEIs, requiring a range of adjustments to be made in both practice and policy” [53].”
A survey on large language model (LLM) security and privacy: The Good, The Bad, and The Ugly
Yao, Y., Duan, J., Xu, K., Cai, Y., Sun, Z., & Zhang, Y. (2024) · High-Confidence Computing · doi:10.1016/j.hcc.2024.100211 · Cited by 1,083 · see citing works
Grassini, S. (2023) · Education Sciences · doi:10.3390/educsci13070692 · Cited by 1,048 · see citing works
Strzelecki, A. (2024) · Interactive Learning Environments · doi:10.1080/10494820.2023.2209881 · Cited by 807 · see citing works
Bahroun, Z., Anane, C., Ahmed, V., & Zacca, A. (2023) · Sustainability · doi:10.3390/su151712983 · Cited by 784 · see citing works
Sajja, R., Sermet, Y., Cikmaz, M., Cwiertny, D., & Demir, I. (2024) · Information · doi:10.3390/info15100596 · Cited by 620 · see citing works
Crawford, J., Cowling, M., & Allen, K.-A. (2023) · Journal of University Teaching and Learning Practice · doi:10.53761/1.20.3.02 · Cited by 547 · see citing works
“There is an implied assumption in the emergent literature that students will use ChatGPT-3 to write assignments for them (e.g., Pavlik, 2023; Perkins, 2023).”
Unlocking the opportunities through ChatGPT Tool towards ameliorating the education system
Javaid, M., Haleem, A., Singh, R. P., Khan, S., & Khan, I. H. (2023) · BenchCouncil Transactions on Benchmarks, Standards and Evaluations · doi:10.1016/j.tbench.2023.100115 · Cited by 535 · see citing works
Strzelecki, A. (2024) · Innovative Higher Education · doi:10.1007/s10755-023-09686-1 · Cited by 477 · see citing works
A Scoping Review on How Generative Artificial Intelligence Transforms Assessment in Higher Education
Xia, Q., Weng, X., Ouyang, F., Lin, T. J., & Chiu, T. K. F. (2024) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-024-00468-z · Cited by 493 · see citing works
Generative 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 · see citing works
(2023) · Journal of Applied Learning & Teaching · doi:10.37074/jalt.2023.6.1.23 · Cited by 421 · see citing works
Law, L. (2024) · Computers and Education Open · doi:10.1016/j.caeo.2024.100174 · Cited by 596 · see citing works
Elkhatat, A. M., Elsaid, K., & Almeer, S. (2023) · International Journal for Educational Integrity · doi:10.1007/s40979-023-00140-5 · Cited by 392 · see citing works
“Academic misconduct in undergraduate education using ChatGPT has been widely studied (Crawford et al. 2023; King & chatGpt 2023; Lee 2023; Perkins 2023; Sullivan; et al. 2023).”
Tiwari, C. K., Bhat, M. A., Khan, S. T., Subramaniam, R., & Khan, M. A. I. (2024) · Interactive Technology and Smart Education · doi:10.1108/itse-04-2023-0061 · Cited by 315 · see citing works
Prompting Higher Education Towards AI-Augmented Teaching and Learning Practice
Eager, B., & Brunton, R. (2023) · Journal of University Teaching and Learning Practice · doi:10.53761/1.20.5.02 · Cited by 287 · see citing works
Generative AI in Higher Education: Balancing Innovation and Integrity
Francis, N. J., Jones, S., & Smith, D. P. (2025) · British Journal of Biomedical Science · doi:10.3389/bjbs.2024.14048 · Cited by 295 · see citing works
“Mitigating these risks involves developing clear guidelines for GenAI use in assessments [23] (see Institutional Strategies and Governance below).”
Wang, H., Dang, A., Wu, Z., & Mac, S. (2024) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2024.100326 · Cited by 272 · see citing works
Perkins, M., Furze, L., Roe, J., & MacVaugh, J. (2024) · Journal of University Teaching and Learning Practice · doi:10.53761/q3azde36 · Cited by 291 · see citing works
Self-citationChatGPT in education: Methods, potentials, and limitations
Memarian, B., & Doleck, T. (2023) · Computers in Human Behavior: Artificial Humans · doi:10.1016/j.chbah.2023.100022 · Cited by 266 · see citing works
Strzelecki, A., & ElArabawy, S. (2024) · British Journal of Educational Technology · doi:10.1111/bjet.13425 · Cited by 260 · see citing works
Karataş, F., Abedi, F. Y., Ozek Gunyel, F., Karadeniz, D., & Kuzgun, Y. (2024) · Education and Information Technologies · doi:10.1007/s10639-024-12574-6 · Cited by 252 · see citing works
Generative AI and Teachers’ Perspectives on Its Implementation in Education
Kaplan-Rakowski, R., Grotewold, K., Hartwick, P., & Papin, K. (2023) · Journal of Interactive Learning Research · doi:10.70725/815246mfssgp · Cited by 169 · see citing works
Higher education students’ perceptions of ChatGPT: A global study of early reactions
Ravšelj, D., Keržič, D., Tomaževič, N., Umek, L., Brezovar, N., A. Iahad, N., Abdulla, A. A., Akopyan, A., Aldana Segura, M. W., AlHumaid, J., et al. (2025) · PLOS ONE · doi:10.1371/journal.pone.0315011 · Cited by 376 · see citing works
La nueva realidad de la educación ante los avances de la inteligencia artificial generativa
García Peñalvo, F. J., Llorens-Largo, F., & Vidal, J. (2023) · RIED-Revista Iberoamericana de Educación a Distancia · doi:10.5944/ried.27.1.37716 · Cited by 238 · see citing works
Using ChatGPT in academic writing is (not) a form of plagiarism: What does the literature say?
Jarrah, A. M., Wardat, Y., & Fidalgo, P. (2023) · Online Journal of Communication and Media Technologies · doi:10.30935/ojcmt/13572 · Cited by 220 · see citing works
Crawford, J., Allen, K.-A., Pani, B., & Cowling, M. (2024) · Studies in Higher Education · doi:10.1080/03075079.2024.2326956 · Cited by 224 · see citing works
Tsai, M.-L., Chong, W.-O., & Chen, C.-L. (2023) · Education for Chemical Engineers · doi:10.1016/j.ece.2023.05.001 · Cited by 171 · see citing works
Ansari, A. N., Ahmad, S., & Bhutta, S. M. (2024) · Education and Information Technologies · doi:10.1007/s10639-023-12223-4 · Cited by 198 · see citing works
Perkins, M., Roe, J., Postma, D., McGaughran, J., & Hickerson, D. (2024) · Journal of Academic Ethics · doi:10.1007/s10805-023-09492-6 · Cited by 234 · see citing works
Self-citationSohail, S. S., Farhat, F., Himeur, Y., Nadeem, M., Madsen, D. Ø., Singh, Y., Atalla, S., & Mansoor, W. (2023) · Journal of King Saud University - Computer and Information Sciences · doi:10.1016/j.jksuci.2023.101675 · Cited by 192 · see citing works
“Concern has also been expressed by a group of scholars who anticipate how ChatGPT could impact writing in both academic (Perkins, 2023) and scientific fields (Ali and Djalilian, 2023b; Tregoning, 2023).”
Kar, A. K., Varsha, P. S., & Rajan, S. (2023) · Global Journal of Flexible Systems Management · doi:10.1007/s40171-023-00356-x · Cited by 209 · see citing works
Dabis, A., & Csáki, C. (2024) · Humanities and Social Sciences Communications · doi:10.1057/s41599-024-03526-z · Cited by 181 · see citing works
“This fi erce competition generates gaps in real-time in everyday and academic life, the latter of which is also trying to make sense of the rapid technological advancement and its effects on university-level education (Perkins, 2023).”
Gruenhagen, J. H., Sinclair, P. M., Carroll, J.-A., Baker, P. R. A., Wilson, A., & Demant, D. (2024) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2024.100273 · Cited by 180 · see citing works
A systematic literature review of empirical research on ChatGPT in education
Albadarin, Y., Saqr, M., Pope, N., & Tukiainen, M. (2024) · Discover Education · doi:10.1007/s44217-024-00138-2 · Cited by 191 · see citing works
Samala, A. D., Rawas, S., Wang, T., Reed, J. M., Kim, J., Howard, N.-J., & Ertz, M. (2025) · Education and Information Technologies · doi:10.1007/s10639-024-12936-0 · Cited by 229 · see citing works
Kelly, A., Sullivan, M., & Strampel, K. (2023) · Journal of University Teaching and Learning Practice · doi:10.53761/1.20.6.12 · Cited by 177 · see citing works
Gao, Y., Wang, Q., & Wang, X. (2024) · Asia Pacific Journal of Education · doi:10.1080/02188791.2024.2305173 · Cited by 196 · see citing works
Generative <scp>AI</scp> in education and research: A systematic mapping review
Yusuf, A., Pervin, N., Román‐González, M., & Noor, N. M. (2024) · Review of Education · doi:10.1002/rev3.3489 · Cited by 182 · see citing works
Wang, M., Wang, M., Xu, X., Yang, L., Cai, D., & Yin, M. (2023) · IEEE Transactions on Learning Technologies · doi:10.1109/tlt.2023.3324714 · Cited by 176 · see citing works
Peláez‐Sánchez, I. C., Velarde-Camaqui, D., & Glasserman‐Morales, L. D. (2024) · Frontiers in Education · doi:10.3389/feduc.2024.1392091 · Cited by 182 · see citing works
Exploring the impact of ChatGPT on education: A web mining and machine learning approach
Rejeb, A., Rejeb, K., Appolloni, A., Treiblmaier, H., & Iranmanesh, M. (2024) · The International Journal of Management Education · doi:10.1016/j.ijme.2024.100932 · Cited by 181 · see citing works
Nguyen, A., Kremantzis, M., Essien, A., Petrounias, I., & Hosseini, S. (2024) · Journal of University Teaching and Learning Practice · doi:10.53761/caraaq92 · Cited by 146 · see citing works
ChatGPT in higher education - a synthesis of the literature and a future research agenda
Bhullar, P. S., Joshi, M., & Chugh, R. (2024) · Education and Information Technologies · doi:10.1007/s10639-024-12723-x · Cited by 139 · see citing works
A Design Space for Intelligent and Interactive Writing Assistants
Lee, M., Gero, K. I., Chung, J. J. Y., Shum, S. B., Raheja, V., Shen, H., Venugopalan, S., Wambsganss, T., Zhou, D., Alghamdi, E. A., et al. (2024) · Proceedings of the CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642697 · Cited by 136 · see citing works
“System is meant to evoke a sense of another agent acting on the interface or data Tool System is presented as tools, where there is not a sense of interacting with another agent Hybrid System draws from both “agent” and “tool” metaphors”
Akpan, I. J., Kobara, Y. M., Owolabi, J., Akpan, A. A., & Offodile, O. F. (2024) · International Transactions in Operational Research · doi:10.1111/itor.13522 · Cited by 171 · see citing works
(2023) · Journal of Applied Learning & Teaching · doi:10.37074/jalt.2023.6.2.12 · Cited by 132 · see citing works
Alwaqdani, M. (2025) · Education and Information Technologies · doi:10.1007/s10639-024-12903-9 · Cited by 136 · see citing works
Will artificial intelligence drive the advancements in higher education? A tri-phased exploration
Kumar, S., Rao, P., Singhania, S., Verma, S., & Kheterpal, M. (2024) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2024.123258 · Cited by 131 · see citing works
Higher Education's Generative Artificial Intelligence Paradox: The Meaning of Chatbot Mania
Rudolph, J., Mohamed Ismail, F. M., & Popenici, S. (2024) · Journal of University Teaching and Learning Practice · doi:10.53761/54fs5e77 · Cited by 128 · see citing works
Gonsalves, C. (2025) · Assessment & Evaluation in Higher Education · doi:10.1080/02602938.2024.2415654 · Cited by 121 · see citing works
Chan, C. K. Y. (2025) · Education and Information Technologies · doi:10.1007/s10639-024-13151-7 · Cited by 128 · see citing works
Nedungadi, P., Tang, K., & Raghu Raman, R. (2024) · Sustainability · doi:10.3390/su16229779 · Cited by 155 · see citing works
Understanding University Students’ Acceptance of ChatGPT: Insights from the UTAUT2 Model
Grassini, S., Aasen, M. L., & Møgelvang, A. (2024) · Applied Artificial Intelligence · doi:10.1080/08839514.2024.2371168 · Cited by 122 · see citing works
Evangelista, E. D. L. (2025) · Contemporary Educational Technology · doi:10.30935/cedtech/15775 · Cited by 124 · see citing works
Salah, M., Halbusi, H. A., & Abdelfattah, F. (2023) · Computers in Human Behavior Artificial Humans · doi:10.1016/j.chbah.2023.100006 · Cited by 122 · see citing works
ChatGPT Promises and Challenges in Education: Computational and Ethical Perspectives
Adel, A., Ahsan, A., & Davison, C. (2024) · Education Sciences · doi:10.3390/educsci14080814 · Cited by 149 · see citing works
Aljuaid, H. (2024) · Arab World English Journal · doi:10.24093/awej/chatgpt.2 · Cited by 129 · see citing works
Simple techniques to bypass GenAI text detectors: implications for inclusive education
Perkins, M., Roe, J., Vu, B. H., Postma, D., Hickerson, D., McGaughran, J., & Khuat, H. Q. (2024) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-024-00487-w · Cited by 117 · see citing works
Self-citation(2023) · doi:10.1057/s41307-023-00323-2 · Cited by 115 · see citing works
Self-citationThe application of Chat Generative Pre-trained Transformer in nursing education
Liu, J., Liu, F., Fang, J., & Liu, S. (2023) · Nursing Outlook · doi:10.1016/j.outlook.2023.102064 · Cited by 103 · see citing works
The impact of Artificial Intelligence in academia: Views of Turkish academics on ChatGPT
Livberber, T., & Ayvaz, S. (2023) · Heliyon · doi:10.1016/j.heliyon.2023.e19688 · Cited by 103 · see citing works
Kizilcec, R. F., Huber, E., Papanastasiou, E. C., Cram, A., Makridis, C. A., Smolansky, A., Zeivots, S., & Raduescu, C. (2024) · Computers and Education Artificial Intelligence · doi:10.1016/j.caeai.2024.100269 · Cited by 150 · see citing works
Salifu, I., Arthur, F., Arkorful, V., Nortey, S. A., & Osei-Yaw, R. S. (2024) · Cogent Social Sciences · doi:10.1080/23311886.2023.2300177 · Cited by 119 · see citing works
Lund, B. D., & Naheem, K. T. (2023) · Learned Publishing · doi:10.1002/leap.1582 · Cited by 122 · see citing works
Academic publisher guidelines on AI usage: A ChatGPT supported thematic analysis
Perkins, M., & Roe, J. (2023) · F1000Research · doi:10.12688/f1000research.142411.2 · Cited by 111 · see citing works
Self-citationMustapha, K. B. (2024) · Advanced Engineering Informatics · doi:10.1016/j.aei.2024.103066 · Cited by 96 · see citing works
Mohammadkarimi, E. (2023) · Journal of Applied Learning and Teaching · doi:10.37074/jalt.2023.6.2.10 · Cited by 90 · see citing works
Roe, J., & Perkins, M. (2023) · Humanities and Social Sciences Communications · doi:10.1057/s41599-023-02282-w · Cited by 87 · see citing works
Self-citationThe AI Assessment Scale (AIAS) in action: A pilot implementation of GenAI supported assessment
Furze, L., Perkins, M., Roe, J., & MacVaugh, J. (2024) · Australasian Journal of Educational Technology · doi:10.14742/ajet.9434 · Cited by 92 · see citing works
Self-citationAteeq, A. A., Alzoraiki, M., Milhem, M., & Ateeq, R. A. (2024) · Frontiers in Education · doi:10.3389/feduc.2024.1470979 · Cited by 85 · see citing works
Watts, F. M., Dood, A. J., Shultz, G. V., & Rodriguez, J.-M. G. (2023) · Journal of Chemical Education · doi:10.1021/acs.jchemed.3c00664 · Cited by 81 · see citing works
Exploring student perspectives on generative artificial intelligence in higher education learning
Baidoo-Anu, D., Asamoah, D., Amoako, I., & Mahama, I. (2024) · Discover Education · doi:10.1007/s44217-024-00173-z · Cited by 136 · see citing works
Friend or foe? Exploring the implications of large language models on the science system
Fecher, B., Hebing, M., Laufer, M., Pohle, J., & Sofsky, F. (2025) · AI & SOCIETY · doi:10.1007/s00146-023-01791-1 · Cited by 78 · see citing works
“To date, scholars have primarily focused on the implications of LLMs on education (e.g., Perkins 2023; Fyfe 2023) with limited attention being paid to their impact on science and scientific practices (for exceptions, see Chubb et al. 2022; Ribeiro et al. 2023).”
InfluentialStudents' reflections on their experience with <scp>ChatGPT</scp>
Šedlbauer, J., Činčera, J., Slavík, M., & Hartlová, A. (2024) · Journal of Computer Assisted Learning · doi:10.1111/jcal.12967 · Cited by 71 · see citing works
ChatGPT’s Capabilities in Spotting and Analyzing Writing Errors Experienced by EFL Learners
Algaraady, J., & Mahyoob, M. (2023) · Arab World English Journal · doi:10.24093/awej/call9.1 · Cited by 89 · see citing works
The use of Generative AI in qualitative analysis: Inductive thematic analysis with ChatGPT
(2024) · Journal of Applied Learning & Teaching · doi:10.37074/jalt.2024.7.1.22 · Cited by 70 · see citing works
Self-citationSalih, S., Husain, O., Hamdan, M., Abdelsalam, S., Elshafie, H., & Motwakel, A. (2025) · Results in Engineering · doi:10.1016/j.rineng.2024.103837 · Cited by 74 · see citing works
Ganjavi, C., Eppler, M., O’Brien, D., Ramacciotti, L. S., Ghauri, M. S., Anderson, I., Choi, J., Dwyer, D., Stephens, C., Shi, V., et al. (2024) · PLOS Digital Health · doi:10.1371/journal.pdig.0000596 · Cited by 73 · see citing works
Nelson, A. S., Santamaría, P. V., Javens, J. S., & Ricaurte, M. (2025) · Education Sciences · doi:10.3390/educsci15050611 · Cited by 79 · see citing works
Oc, Y., Gonsalves, C., & Quamina, L. T. (2024) · Journal of Marketing Education · doi:10.1177/02734753241302459 · Cited by 90 · see citing works
Linking artificial intelligence facilitated academic misconduct to existing prevention frameworks
Birks, D., & Clare, J. (2023) · International Journal for Educational Integrity · doi:10.1007/s40979-023-00142-3 · Cited by 69 · see citing works
Kong, S. C., Lee, J. C., & Tsang, O. (2024) · Research and Practice in Technology Enhanced Learning · doi:10.58459/rptel.2024.19030 · Cited by 97 · see citing works
Bukar, U. A., Sayeed, M. S., Razak, A., Yogarayan, S., & Sneesl, R. (2024) · IEEE Access · doi:10.1109/access.2024.3425172 · Cited by 89 · see citing works
A Review on the Use of Large Language Models as Virtual Tutors
García-Méndez, S., de Arriba-Pérez, F., & Somoza-López, M. D. C. (2024) · Science & Education · doi:10.1007/s11191-024-00530-2 · Cited by 78 · see citing works
Maphoto, K. B., Sevnarayan, K., Mohale, N. E., Suliman, Z., Ntsopi, T. J., & Mokoena, D. (2024) · Open Praxis · doi:10.55982/openpraxis.16.2.649 · Cited by 115 · see citing works
Roe, J., Renandya, W. A., & Jacobs, G. M. (2023) · Journal of English and Applied Linguistics · doi:10.59588/2961-3094.1035 · Cited by 100 · see citing works
Cong-Lem, N., Tran, T. N., & Nguyen, T. T. (2024) · Teaching English With Technology · doi:10.56297/fsyb3031/mxnb7567 · Cited by 64 · see citing works
Zou, B., Wang, C., Yan, Y., Du, X., & Ji, Y. (2025) · International Journal of Applied Linguistics · doi:10.1111/ijal.12658 · Cited by 66 · see citing works
Emerging Research and Policy Themes on Academic Integrity in the Age of Chat GPT and Generative AI
Plata, S., De Guzman, M. A., & Quesada, A. (2023) · Asian Journal of University Education · doi:10.24191/ajue.v19i4.24697 · Cited by 65 · see citing works
Ursavaş, Ö. F., Yalçın, Y., İslamoğlu, H., Bakır-Yalçın, E., & Cukurova, M. (2025) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-025-00535-z · Cited by 69 · see citing works
A Survey of Text Watermarking in the Era of Large Language Models
Liu, A., Pan, L., Lu, Y., Li, J., Hu, X., Zhang, X., Wen, L., King, I., Xiong, H., & Yu, P. S. (2024) · ACM Computing Surveys · doi:10.1145/3691626 · Cited by 87 · see citing works
Gulati, A., Saini, H., Singh, S., & Kumar, V. (2024) · Marketing Education Review · doi:10.1080/10528008.2023.2300139 · Cited by 75 · see citing works
Chen, G., Fan, J., & Azam, M. (2024) · Journal of Librarianship and Information Science · doi:10.1177/09610006241269189 · Cited by 73 · see citing works
Lozano, A., & Blanco Fontao, C. (2023) · Education Sciences · doi:10.3390/educsci13070733 · Cited by 60 · see citing works
Faruk, L. I. D., Rohan, R., Ninrutsirikun, U., & Pal, D. (2023) · doi:10.1145/3628454.3629552 · Cited by 64 · see citing works
Pani, B., Crawford, J., & Allen, K.-A. (2024) · Applications of Generative AI · doi:10.1007/978-3-031-46238-2_13 · Cited by 57 · see citing works
- Authors
- Perkins, M.
- Affiliations
- British University Vietnam (VN)
- Corresponding author
- Perkins, Mike
- ORCID
- Perkins, Mike
- Venue
- Journal of University Teaching and Learning Practice
- Publisher
- Open Access Publishing Association
- Published
- 2023-01-01
- Volume
- 20
- Issue
- 2
- ISSN
- 1449-9789 (ISSN-L)
- OpenAlex
- W4321605350
- Cited by
- 839
- References
- 84
- Citations by year
- 2026: 270 · 2025: 210 · 2024: 251 · 2023: 108
- FWCI
- 209.33
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Locations
- Journal of University Teaching and Learning Practice (publishedVersion)
- Research Online (University of Wollongong) (submittedVersion)
- Topics
- Social Sciences › Safety Research › Academic integrity and plagiarism
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- academic integrity, large language models, ChatGPT, generative AI in education, academic misconduct, plagiarism, automated writing evaluation, EFL learners, digital writing, composition teaching, human-AI co-creation, academic integrity policy, higher education, post-pandemic education
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.
Claims read by deepseek-v4.1-flash under definition version 3 (extract-v3) on 2026-10-11.
Classified by jev-1.13-20260917 (gold-guideline-v0.2) on 2026-10-11.
Cited sources are checked by gpt-6-sol.
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
