Not quite eye to A.I.: student and teacher perspectives on the use of generative artificial intelligence in the writing process

Abstract

Generative artificial intelligence (GenAI) can be used to author academic texts at a similar level to what humans are capable of, causing concern about its misuse in education. Addressing the role of GenAI in teaching and learning has become an urgent task. This study reports the results of a survey comparing educators’ (n = 68) and university students’ (n = 158) perceptions on the appropriate use of GenAI in the writing process. The survey included representations of user prompts and output from ChatGPT, a GenAI chatbot, for each of six tasks of the writing process (brainstorming, outlining, writing, revising, feedback, and evaluating). Survey respondents were asked to differentiate between various uses of GenAI for these tasks, which were divided between student and teacher use. Results indicate minor disagreement between students and teachers on acceptable use of GenAI tools in the writing process, as well as classroom and institutional-level lack of preparedness for GenAI. These results imply the need for explicit guidelines and teacher professional development on the use of GenAI in educational contexts. This study can contribute to evidence-based guidelines on the integration of GenAI in teaching and learning.

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RESEARCH ARTICLE
Barrettand Pack Int J Educ Technol High Educ (2023) 20:59
https://doi.org/10.1186/s41239-023-00427-0
International Journal of Educational
Technology in Higher Education
Not quite eye toA.I.: student andteacher
perspectives ontheuse ofgenerative articial
intelligence inthewriting process
Alex Barrett1* and Austin Pack2
Abstract
Generative artificial intelligence (GenAI) can be used to author academic texts at a simi-
lar level to what humans are capable of, causing concern about its misuse in education.
Addressing the role of GenAI in teaching and learning has become an urgent task. This
study reports the results of a survey comparing educators’ (n = 68) and university stu-
dents’ (n = 158) perceptions on the appropriate use of GenAI in the writing process. The
survey included representations of user prompts and output from ChatGPT, a GenAI
chatbot, for each of six tasks of the writing process (brainstorming, outlining, writing,
revising, feedback, and evaluating). Survey respondents were asked to differentiate
between various uses of GenAI for these tasks, which were divided between student
and teacher use. Results indicate minor disagreement between students and teachers
on acceptable use of GenAI tools in the writing process, as well as classroom and insti-
tutional-level lack of preparedness for GenAI. These results imply the need for explicit
guidelines and teacher professional development on the use of GenAI in educational
contexts. This study can contribute to evidence-based guidelines on the integration
of GenAI in teaching and learning.
Keywords: Artificial intelligence, Large language model, GPT, Writing education,
Academic integrity
Introduction
Public interest in artificial intelligence (AI) has grown substantially as a result of
recent public access to large language models (LLMs; e.g., OpenAI’s GPT-3 and 4,
Google’s PaLM 1 and 2), and chatbots (e.g., OpenAI’s ChatGPT, Google’s Bard,
Microsoft’s Bing) that allow users to interface with LLMs. ese Generative AI
(GenAI) tools afford individuals with the ability to instantly generate writing on any
topic by inputting a simple prompt. e public discourse surrounding GenAI has
been mostly positive, but in the education sector there is serious concern about aca-
demic integrity and plagiarism (Dehouche, 2021; Lampropoulos etal., 2023; Sullivan
etal., 2023; Yeo, 2023). Some schools have responded by banning the technology out-
right (Yang, 2023), a move likened by some to the banning of the pocket calculator
when it was perceived as a threat to math education (Urlaub & Dessein, 2022). What
*Correspondence:
abarrett3@fsu.edu
1 College of Education, Florida
State University, Stone Building,
114 West Call Street, Tallahassee,
FL 32306, USA
2 Faculty of Education and Social
Work, Brigham Young University-
Hawaii, 55-220 Kulanui Street,
Laie, HI 96762, USA

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Barrettand Pack Int J Educ Technol High Educ (2023) 20:59
is clear is that this new technology possesses disruptive potential and that institutions
which have relied heavily on student writing for education and assessment will need
to respond accordingly.
Although a few schools have banned ChatGPT and similar tools, many have not,
displaying confidence that their institution’s academic integrity policy is robust
enough to accommodate the new technology. However, current definitions of pla-
giarism have been described as medieval (Dehouche, 2021; Sadeghi, 2019), typi-
cally including language such as kidnapping, stealing or misappropriating the work
of others (Sutherland-Smith, 2005), which now leads us to question whether a chat-
bot counts as one of these others. Generative AI is trained on a selection of diverse
natural language data from across the Internet which allows it to string together
unique combinations of words and phrases, similar to how humans learn to produce
an unlimited amount of novel spoken or written text from the limited language they
absorb from their environment, a tenet of generative grammar (Chomsky, 1991). e
result is that there is no identifiable other whose work is being stolen by a chatbot.
To complicate matters, the language of OpenAI’s Terms of Use state that it assigns
users “all its right, title and interest in and to Output” from ChatGPT, including for
purposes of publication (OpenAI, 2023). Any practiced educator would likely agree
that submitting an essay written by ChatGPT without disclosure violates academic
integrity, but students may not readily see a problem with it.
Although GenAI has multiple applications, its use as an authoring tool in programs
like ChatGPT allow for easy misuse. Students who have purposefully violated aca-
demic integrity in the past through the use of contract cheating or paper mills will
likely not hesitate to use ChatGPT or other GenAI tools to do so now, but other stu-
dents will need guidance on how to avoid inadvertently cheating. Student perceptions
of academic dishonesty have historically been unclear or incomprehensive, and rarely
align with teacher expectations (Tatum, 2022), GenAI will only serve to complicate
this (Farrokhnia etal., 2023).
Some advocate working towards a coexistence with AI in education by establishing
common goals and guided exploration of the limitations of the technology (Godwin-
Jones, 2022; Tseng & Warschauer, 2023). Yeo (2023) has specifically recommended
the exploration of student perceptions about the ethics of using GenAI tools, and
Pack and Maloney (2023a) suggested teacher and researcher use should also be
investigated.
To date no consensus has arisen regarding what constitutes appropriate use of
GenAI in higher education. erefore, with the goal of identifying some common
expectations, the purpose of this study is to explore student and teacher perspectives
of using GenAI for various tasks in the writing process, including brainstorming, out-
lining, writing, and revising done by students, and evaluation and feedback done by
teachers. e research questions guiding the study are:
1. What are undergraduate students’ and teachers’ perspectives on using GenAI in the
writing process (brainstorming, outlining, writing, revising, evaluation, and feed-
back)?

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Barrettand Pack Int J Educ Technol High Educ (2023) 20:59
2. How do student and teacher perspectives on the use of GenAI in the writing process
compare?
Related literature
Writing instruction and assessment have made use of AI for some time in the form of
programs like Grammarly or spell checker that identify grammatical and lexical errors
in writing (Godwin-Jones, 2022). Yet, with recent advances in machine learning and
natural language processing, AI-integrated language tools now pose a considerable
challenge for educational systems that have relied heavily on writing to develop and
assess the cognitive and linguistic competencies of learners. Students who do not use
English as a first language can now use machine translation programs to accurately
render their native language writing into a target language (Godwin-Jones, 2022);
students can use programs like Quillbot or Wordtune to paraphrase, summarize, or
adjust the tone of a paragraph (Yeo, 2023); and they can use ChatGPT to instantly
generate entire essays. Authoring tools such as ChatGPT are prevalent and affordable
(or even free) to anyone with an internet connection. ChatGPT garnered over 100
million users within three months of its launch (Hu, 2023), many of which are likely
students and teachers. How GenAI, like ChatGPT, is being used in academic settings
and how much reform will be required of educational systems as a result of this use is
yet to be determined.
Looking at how individuals and institutions have used AI-integrated writing and lan-
guage tools in the past might inform predictions of how GenAI tools like ChatGPT will
be used in the future. For instance, automated essay scoring with AI has been the sta-
tus quo for many large testing services, such as Pearson’s Intelligent Essay Assessor and
ETS’s e-Rater (Gardner etal., 2020), which administer university placement exams, and
other major exams such as the GRE and TOEFL. Receiving millions of summative writ-
ing samples each year, AI can greatly reduce the workload of manually scoring each essay
(Hockley, 2018). However, the programs are typically limited to assessment of grammar,
usage, mechanics, and style and are not able to detect more complex features like the
presence of a thesis statement or overall coherence (Gardner etal., 2020). GenAI might
be used to strengthen these tools to assess more complex discourse elements.
For formative writing, on the other hand, tools like Grammarly or Criterion are com-
monly used to provide corrective feedback during the revising and editing phases of
the writing process. is includes basic grammar, spelling, and punctuation, or more
advanced analytics such as word counts and readability (Fitria, 2021), all of which can be
procured instantly and at any time. Multiple studies have researched automated writing
evaluation (AWE) tools such as these with mixed results as to their efficacy and reli-
ability (Huawei & Aryadoust, 2023; Wang & Han, 2022; Zhang, 2020), however, students
tend to appreciate the feedback and respond positively to it (O’Neill & Russell, 2019).
ChatGPT may be used as a cheaper and more robust AWE tool by students.
A recent experimental study by Fan (2023) looked at the effects and perceptions of
university EFL students’ use of Grammarly for corrective feedback on their writing.
Although no differences were noticed in terms of writing improvement, most students
in the Grammarly group found the feedback to be understandable and useful. For those
that did not, Fan (2023) noted that students’ low proficiency may have prevented them

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Barrettand Pack Int J Educ Technol High Educ (2023) 20:59
from effectively understanding Grammarly’s feedback. One potential use of ChatGPT
by EFL students is to revise commercial AWE feedback to be more comprehensible and
accessible for them.
Although GenAI tools can be useful as a virtual tutor that offers individualized
writing feedback, the threat of misuse sours these benefits. AWE tools like Gram-
marly and Criterion tend to be useful for evaluation and revision stages of the writing
process, but GenAI tools like ChatGPT can further be used in the brainstorming, out-
lining, and writing stages of the writing process. e concern of authorship and pla-
giarism arises when students are using GenAI to give them ideas or to draft writing
assignments (Ingley & Pack, 2023). is is the concern voiced by many in academia
since the release of ChatGPT in November of 2022 (Sullivan etal., 2023).
Writing is a fundamental skill that is necessary for learner academic and profes-
sional development. In a joint publication, the Council of Writing Program Adminis-
trators, the National Council of Teachers of English, and the National Writing Project
described the benefits of writing practices to develop rhetorical knowledge and criti-
cal thinking, which are in turn supported by habits of mind such as curiosity, creativ-
ity, persistence, and responsibility (CWPA, NCTE, & NWP, 2011). It has been shown
that weak writing ability can result in less learning in all school subjects and nega-
tively impact professional success (Graham, 2019; Graham etal., 2020). erefore, it
stands to reason that students outsourcing writing to AI will likely incur a negative
impact on their cognitive ability and prospects for future success.
Concerns have also been voiced in how AI may be used by educators (Pack &
Maloney, 2023a, b;Carlson etal., 2023; Lo, 2023). Educators have used GenAI to cre-
ate course material and assessment tasks, adapt materials to be more suitable for spe-
cific students, and generate lecture notes (Bonner etal., 2023; Lo, 2023). However,
one of the most frequently suggested uses of GenAI for teachers is to grade and pro-
vide feedback on student writing (Chiu etal., 2023; Kaplan & Haenlein, 2018; Weigle,
2013; Yeo, 2023). Accompanying some of these suggestions are characterizations of
grading writing as burdensome and tedious, and GenAI is seen as a way of reducing
teacher workload.
Teachers who seek to escape the ‘tedious and burdensome’ process of essay grading
by using GenAI may inadvertently be signaling that it is ok for students to use the tool
to “take the pain out of…the writing process” (Yeo, 2023, p. 2). In a position paper
on machine essay scoring, the National Council of Teachers of English (2013) high-
lighted the social aspect of writing and that machine scoring sends students a mes-
sage that writing is not worth the time because reading it is not worth the time. It will
be imperative to incorporate GenAI in the writing process in a way that alleviates the
“pain” and “burden” of the process without diminishing the social nature of writing.
Teachers might also be misled by the seeming impartiality of AI tools when the
truth is these instruments are susceptible to influences unwittingly included in them
by the developers who created them, a phenomenon known as algorithmic bias (Jack-
son, 2021). In educational contexts, algorithmic bias in GenAI can manifest when
LLMs are trained on a convenience sample of language from the Internet which tends
to be majority English language and majority western (Graham etal., 2015), resulting
in underrepresentation of different languages, dialects, philosophies, ethnicities, and

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Barrettand Pack Int J Educ Technol High Educ (2023) 20:59
multiple other demographic divisors (Baker & Hawn, 2021). A study by Bridgeman
etal. (2012), for example, showed that the e-Rater essay scoring AI produced inaccu-
rate scores for populations along ethnic lines.
e use of GenAI for education involves other risks such as functional opacity, data
privacy, and reliability (Yu & Guo, 2023) which need to be accounted for, but the useful-
ness of the technology for assisting in the writing process will likely lead to its adoption
by teachers and students regardless of these limitations. To what degree do students and
teachers feel it is acceptable to use GenAI in writing? Addressing this research question
will provide a foundation from which common goals can be established that might assist
with the successful adoption of GenAI in education.
Methodology
is study utilized a cross-sectional questionnaire design to make within- and between-
group comparisons of students’ and teachers’ perceptions of the use of GenAI in learn-
ing and teaching.
Participants
A total of 226 participants (158 students and 68 teachers) completed the questionnaire.
Students from across a variety of disciplines were recruited via non-probability volun-
tary response from a public research university in the United States with an incentive
of course credit. Teachers were recruited using a purposeful sampling and snowballing
method whereby the questionnaire was sent out to contacts of the authors at multiple
institutions worldwide for distribution. e teacher questionnaire also provided a self-
reflexive URL link for those who completed it with a request to forward the link to rel-
evant individuals.
Second language teachers that teach a language different than the students’ native lan-
guage were the primary target of the teacher survey due to their experience with ground-
up writing instruction and the corresponding need to address plagiarism and academic
dishonesty regularly. Insights from English as a Second Language (ESL) and English as
a Foreign Language (EFL) teachers may prove useful as they are exposed to non-west-
ern writing traditions which hold diverse views on intellectual property and originality
(Pennycook, 1996; Sutherland-Smith, 2005). e student population was chosen for its
homogeneity and typicality of public university undergraduates. A minority of surveys
were collected from graduate students (n = 5) and non language educators (n = 3).
Informed consent from all participants was acquired and all ethical procedures were
adhered to according to the standards of the university institutional review board. Par-
ticipant demographics can be found in Tables1 and 2.
Instrument
Divisions ofuse ofAI inthewriting process
A quantitative questionnaire instrument was developed to measure participants’
perspectives on ways of including GenAI in the writing process. According to Seow
(2002), process writing includes four basic stages: planning (including brainstorming
and outlining), writing, and revising or editing. Planning includes prewriting activi-
ties that assist students in generating and organizing ideas. During the writing stage

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Barrettand Pack Int J Educ Technol High Educ (2023) 20:59
students focus on communicating their ideas to a specific audience in an initial draft.
Based on feedback from the initial draft, revising occurs as students reexamine their
writing and rewrite areas where their intent, style, tone, mechanics, or organiza-
tion was identified as needing improvement. In addition to these steps that students
Table 1 Student demographics (n = 158)
Category Value Frequency % (rounded)
Sex Male 57 36
Female 99 63
Non-binary/transgender 2 1
Age 18–24 151 96
25–34 7 4
Degree level Undergraduate 153 97
Graduate 5 3
Highest degree obtained High school 89 56
Associate’s 53 34
Bachelor’s 16 10
Language English native 151 96
English non-native 7 4
Table 2 Teacher demographics (n = 68)
Category Value Frequency % (rounded)
Sex Male 25 37
Female 43 63
Age 18–24 1 2
25–34 23 34
35–44 22 32
45–54 13 19
55–64 5 7
65 + 4 6
Education level Associate’s (2-year degree) or lower 2 3
Bachelor’s 3 4
Master’s 46 68
Doctoral 17 25
Years of experience Less than 1 2 3
1 to 3 5 7
4 to 6 13 19
7 or more 48 71
Country currently teaching in United States 38 56
China 14 21
Other 15 23
Level currently teaching Adult/university 63 93
K-12 2 3
Other 3 4
Subject English as a new language (ENL) 55 81
Modern languages 10 15
Other (i.e., Health, Psychology, Law) 3 4

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Barrettand Pack Int J Educ Technol High Educ (2023) 20:59
complete, Seow highlights the importance of evaluation and feedback from teachers
throughout the writing process. e questionnaire used in this study explored partici-
pants’ perspectives on potential uses of GenAI for brainstorming, outlining, writing,
revising, evaluating, and providing feedback.
For each of the writing process steps participants were asked to read an example GenAI
prompt and response produced by OpenAI’s ChatGPT (GPT-3.5 turbo) obtained in Feb-
ruary of 2023. ey were then presented with four divisions of use (or misuse) and asked
to rate the appropriateness of each division on a five-point Likert scale (strongly disagree
to strongly agree). ese divisions and their explanations can be found in Table3. e
internal consistency of this instrument was measured with the alpha coefficient for each
division of use of AI in the writing process. All alpha coefficients were above 0.7 which is
considered satisfactory (Bland & Altman, 1997). ese divisions of use were conceptual-
ized by considering the common suggested uses of GenAI in recent literature (e.g., Kasneci
etal., 2023), which include things like cognitive offloading of trivial tasks and idea genera-
tion, and also incorporating theory of plagiarism degrees of severity (Evering & Moorman,
2012; Yeo, 2007; Yeo & Chien, 2007), which differentiates plagiaristic behaviors.
Each division of use represented in the instrument was accompanied by an example
of GenAI output that could be generated with simplistic prompting (using ChatGPT
3.5-turbo, February 2023 version). e simplistic approach taken when creating the
examples was adopted to portray the capabilities of the technology from a layper-
son perspective, who may approach prompting naively. No sophisticated or iterative
prompt engineering was used when generating the output for these examples but each
prompt was submitted to ChatGPT separately so that the chatbot would not have a
direct memory resource of previously submitted prompts. All complete prompts and
output can be found in the appendix.
We divided the examples of GenAI use in the writing process between students and
teachers so that brainstorming, outlining, writing, and revising were student actions
and evaluating and providing feedback were teaching actions. In doing this we do not
intend to suggest students cannot or should not use GenAI for evaluation or feedback
purposes, but to demonstrate to participants that GenAI use in the writing process is
Table 3 Divisions of use of AI for the writing process as worded in the questionnaire, with
explanations
A B C D
“This use of AI is appropri-
ate if the person already
knows how to perform
the task.”
“This use of AI is appropri-
ate if the person uses the
output to generate ideas
about the task, but com-
pletes the task without
further assistance from AI.”
“This use of AI is appropri-
ate if the person submits
the output for the task
assignment and discloses
the use of AI.”
“This use of AI is appropri-
ate if the person submits
the output for the task
assignment but does not
disclose the use of AI.”
Explanation: If a student or
teacher is tasked with an
assignment that they can
already perform with a
high degree of compe-
tency, then it is acceptable
to offload that task to AI
Explanation: It is accept-
able for students or teach-
ers to use AI to generate
models or ideas for task
assignments, considering
the submitted assignment
is composed of their own
language and not the
language of the AI
Explanation: It is accept-
able for students or
teachers to submit task
assignments that contain,
in part or in whole,
language produced by AI,
considering that any AI
language is clearly identi-
fied as such
Explanation: It is acceptable
for students or teachers to
submit task assignments
that contain, in part or in
whole, language produced
by AI without disclosing
the use of AI

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Barrettand Pack Int J Educ Technol High Educ (2023) 20:59
not restricted to only students, and to elicit broader perceptions on the use of GenAI
in education by multiple stakeholders.
Looking at the student-oriented prompts, for brainstorming we prompted ChatGPT
to come up with ideas for an essay on the topic of urban challenges and global warm-
ing and the output provided 8 relevant ideas. e outlining prompt requested an outline
for a 5-paragraph essay on the same topic and returned a bulleted outline including a
thesis statement, paragraph topics, and supporting points. For the writing prompt we
requested a fully written 5-paragraph essay on the same topic and it returned a coherent
and cohesive essay. For revision we provided ChatGPT with a four-sentence paragraph
that was written in an informal tone and asked for it to be revised to be more academic;
the resulting output was more formal and academic.
As for the teacher-oriented prompts, we provided ChatGPT with a short paragraph
that was replete with grammatical and lexical errors and prompted it to provide sugges-
tions to the student on how to improve their writing. e output provided five somewhat
generic suggestions for improvement along with examples, such as using transitional
phrases, clearer language, and proofreading for errors. Last, we provided an error-free
paragraph and prompted ChatGPT to evaluate the quality of ideas expressed therein.
ChatGPT returned several sentences evaluating the argument made in the paragraph,
commenting on development and logic.
Survey
Following the questionnaire section measuring perceptions of acceptable use, a short
survey was included to better understand the sampled populations and to detect poten-
tial covariates or subgroups which might afford further analysis. ese included eight
survey items covering opinions about AI and technology in general measured on a five-
point Likert scale (strongly disagree to strongly agree), and two to three (depending on
group) yes/no response items (all items are listed in Table7 in Sect."Survey results").
To investigate the validity of the eight survey items, an exploratory factor analysis was
conducted with all the completed surveys (n = 226) using principal axis factoring and
a promax rotation method. e KMO test value was 0.674, showing an adequate pro-
portion of variance in the survey which could indicate underlying factors. Also, Bart-
lett’s Test of Sphericity was significant at the 0.001 alpha level. e analysis confirmed
a four-factor solution (Table4) which cumulatively accounted for 77.6% of the variance.
Table 4 Exploratory factor analysis loadings
1 2 3 4
AI will be a useful tool for students 0.927
AI will be a useful tool for teachers 0.766
I think AI will have a positive impact on education 0.638
I am concerned with how students will use AI 0.874
I am concerned with how teachers will use AI 0.545
I enjoy using new technologies to help me learn 0.711
I prefer to stick with tools and methods of learning I am
familiar with
− 0.497
I am familiar with how AI may be used for education 0.574
Factor alpha coefficient (α) 0.82 0.63 0.45 –

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Barrettand Pack Int J Educ Technol High Educ (2023) 20:59
However, factors 2 and 3 showed poor internal consistency, with alpha coefficients below
the 0.7 benchmark. e low reliability of factors 2 and 3 is likely due to only having two
items per factor and also disparate attitudes concerning teachers and students from the
surveyed groups. Factor 1 can be described as the perceived utility of AI in education;
factor 2 as perceived concern about AI in education; factor 3 as perceptions on technol-
ogy change and innovation; and factor 4 as familiarity with AI.
Data collection andanalysis
e questionnaire was created and distributed using QualtricsⓇ. Questionnaire
responses were collected during the spring semester of 2023. 15 incomplete question-
naires were discarded following the collection period.
Response frequencies were calculated for both groups on the levels of acceptable use
of GenAI across each task and for the yes/no survey items. Mean responses and stand-
ard deviations were calculated for levels of acceptable use as well as the Likert-style sur-
vey items.
Although our teacher and student samples were disparate in size and population,
exploratory comparisons were made to infer differences in perceptions. To do this,
the Mann–Whitney U test was used to compare means for the teachers’ and students’
responses on the level of appropriateness of using GenAI, and also for mean responses
to the survey items. A key assumption of the Mann–Whitney U test is independent
observations from compared groups. is assumption was satisfied as participants com-
pleted the questionnaire individually and cross-group contamination was unlikely due to
separate questionnaire hyperlinks.
Lastly, a principal component analysis was conducted to better understand teacher
and student perceptions of GenAI use in the writing process across the multiple writing
process steps and divisions of use.
Results
In general, both teachers and students held similar perceptions on what is appropri-
ate use of GenAI in the writing process. at is, both groups predominantly agreed or
disagreed along each division of use for each of the writing process tasks presented to
them. Despite this general conformity, there were some significant differences in mean
responses for some of the divisions of use as measured by the Mann–Whitney U test.
Details of these response frequencies are provided in Table5, and student–teacher com-
parisons are presented in subsequent subsections.
Brainstorming
Figure1 shows response frequencies for teachers and students for the brainstorming
task of the writing process. Students and teachers both generally felt that using GenAI
to brainstorm ideas was acceptable if the student was already a competent brainstormer
or only used the output as a model. Submitting AI-brainstormed ideas in class was
seen as acceptable by half of teachers and students who took the survey, with another
10 to 16% uncertain and the remaining against. No significant differences were found
between groups on these uses of GenAI. However, there was a larger difference between
teachers and students when asked if it was ok to use GenAI to brainstorm ideas without

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Barrettand Pack Int J Educ Technol High Educ (2023) 20:59
disclosing the use of GenAI. Although teachers and students were predominantly in
alignment in their disagreement that this use was acceptable, fewer students disagreed,
and about 11% were uncertain. A Mann–Whitney U test comparing means between
groups found a p-value of 0.032 (U = 4491, r = −0.143) for division D on the brain-
storming task.
Outlining
e outlining task resulted in more differences between student and teacher perceptions
(Fig.2). Means for division A of GenAI use (acceptable if the student is already a com-
petent outliner) were not significantly different between groups, but for divisions B, C,
and D significant differences were detected with students being more accepting of these
uses. For using the GenAI output as a model, students were more accepting (U = 3949.5,
Table 5 Mean and standard deviation for student (n = 158) and teacher (n = 68) responses to the
use of GenAI in the writing process across four divisions of use (A, B, C, D)
Step in writing process Group A B C D
Brainstorming Student 3.55 (1.17) 3.82 (1.15) 3.13 (1.31) 1.99 (1.22)
Teacher 3.53 (1.26) 3.78 (1.18) 3.21 (1.38) 1.65 (1.38)
Outlining Student 3.27 (1.29) 3.42 (1.28) 2.82 (1.40) 1.84 (1.19)
Teacher 2.96 (1.42) 2.76 (1.37) 2.41 (1.28) 1.34 (0.86)
Writing Student 2.44 (1.47) 2.91 (1.42) 2.25 (1.43) 1.62 (1.12)
Teacher 2.13 (1.48) 3.15 (1.30) 1.68 (1.04) 1.22 (0.79)
Revision Student 2.80 (1.41) 3.54 (1.34) 2.62 (1.47) 1.92 (1.24)
Teacher 2.53 (1.46) 3.67 (1.22) 2.10 (1.19) 1.44 (1.07)
Feedback Student 2.98 (1.38) 3.23 (1.37) 2.80 (1.39) 2.01 (1.23)
Teacher 3.23 (1.27) 3.71 (1.11) 3.03 (1.28) 1.82 (1.28)
Evaluation Student 2.73 (1.38) 3.11 (1.43) 2.63 (1.42) 1.89 (1.31)
Teacher 3.07 (1.29) 3.54 (1.14) 2.85 (1.39) 1.17 (1.17)
αStudent 0.87 0.78 0.85 0.91
Teacher 0.84 0.79 0.83 0.90
Fig. 1 Response frequencies for brainstorming task

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Barrettand Pack Int J Educ Technol High Educ (2023) 20:59
r = − 0.217, p = 0.001). For submitting GenAI output with disclosure (U = 4488.5,
r = −0.134, p < 0.05) and without disclosure (U = 3973.5, r = −2.42, p < 0.001) students
were also more accepting than teachers.
Writing
Teachers and students had comparable perceptions on acceptable use of GenAI for writ-
ing an essay (Fig.3) if the student was already competent in writing an essay (Division
A pair) or if a student wanted to use a GenAI draft of an essay to model their own writ-
ing (Division B pair). Interestingly, both students and teachers predominantly viewed the
use of GenAI for writing essays, even when the student is a competent writer, as inap-
propriate, but both students and teachers mostly agreed it was ok for students to use a
GenAI generated essay as a model. Significant disagreement was found between teachers
and students regarding submitting a GenAI-written essay with disclosure (U = 4311.5,
Fig. 2 Response frequencies for outlining task
Fig. 3 Response frequencies for writing task

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Barrettand Pack Int J Educ Technol High Educ (2023) 20:59
r = −0.169, p = 0.011) and without disclosure (U = 4245.5, r = −0.223, p < 0.001), w ith
teachers more heavily disagreeing with this behavior.
Revision
Similar to writing, having GenAI revise an essay (Fig. 4) showed mixed perceptions
between teachers and students with no differences detected for divisions A and B, but
significant differences found in divisions C and D. Students were more accepting of
using GenAI to revise their writing both with disclosure (U = 4393, r = −0.15, p < 0.05)
and without disclosure (U = 4030, r = −0.23, p < 0.001). However, like the writing task,
the majority of both groups saw this as inappropriate use of AI.
Feedback
Feedback (Fig.5) was framed as a teacher use of GenAI as an AWE tool. Non-signifi-
cant differences in mean response frequencies were found along divisions A, C, and D,
however, teachers agreedsignificantly more than students that division B was acceptable
(using GenAI generated feedback as a model) (U = 6397, r = −0.157, p < 0.05), although
both groups were generally accepting of this behavior. Heavy disagreement was reported
by both groups in using GenAI for providing writing feedback without disclosing the use
of GenAI.
Evaluating
Using GenAI for evaluation of student writing was also framed as a teacher task (Fig.6).
Similar to the feedback task, significant differences on perceptions of acceptable use
were only detected in division B (using the AI-generated evaluation as a model), again
with teachers being more accepting of this use (U = 6247, r = −0.133, p < 0.05). Again,
both teachers and students felt it was inappropriate to use GenAI for this purpose with-
out disclosing the use of GenAI.
Fig. 4 Response frequencies for revision task

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Barrettand Pack Int J Educ Technol High Educ (2023) 20:59
Fig. 5 Response frequencies for feedback task

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Barrettand Pack Int J Educ Technol High Educ (2023) 20:59
Principal component analysis
In order to better understand how teachers and students perceive acceptable use of
GenAI in the writing process a principal component analysis was conducted (Table6).
Dimension reduction was achieved with a varimax rotation specifying 3 factors,
identified from components with an eigenvalue of 2 or greater on a scree plot, which
Fig. 6 Response frequencies for evaluation task
Table 6 Principal component analysis
123
Writing D 0.846
Outlining D 0.868
Revision D 0.796
Brainstorming D 0.749
Writing C 0.747
Evaluate D 0.695
Feedback D 0.633
Revision C 0.622
Outline C 0.502
Outlining A 0.747
Outlining B 0.733
Brainstorming A 0.677
Brainstorming B 0.666
Revision B 0.652
Revision A 0.640
Writing B 0.550
Writing A 0.545
Brainstorming C 0.322
Evaluate B 0.766
Evaluate C 0.762
Feedback C 0.752
Evaluate A 0.747
Feedback B 0.742
Feedback A 0.725

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Barrettand Pack Int J Educ Technol High Educ (2023) 20:59
accounted for 60% of the variance. e KMI measure of sampling adequacy value was
0.862 and Bartlett’s Test of Sphericity was significant (p < 0.001). For variables with load-
ings in more than one factor, the smaller loading was suppressed.
Factor 1 included all of division D as well as writing, outlining, and revising for divi-
sion C. ese use examples all had the lowest mean agreement of acceptability (refer
to Table5) so we can label this factor as highly unacceptable use. Factor 2, on the other
hand, primarily contained divisions of use A and B for outlining, revision, brainstorm-
ing, and writing. ese use examples had relatively high means and were seen as gen-
erally permissible uses of GenAI, despite writing A having a majority disagreement
response frequency. Factor 3 contained evaluation and feedback for all A, B and C divi-
sions of use. e ratings for these use examples were mostly supportive, especially along
the A and B divisions of use.
is analysis supports the validity of the instrument. Factor 1 contains the divisions
of use that involve GenAI doing all the writing of a writing assignment, which was rated
as the most unacceptable use of GenAI. Factor 2 contained the most acceptable uses of
GenAI across four student-oriented steps of the writing process, which included utiliz-
ing GenAI for tasks that the user is already proficient in, and to generate ideas or model
answers. e factor 3 use examples were categorized as being teacher-oriented, and their
inclusion in one factor here demonstrates that participants conceptualized acceptable
teacher use of GenAI differently.
Survey results
In addition to measuring perceptions on the use of GenAI in the writing process, we also
included several survey items to measure other aspects of AI use in educational contexts
(Table7).
We asked questions to better understand perceptions about the utility of AI in edu-
cation (items 1 through 3). ere was a tendency to agree that AI would be useful to
students and teachers in education, although some trepidation can be insinuated from
relatively less agreement (and neutrality for students) to item 3, about AI having a posi-
tive impact on education. Some disagreement is evident between teachers and students
for item 2, about the utility of AI for teachers, with a significantly higher percentage of
teachers agreeing on AI’s utility compared to students (U = 7098, p < 0.001).
Concern for student use of AI (items 4 and 5) was fairly high for both teachers and
students, with slightly less concern for teacher use. Students showed significantly more
concern regarding teacher use of AI than did teachers (U = 4288.5, p < 0.05).
We asked two questions to get a sense of participants’ general feelings toward new
technology and innovation (items 6 and 7). Teachers reported significantly more open-
ness to the use of new technologies and innovative tools and methods in their teaching
than did students for their learning (U = 6441.5, p < 0.05; U = 3577.5, p < 0.001).
Item 8 inquired about participant familiarity with AI. Mean familiarity scores were not
high, but students reported slightly more familiarity with AI than did teachers, however
the difference was non-significant.
We asked three yes/no questions (items 9 through 11; Table8) about AI policy and
preparedness. e student group (who all attend the same university) reported mixed

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Barrettand Pack Int J Educ Technol High Educ (2023) 20:59
Table 7 Survey item total response percentages (rounded) and mean comparisons between students (n = 158) and teachers (n = 68)
S student, T teacher, SD strongly disagree, D disagree, N neither agree nor disagree, A agree, SA strongly agree
*Bracketed text was displayed to the student group only
#Item S/T SD D N A SA M (SD) U p
1 AI will be a useful tool for students S 10 11 19 43 17 3.47 (1.18) 5914.5 0.201
T 3 12 13 56 16 3.71 (0.98)
2 AI will be a useful tool for teachers S 11 11 23 41 13 3.32 (1.18) 7098.0 < 0.001
T 0 6 10 63 21 3.99 (0.74)
3 I think AI will have a positive impact on education S 13 21 28 30 7 2.96 (1.15) 6084.5 0.101
T 4 16 37 37 6 3.24 (0.95)
4 I am concerned with how students will use AI S 5 4 8 30 53 4.23 (1.08) 5522.0 0.713
T 3 3 7 32 54 4.32 (0.95)
5 I am concerned with how teachers will use AI S 4 10 17 30 39 3.89 (1.16) 4288.5 0.012
T 4 16 24 35 21 3.51 (1.13)
6 I enjoy using new technologies for teaching [to help me learn]* S 2 10 21 45 23 3.76 (0.98) 6441.5 0.012
T 2 3 21 34 41 4.10 (0.93)
7 I prefer to stick with tools and methods [of learning]* I am familiar with S 3 17 23 36 21 3.58 (1.08) 3577.5 < 0.001
T 10 27 29 31 3 2.90 (1.05)
8 I am familiar with how AI may be used for education S 9 18 13 47 13 3.36 (1.18) 4728.5 0.133
T 6 31 18 35 10 3.13 (1.15)

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Barrettand Pack Int J Educ Technol High Educ (2023) 20:59
answers when asked if their university had a policy on AI use. Teachers (who affiliated
with various institutions), by comparison, expressed much more certainty that their
institutions did not have an AI policy. Around 95% of both groups reported that they
had received no training on the use of AI, and about 90% of teachers have not taught
their students about the appropriate use of AI.
Discussion
is study sought to better understand how student and teachers perceive of the use
of GenAI in the writing process within a framework of acceptability. e goal of this
research is to contribute to the burgeoning discussion on how GenAI can be integrated
into educational contexts successfully (see Godwin-Jones, 2022; Yeo, 2023). e prevail-
ing narrative in our results demonstrated that for all the steps of the writing process,
students and teachers generally agreed that using GenAI to brainstorm ideas or model
answers, or as a form of cognitive offloading for tasks that the user is already competent
in, is acceptable. Conversely, using GenAI to complete writing task assignments, with or
without disclosing the use of GenAI, is unacceptable.
Both students and teachers perceived GenAI use to be more acceptable in the early
stages of the writing process (i.e., brainstorming and outlining) than in later stages.
ese results suggest that use of GenAI for writing purposes is viewed as more accept-
able when it is fulfilling a supportive role focused on idea generation and organization
rather than when leveraged as an automatic writing completion tool. As to differences
in perspectives, students tended to disagree less than teachers that using GenAI with-
out disclosure was appropriate, and teachers tended to disagree less than students about
using GenAI to model feedback or for evaluation of student writing.
e survey results (Tables7, 8) further illuminated the findings on acceptable use of
AI in educational contexts. Students and teacher both agreed that artificial intelligence
would be a useful tool for teachers and students, but teachers tended to have a more
positive outlook on teacher use of AI than did students. Yet both groups responded
more cautiously when asked if AI would have a positive impact on education, and both
groups reported concern about how AI might be used by teachers and students. e
apparent trepidation regarding AI in education seems to be countered by the perceived
utility of the tool. ese are apprehensions that can be addressed by establishing clear
policies on the use of AI and by educating both teachers and students on acceptable use.
Given the positive impact that university and classroom honor codes have on aca-
demic integrity by delimiting inappropriate practices (Ely etal., 2013; Konheim-Kalk-
stein etal., 2008), it is alarming that 94.1% of teachers reported their university as not
having a policy in place regarding the use of AI and that 89.7% of teachers acknowledged
Table 8 Yes/no item response percentages
#Item Student Teacher
9 My school or university has a policy in place regarding the use of AI YES = 52%
NO = 47% YES = 5.9%
NO = 94.1%
10 My school or university has provided me with training about the use of AI YES = 5.7%
NO = 94.3% YES = 4.4%
NO = 95.6%
11 I have educated my students about the appropriate use of AI N/A YES = 10.3%
NO = 89.7%

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Barrettand Pack Int J Educ Technol High Educ (2023) 20:59
they had never educated their students on acceptable use of AI. Clear university policies
and statements on ethical use of GenAI are needed, such as the framework proposed by
Chan (2023).
Additionally, teachers showed more openness to innovation, but 95.6% of teachers
reported receiving no training on the use of AI from their institution. Many may be
hesitant to embrace GenAI tools, such as ChatGPT, due to concerns related to cost,
privacy, and legality (Kumar, 2023), in addition to a naivety as to how these tools can
be appropriately used for educational purposes. Complicating this issue is the ques-
tion of whether any prescriptions regarding the use of GenAI by students should also
apply to teachers. Teacher training on these issues is urgently required.
e findings of this study underscore the importance of students and teachers being
transparent in their use of GenAI tools. Using AI without disclosure, by both students
and teachers, was considered the least acceptable practice. Yeo (2023) suggests that
educators “accept and befriend [GenAI] by showing learners how to use AI authoring
ethically and gainfully to achieve their learning intentions and goals” (p. 10). If educa-
tors are transparent and clearly state and model acceptable uses of GenAI then students
may follow. While there is a nascent body of literature offering practical suggestions and
advice for using GenAI in education by educators, learners, and researchers (Ingley &
Pack, 2023; Pack & Maloney,2023b; Bonner etal., 2023), an evidence-centered frame-
work for leveraging GenAI in writing and in higher education in general needs further
attention.
Conclusion
By the time this paper is published the GenAI tools and examples of use in this study
will likely be anachronistic to a wider variety of available programs that users might
interact with; a common limitation in emerging technology research. Even as this
paper was being prepared, advances in prompt engineering have shown how stu-
dents or educators might be able to use GenAI tools in ever more sophisticated ways.
Regardless, the seemingly rudimentary prompts exemplified in this study represent
how a teacher or student without much experience or training in prompt engineering
would likely interact with a GenAI tool like ChatGPT. For this reason, the measured
perceptions herein will likely be useful when considering approaches to GenAI inte-
gration in education.
Limitations of the study include the non-random sample which inhibits generaliz-
ing findings to a wider population, and the heterogeneous population of teachers and
students that render between-group comparisons tenuous. Also, the cross-sectional
design of the current study does not take into account the dynamic nature of student
and teacher perceptions. It is reasonable to assume that both student and teacher per-
ceptions of the ethics of using GenAI in writing education will change as individuals
become more aware of and competent in the technology, and as the technology devel-
ops and changes over time.
While this research provides initial insights into student and teacher perspectives
on using GenAI in academic writing, future studies could further examine poten-
tial differences in perceptions of students and educators in varying cultural and

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educational backgrounds, or take a longitudinal approach to see if opinions change as
the technology becomes more prevalent. In addition, investigating stakeholder per-
ceptions of the use of GenAI through more advanced prompts (such as having GenAI
act as a teacher in a writing conference to guide a student through the writing pro-
cess) would likely prove insightful.
Appendix
#1‑ Brainstorming
Student input:
Please brainstorm some ideas for an essay on the topic of urban challenges and global
warming.
AI output:
1. Discuss the impact of increased urbanization on global warming and climate change.
2. Explore the role of city governments in mitigating the effects of global warming, such
as implementing renewable energy sources and reducing greenhouse gas emissions.
3. Examine the challenges faced by cities in developing countries in addressing global
warming and climate change, such as limited access to resources and funding.
4. Analyze the impact of global warming on urban ecosystems, such as heat islands and
the loss of green space.
5. Discuss the ways in which urban residents can play a role in addressing global warm-
ing and reducing their carbon footprint.
6. Evaluate the effectiveness of existing solutions to urban challenges related to global
warming, such as green buildings and public transportation.
7. Explore the potential long-term consequences of global warming for urban areas,
such as sea level rise and extreme weather events.
8. Consider the potential benefits of addressing global warming in urban areas, such as
improved air and water quality, and increased public health.
#2‑ Outline
Student input:
Write an outline of a 5-paragraph essay on the topic of urban challenges and global
warming.
AI output:
I. Introduction
II. Briefly introduce the topic of global warming and its impact on urban areas.
III. esis statement: Despite the challenges, addressing global warming in urban areas
is essential for the health and sustainability of our cities.
IV. Body Paragraph1: e impact of urbanization on global warming and climate
change
V. Discuss the increased greenhouse gas emissions resulting from urbanization
VI. Explore the impact of heat islands on global warming

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VII. Mention the role of cities in contributing to global warming and climate change
VIII. Body Paragraph 2: e role of city governments in addressing global warming
IX. Discuss the ways in which city governments can mitigate the effects of global
warming, such as implementing renewable energy sources and reducing green-
house gas emissions
X. Evaluate the effectiveness of existing solutions, such as green buildings and public
transportation
XI. Body Paragraph 3: e challenges faced by cities in developing countries in
addressing global warming
XII. Explore the limited access to resources and funding faced by cities in developing
countries
XIII. Discuss the potential consequences of global warming for these cities, such as sea
level rise and extreme weather events
XIV. Conclusion
XV. Summarize the main points made in the essay
XVI. Restate the thesis
XVII. Call to action: urge readers to consider the importance of addressing global
warming in urban areas and take action to reduce their carbon footprint.
#3‑ Writing
Student input:
Write a 5-paragraph essay on the topic of urban challenges and global warming.
AI output:
Urban areas around the world are facing a range of challenges related to global
warming and climate change. e increased greenhouse gas emissions resulting from
urbanization contribute to global warming and exacerbate the impacts of climate
change. Additionally, the heat generated by urban areas can create heat islands, which
further contribute to global warming.
City governments have an important role to play in addressing global warming in
urban areas. ey can implement policies and programs aimed at reducing green-
house gas emissions, such as promoting the use of renewable energy sources and
increasing energy efficiency. ey can also invest in infrastructure that supports sus-
tainable transportation, such as public transit and bike lanes.
However, cities in developing countries often face challenges in addressing global
warming and climate change. ey may have limited access to resources and funding,
making it difficult to implement solutions to mitigate the effects of global warming.
ese cities may also be more vulnerable to the impacts of global warming, such as
sea level rise and extreme weather events.
Despite these challenges, addressing global warming in urban areas is essential for
the health and sustainability of our cities. By taking action to reduce greenhouse gas
emissions and adapt to the impacts of global warming, we can protect our urban envi-
ronments and ensure that they remain livable for future generations.

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In conclusion, global warming and climate change present significant challenges
for urban areas. City governments have an important role to play in addressing these
challenges, but they must also overcome obstacles such as limited resources and fund-
ing. By taking action to reduce greenhouse gas emissions and adapt to the impacts of
global warming, we can ensure that our cities remain sustainable and livable for the
future.
#4‑ Revision
Student input:
Rewrite the following paragraph and make it sound more academic:
Global warming is important for several reasons. First, global warming will affect
many people. All countries and their people will be affected by global warming. Sec-
ond, the effects of global warming will be costly. ird, many people will die because
of global warming.
AI output:
e significance of global warming is multifaceted. Its impacts will be far-reaching,
affecting individuals and communities across all nations. Additionally, the economic
costs of global warming will be significant. Furthermore, it is anticipated that global
warming will result in a significant loss of human life.
#5‑ Feedback
Teacher input:
Can you provide some suggestions on how to improve this student’s writing:
Culture shock is a feeling of disorient that occur when somone is in a new culture.
It can be caused by factors such as differences in language, culture, and ideas and cul-
ture shock include homesickness, difficulty eat to new foods, and feeling of isolation
or depression and it is important to recognize that culture shock is normal part of
adjusting to new culture. And it is possible to overcome it with time and effort. Some-
way to cope with culture shock is connect with friends and family back home, and get
out support from others who also have same feelings. By choose to learn about and
adapt to a new culture, individuals can slowly learn enjoy and appreciate new culture.
AI output:
Here are some suggestions for improving the writing:
1. Use clear and concise language: Avoid using long or complex sentences, and try to
use straightforward language.
2. Use transitions: Add transitional phrases or words (e.g., "for example," "however," "in
addition") to help the reader follow the flow of the writing.
3. Use concrete examples: Provide specific examples to illustrate the points you are
making. is will help the reader understand and relate to your writing more easily.
4. Use active voice: Write in the active voice (e.g., "She overcame culture shock by
learning about the new culture") rather than the passive voice (e.g., "Culture shock

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Barrettand Pack Int J Educ Technol High Educ (2023) 20:59
was overcome by her learning about the new culture"). e active voice makes the
writing more engaging and easier to follow.
5. Use proper grammar and punctuation: Review your writing for errors in grammar
and punctuation, and correct them as needed. is will help ensure that your writing
is clear and easy to understand.
#6‑ Evaluate
Teacher input:
Evaluate the quality of ideas expressed in the following students’ paragraph:
I believe arming teachers with guns will make the classroom safer. To begin with, the
only way to stop a bad guy with a gun is a good guy with a gun. Teachers are good peo-
ple and people trust them. Students and teachers can feel safer if the teacher has a gun.
Second, having a gun is protected by the government. e reason why this is protected
is because guns can protect people from dangerous people and governments. erefore,
teachers with guns will make the classroom safer.
AI output:
While the paragraph presents some ideas about arming teachers with guns, the rea-
soning and logic used to support these ideas are problematic and flawed. e first point
made in the paragraph relies on the premise that the only way to stop a bad person with
a gun is to have a good person with a gun, which is not necessarily accurate or supported
by evidence. e second point asserts that having a gun is protected by the government,
but does not provide any context or explanation for this claim. Additionally, the final
statement that teachers with guns will make the classroom safer is not supported by
any evidence or logical reasoning. Overall, the ideas expressed in this paragraph are not
well-developed or thoughtfully presented and do not effectively convey a coherent or
logical argument.
Acknowledgements
Not applicable.
Funding
Not applicable.
Availability of data and materials
The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable
request.
Declarations
Competing interests
The authors declare that they do not have any competing interests.
Received: 25 July 2023 Accepted: 23 October 2023
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