IAFOR Journal of Education: Technology in Education

Volume 12 – Issue 2 – 2024

Perception of ChatGPT Usage for Homework Assignments: Students’ andProfessors’ Perspectives

Irena Miljkovic Krecar
VERN’ University, Croatia
Maja Kolega
VERN’ University, Croatia
Lana Jurcec
University of Zagreb, Croatia

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Abstract
In the context of education, the issues of integrating artificial intelligence (AI) into teaching
and maintaining academic integrity in students’ use of AI are particularly relevant. This paper
empirically examined the issue of ChatGPT usage for writing homework from the perspectivesof students and professors. Study research methods included both quantitative and qualitative
approaches. In Study 1, an anonymous questionnaire was administered to 350 Croatianstudents, users of ChatGPT, to investigate their perceptions, attitudes, habits, and intentions
regarding ChatGPT usage for homework assignments. In Study 2, twelve faculty members
were tested on their accuracy of distinguishing between original students’ papers and
ChatGPT-generated papers. For this purpose, 25 different versions of papers for 8 different
courses were prepared. The results of the students’ survey showed that most students still do
not use ChatGPT regularly and have neutral attitudes about its usefulness, ease of use, risks,
and intentions for future use. In addition, they were moderately concerned about ethical issues
around its usage. Differences across gender and field of study were found. Professors, on theother hand, reported having average self-efficacy in appraising authorship, which is in line with
their low average accuracy of 53%. Accuracy in distinguishing was lowest when ChatGPT wasinstructed to write a paper as a student. These results strongly suggest the necessity for clear
guidelines, plagiarism detection tools, and educational initiatives to promote ethical use of AI
technology.
Keywords: academic integrity, attitudes, ChatGPT ethical considerations, higher education,
students’ and professors’ perspectives

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In the dynamic development of higher education, implementing Artificial Intelligence (AI) in
the classroom introduces many different opportunities and even more complex challenges. Oneof the main challenges is how to balance the indisputable advantages of technology withmaintaining academic integrity. Academic integrity is the foundation of higher education,encompassing principles such as responsibility, honesty, trust, and fairness. Kiralj (2020)
emphasizes that students are the driving force behind the future strength of society. Student
honesty is essential, not only for academic success but also for their future work ethics, which
is a prerequisite for economic and social development. The question of authorship andoriginality becomes increasingly complex in an academic environment where AI could
significantly contribute to content creation.
Educators play a crucial role in fostering a culture of academic integrity. By adapting
pedagogical approaches that emphasize critical thinking and originality, educators canempower students to use technology without compromising academic integrity. However, italso means they should adapt to new circumstances and accept new challenges, continuously
improving their digital competencies.
This paper aims to address two important questions:
1. What are the students’ habits and attitudes towards the use of ChatGPT in writing
homework assignments, with an emphasis on the ethical component of their use?
2. Can educators distinguish between the papers generated by ChatGPT and the ones
written by their students?
In the following section, an overview of previous research on the use of AI in education isprovided. Subsequently, the methodology of the empirical study conducted with a group of
students and professors is described. The results are then presented alongside the discussion,
followed by the conclusion with practical guidelines and recommendations.
Literature Review
Many scholars have highlighted multiple benefits of using ChatGPT for students and educatorsfor educational purposes: it could serve as a starting point for personalized learning and provide
personalized feedback; support individual and group research, provide ideas and guidance on
designing and adapting educational content to make it more appealing and accessible tostudents; improve writing skills, especially in English; offer various possibilities for disabledstudents; and improve time efficiency (Dwivedi et al., 2023; Kasneci et al., 2023; Memarian &Doleck, 2023; Rahman et al., 2023; Rasul et al., 2023; Sok & Heng, 2023). This innovative
way of learning could improve student’s motivation and engagement in acquiring new
knowledge and skills, providing educators time to focus on more demanding tasks such as
developing students’ critical thinking, complex problem-solving, responsible decision-making,communication skills, and so on, and give students emotional support in their progress.

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Rasul and colleagues (2023) summarized five main challenges of using GPT in an academic
context: academic integrity, reliability, inability to evaluate and reinforce graduate skill sets,
limitations in assessing learning outcomes, and potential biases and falsified information in
information processing. Further, using GPT could lead to an unfair disadvantage for students
who do not have access to it (Dwivedi et al., 2023).
Previous research shows that both professors and students have a positive attitude towards
using ChatGPT for educational purposes, albeit with concerns, predominantly centered on theaccuracy of the generated data (Chan & Hu, 2023; Kiryakova & Angelova, 2024; Lozano &Blanco Fontao, 2023). Simultaneously, professors have a more negative attitude toward using
ChatGPT, considering cheating and plagiarism as a major challenge (Iqbal et al., 2022;
Nguyen, 2023; Waltzer et al., 2023). The assessment of students’ work in the form of essays,
projects, research papers, or similar tasks, often conducted remotely, is a pivotal component ofhigher education. These assignments serve to evaluate a broad spectrum of learning outcomes,
including the ability to locate, summarize, and paraphrase relevant literature, critical analysis
skills, creativity, innovation, and attitudes. In achieving educational outcomes maintaining
academic integrity despite the easily accessible online tools that can complete most of the work
becomes a great challenge. Although research indicates that online cheating is more prevalentthan traditional offline cheating, studies on online academic dishonesty are still in the earlystage (Chiang et al., 2022). Despite the prevailing belief among most students that cheating is
ethically wrong, they frequently rationalize their engagement in academic cheating(Majstorović, 2016; Waltzer & Dahl, 2023). Prior research suggests that various motivational
factors significantly influence students’ decision to cheat (Miles et al., 2022; Sozon et al., 2024;
Waltzer & Dahl, 2023). For example, lack of understanding of what academic misconduct is,increased pressure on students, fear of failure, time pressure, lack of motivation, lack ofinstitutional policy, perceived risk and penalties, and peer influence. According to Zhao andcolleagues (2022) perceived peer cheating is significantly stronger than other factors and plays
a crucial role in students’ academic cheating behavior. Considering all these factors that
influence attitudes toward academic dishonesty, it is essential to also consider perspectives ontechnology use to gain a more comprehensive understanding of attitudes toward the use of AI
in academic settings. Measuring attitudes toward ChatGPT usage for homework assignments
serves as the initial step towards understanding present behaviors and forecasting future
conduct.
In this paper the Technology Acceptance Model (TAM; Davis, 1989) was used as thefundamental theoretical framework. TAM emerged as a modification of the Theory of
Reasoned Action (TRA; Ajzen & Fishbein, 1980) and the Theory of Planned Behavior (TPB;
Ajzen, 1985), both cited in Ajzen 1991 and is frequently employed to examine how users
interact with various technologies. According to TAM, two factors determine whether a newtechnology will be embraced by its potential users: (1) perceived usefulness and (2) perceived
ease of use. Perceived usefulness is defined as "the degree to which a person believes that using
a particular system would enhance his or her job performance" while perceived ease of use
refers to "the degree to which a person believes that using a particular system would be free ofeffort" (Davis, 1989, p. 320). If a new technology is considered easy to use, there is an increased

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likelihood that it will also be perceived as useful, thereby enhancing its acceptance. Over time,
TAM has evolved to become the key model in understanding the potential acceptance orrejection of technology, which is of special importance for educational challenges in a digital
environment. Although the application of TAM has confirmed its robustness, the model’ssimplicity and the limited understanding of the antecedents of technology acceptance led to thedevelopment of extended versions such as TAM 2 (Venkatesh & Davis, 2000) and TAM 3(Venkatesh & Bala, 2008). Despite these models, numerous scholars continue to use theoriginal TAM, extending it with variables and theories relevant to their specific study contexts(Sukackė, 2019).
Numerous studies have confirmed TAM in the field of education (Abdullah & Ward, 2016;
Granić & Maragunić, 2019; Sherer et al., 2019) showing the significance of perceived
usefulness and perceived ease of use in understanding students’ attitudes towards using
ChatGPT for learning (Obenza et al. 2024; Rahman et al., 2023; Shaengchart, 2023). Also,
several studies have shown that AI can produce high-quality materials in different disciplines(Herbold et al., 2023; Susnjak, 2022; Yeadon et al., 2023) and the educators can hardly
distinguish students’ work from the work written by ChatGPT (Busch & Hausvik, 2023;
Fleckenstein et al., 2024; Liu et al., 2023; Waltzer et al., 2023). Bašić and colleagues (2023)
highlighted that ChatGPT-assisted writing quality is highly connected with previous
knowledge and skills, and inexperienced students could show poorer essay writing
performance with its usage. To make use of all the advantages of AI, students need a solid baseof knowledge to ask proper and relevant questions.
However, there is still a lack of empirical examination of attitudes toward using ChatGPT as ashortcut for writing homework assignments. Most investigations have been conducted within
the domain of English as a Second Language (ESL) instructional essays, with comparatively
fewer studies in other disciplinary contexts. This opens numerous possibilities for further
research into tailoring home essay instructions across diverse subjects, particularly in the social
sciences and humanities, where essay writing constitutes a significant component of thepedagogical process. Additionally, it is crucial to explore the materials produced by ChatGPTin languages other than English, to understand its suitability and potential limitations in
multilingual educational settings. This topic is especially important to educational institutions
considering the importance of academic integrity preservation. Furthermore, while previous
literature strongly suggests that AI (namely ChatGPT) can produce convincing “student
works”, there is a lack of objective empirical evidence on how proficient professors are in
distinguishing between student work from AI-generated content in various courses.
Methodology
Goals
This research has two main goals, first, to empirically examine students’ attitudinalcomponents of cognition, emotions, and behaviors related to the use of ChatGPT in homework

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assignments. Second, to determine professors’ level of accuracy in distinguishing students’
own writing from AI generated homework assignments.
The following research questions were formulated for this purpose:
1. What attitudes, habits, and intentions do students have when utilizing ChatGPT for
writing homework assignments, with a particular emphasis on the ethical dimensionof their usage?
2. Are there significant differences in ChatGPT usage perceptions, attitudes, andhabits between students of different gender and field of study?
3. Can professors accurately discriminate between homework assignments written by
students and those generated by ChatGPT?
An online survey of 350 students from various Croatian universities (Study 1) and a workshop
with 12 professors from VERN’ University1 (Study 2) were conducted in December 2023 toanswer the research questions. This included both quantitative and mixed (quantitativequalitative) approaches. The studies adhered to key ethical considerations, including voluntary
participation, informed consent, and the maintenance of anonymity and confidentiality.
Participants were fully informed about the research purpose, procedures, and their right towithdraw at any time without negative consequences. Ethical approval was obtained from theinstitution’s ethics committee.
To ensure clarity, we first present the method, findings, and discussion of the results of thestudents’ survey, followed by the same information for the sample of professors. Finally, thepaper addresses limitations of the research and provides a common conclusion.
Study 1: Students’ Survey
MethodThe questionnaire for students about their perceptions, attitudes, habits, and ethical issues
related to ChatGPT usage was constructed based on the TAM model (perceived usefulness,
perceived ease of use and intentions) and research questions. Some items were created resulting
from student-led debate (organized for this purpose with 40 students) on the use of ChatGPT
in homework assignments2. Clarity of the items was pilot tested on another group of 15
students. Besides sociodemographic questions, the final version of the questionnaire contained
items about ChatGPT usage in completing homework assignments: (1) habits, (2) level of1

Two of the three authors are employed at VERN’, which was chosen for the research due to practical reasons.
Most of the initial students’ sample are from VERN’ (N=227). When the desired sample size was not achieved,
the call for participants was extended to other Croatian students through the private and professional networks of
the authors.
2
The debate was conducted as part of regular psychology classes with two groups of students from the VERN’
University (Cyber communication and network sciences (N=25) and Business IT (N=15)). The professor who
conducted the debate is also an author of this paper.

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acceptability, (3) attitudes, (4) ethical issues, and (5) perception of professors’ knowledge of
ChatGPT.
Habits were measured by frequency of ChatGPT usage for completing homework assignmentson the scale of five (1 - few times only, 2 - few times a month, 3 - once or twice a week, 4 several times a week, 5 - every day), and a multiple choice-multiple select question about the
purpose of using ChatGPT, offering 16 purposes.
Level of acceptability to use ChatGPT when writing homework was measured by one item on
a 10-point scale, ranging from 1 (to give me some initial information or serve as inspiration)
through 5 and 6 (to take over half of the written content, and shape and complete the rest
myself) to 10 (to completely write a paper or task for me, which I will then hand over to theteacher without changes).
Attitude Towards ChatGPT Usage in Completing Homework Assignments scale contained 16
items rated on a 5-point Likert-type scale, ranging from 1 (absolutely disagree) to 5 (absolutely
agree). Exploratory factor analysis (EFA) extracted a four-factor solution and accounted for
63.5% of the total variance. Each factor consisted of four statements about ChatGPT usage for
writing homework assignments. Intention (F1) measured planned behavior of utilizing
ChatGPT for tasks deemed too challenging, tedious, time-consuming, and so on, for the
students; Risk of Use (F2) referred to students’ diminishing learning adaptability, criticalthinking abilities, and academic skills; Easy and Reliable Use (F3) measured trust in theaccuracy of the given information and the ease of ChatGPT use; Usefulness (F4) was about
fostering the development of students’ computer skills, creativity, interest, and enjoyment (SeeAppendix A). The obtained factors supported both TAM (Shaengchart, 2023; perceived
usefulness, ease of use and intentions to use ChatGPT) and TAME‑ChatGPT (Abdaljaleel etal., 2024; perceived usefulness, behavioral/cognitive factors, perceived risk of use, perceived
ease of use) models and scales. Subscales showed adequate reliability (α) as follows: .85, .80,
.76, .80.
Ethical Issues of ChatGPT Usage in Completing Homework Assignments Scale consists offour-items describing the potential implications ChatGPT usage may have on academic
integrity. Items were rated on a 5-point Likert-type scale, ranging from 1 (absolutely disagree)
to 5 (absolutely agree). EFA extracted a one-factor solution (Appendix B) and accounted for
63.5% of the total variance. Ethical Issues as separate scale is in line with Farhi and colleagues.
(2023) who highlighted concerns regarding ethics of the potential over-reliance on ChatGPT
for educational tasks. The scale showed adequate reliability (α=.77).
Perception of Professors’ Knowledge of ChatGPT were measured with four items focusing on
how students perceive professors’ abilities to detect ChatGPT papers. Items were rated on a 5point Likert-type scale, ranging from 1 (absolutely disagree) to 5 (absolutely agree).
Snowball sampling was used to recruit Croatian students. Along with a link to thequestionnaire, participants were informed about research’s core components and that their

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involvement was voluntary and anonymous, with no credits awarded for participation. The
final sample, consisting of 350 students, was selected based on the criteria of having personal
experience in using ChatGPT for study purposes. 61.1% were females and the total sample
ranged in age from 18 to 46 with a mean age of 23 years (M=22.75, SD=5.52). Students wereat the undergraduate (68.3%) and graduate (31.7%) levels of education from different fields ofstudy: 58% of students were majoring in social sciences, 34.3% in science and technology,
6.3% in humanities, 1.1% in medical sciences, and 0.3% in arts.
Results
Data were analyzed using the Statistical Package for Social Sciences, version 26.0 (SPSS Inc.,
IL). For most variables, the values of skewness and kurtosis were between +1 and -1, except
for the variable purposes. Nonparametric statistics were used for skewed and ordinal variables
and parametric for interval variables with normal distribution. Therefore, with Mann-Whitney(M-W) U test and two-way analysis of variance (ANOVA) we tested the effect of gender andfield of study differences (Cohen d and partial eta squared ηp2) on items and scales of ChatGPT
usage. The level of statistical significance was p=.05.
Students Habits and Acceptability to Use ChatGPT in Completing Homework Assignments
Our analysis (Table 1) showed that most students have not developed the habit of using
ChatGPT in completing their homework assignments. Half of them used it only a few times bynow (50.6%) and the rest of them use it few times a month (24.3%), or on the weekly basis(22.5%), and only few admitted using it daily (2.6%). It was more often used by male thanfemale students (d=0.63), and by science and technology students than social sciences students(d=0.58).
On a 10-point acceptability scale almost one third (29.4%) of students considered ChatGPT
usage as acceptable tool for getting some initial information or serving as inspiration (level 1).
Most students (74.8%) found it unacceptable to take over half of the written content, and shapeand complete the rest themselves (level 5 and 6) while the rest found it acceptable. The usageof ChatGPT to completely write a paper or task (level 10) was considered acceptable by one
percent of the students. The median indicated that the average acceptability level was 3 (Table1). Male students rated its usage as more acceptable than female students did (d=0.24).

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Table 1
Gender and Field of Study Differences in Experiences of ChatGPT Usage in Completing
Homework Assignments
Variables
Frequency

PurposeAcceptabilityGroupsMale
FemaleSoc. Sci.
Sci. Tech.
Male
FemaleSoc. Sci.
Sci. Tech.
Male
FemaleSoc. Sci.
Sci. Tech.

Median
2
1
1
2
5
4
4
5
3
3
3
3

Mean Rank
213.75151.19142.03195.78210.49153.26146.24
188.66190.74165.82
163.12160.10M-W U test
9350

Z
-6.10

p
.000

8127.0

-5.39

.000

9793.5

-5.20

.000

8981.0

-3.98

.000

12479.5

-2.30

.022

11952.5

-0.29

.774

Note. Soc.Sci.=Social Science, Sci.Tech.=Science and Technology

On average, students chose four purposes (Median=4) of using ChatGPT in completing their
homework assignments, and as Figure 1 presents, mostly for finding content that interests them,
deeper analysis of a subject matter and for writing textual assignments (essays, seminars,
reviews, etc.). Male students (d=0.58) and those studying technical sciences (d=0.39) had morereasons for its usage (Table 1), which is in line with the previously mentioned higher frequency
of use within these subsamples.
Figure 1
Reasons for Utilizing ChatGPT in Completing Homework Assignments (%)

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Attitudes and Ethical Issues Regarding ChatGPT Usage in Completing Homework
Assignments
As evident in Table 2, average ratings of 3 (neutral opinion) were obtained on all scales, withstandard deviations suggesting not much variation. Two-way ANOVAs were conducted on thescales to determine potential differences between male (N=130) and female students (N=193),
as well as students majoring in social sciences (N=203) and science and engineering (N=120).
The subsamples for testing interaction effects consisted of males majoring in social sciences(N=50) and in science and engineering (N=80), and females majoring in social sciences(N=153) and in science and engineering (N=40).
Results of ANOVA showed that male students perceived more usefulness (ηp2=.022), and lessrisks (ηp2=.013) in using ChatGPT than female students. They are also intended to use it morethan female students (ηp2=.018). Field of study also showed significant effect on intention andreliability. Social science students had more confidence in ChatGPT (ηp2=.028) and higher
intention to use it for future assignments (ηp2=.027) in comparison to science and technology
students.
Table 2
Gender and Field of Study Differences in Attitudes Towards ChatGPT Usage in Completing
Homework Assignments
Descriptive statistics M (SD

ANOVA

ScalesM

F

Soc.Sci.

Sci.Tech.

Total

Fgender

FfieldFInteraction

Risks

3.13
(1.06)

3.42(0.97)

3.34(0.98)

3.24(1.06)

3.31(1.01)

4.14*

0.03

2.26

Usefulness

3.42(0.98)

3.13(0.91)

3.25(0.96)

3.23(0.93)

3.24(0.95)

7.34**

2.05

2.99

Easy &
Reliable

3.13(0.81)

3.18(0.80)

3.26(0.78)

2.99(0.76)

3.16(0.79).46

9.31**

0.01

Use
Intention

2.88(1.13)

2.71(0.99)

2.87(1.04)

2.62(1.05)

2.78(1.05)

5.95*

8.73**

0.35

EthicalIssues2.70(1.03)

2.95(0.92)

2.87(0.98)

2.82(0.97)

2.85(0.97)

3.23

0.42

8.92**

Note. * p<.05, **p<.01; Soc.Sci.=Social Science, Sci.Tech.=Science and Technology.

The results revealed statistically significant interaction effect of gender and field of study on
perceived ethical issues on ChatGPT usage when doing homework assignments (ηp2=.027). In

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social sciences female students were most concerned about potential implications on academic
integrity while male students had least ethical doubt (Figure 2).
Figure 2
Interaction Effect of Gender and Field of Study on Perceived Ethical Issues on ChatGPT Usage
When Doing Homework Assignments

Perception of Professors’ Knowledge of ChatGPT
Table 3 shows the students mostly had a neutral opinion on professors’ knowledge of ChatGPT.
Nearly half of the students (42.7% and 46.5% answered absolutely or mostly disagree on thefive-point scale for first two items) were completely or mostly convinced in professors’
efficacy of appraising authenticity of student papers, even though nearly equal percentage ofthe students expressed concern about objectivity of the assessment practices (39.4% answered
4-mostly agree or 5-absolutely agree). Twenty-two percent of students agreed that it is up to
the students to manage their assignments, while it is the professors’ responsibility to identify
any misuse.

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Table 3Students’ perceptions of professors’ knowledge of ChatGPT (%)
Items/Scale
Most professors do not verify theauthenticity of student papers.
Teachers cannot detect whether
homework assignments were
written by ChatGPT or the student.
It is up to us students to ‘get by’with the help of ChatGPT, and it is
up to the professors to try to catch
us in that.
Due to ChatGPT, homeworkassignments cannot be objectively
evaluated.

1

2

3

4

5

M

SD

14.0

28.7

30.9

15.8

10.6

2.80

1.18

22.8

23.7

30.6

15.9

6.9

2.60

1.20

31.7

20.6

25.7

12.0

10.0

2.48

1.31

10.4

15.6

34.6

25.6

13.8

3.17

1.16

Discussion
As seen in our sample of Croatian students, male students use ChatGPT more than female,consistent with previous studies (Draxler et al., 2023; Siregar et al., 2023). Similarly, inGermany male students and students of engineering sciences, mathematics and natural sciencesused AI-based tools most frequently (von Garrel & Mayer, 2023). According to Bouzar et al.
(2024) males reported longer usage times, indicating a potentially deeper engagement with thetool, while females demonstrated higher usage frequency, suggesting more frequentinteractions but possibly for shorter durations.
The unethical usage of ChatGPT (to completely write a paper or task) was considered
acceptable by only one percent of the students, with male students rating it more acceptable
than female. Such findings come as no surprise, as many previous studies suggest that maleparticipants are more prone to unethical academic behavior than female participants (Zhang etal. 2017).
Furthermore, results of ANOVA showed that male students perceived more usefulness and lessrisks in using ChatGPT than female students. They also intend to use it more than female
students. Yilmaz and colleagues’ (2023) findings that male students found ChatGPT easier to
use then female students, similarly, sheds light on the gender-specific responses to the userfriendliness of ChatGPT. The authors observed that understanding these gender-specificresponses is crucial for designing AI systems that are inclusive and accessible to all users,
regardless of gender. In our sample we can assume that both genders have equal access to
ChatGPT; therefore, it is crucial to explore why female students exhibit less interest intechnological innovations and have not fully explored the benefits of using this tool.

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Field of study also has a significant effect on intention and reliability. Social science students
had more confidence in ChatGPT and higher intention to use it for future assignments incomparison to science and technology students. This is somewhat surprising as we havepreviously seen that they use it less. We can assume that they are just discovering its
possibilities, as opposed to science and technology students who might have a better knowledge
and steadier dynamic of ChatGPT usage.
Additionally, two-way ANOVA indicated a statistically significant interaction between genderand field of study on perceived ethical issues related to ChatGPT usage. Female students insocial sciences were the most concerned about the potential impact on academic integrity,
whereas male students in social sciences had the least ethical concern. These results are in linewith recent research showing that chatbots are perceived by students as a valuable tool for
extensive research and analysis, often required in disciplines such as social sciences (Chan,
2023; Jowarder, 2023), however, women have more negative attitudes towards academic
dishonesty (Witmer & Johansson, 2015).
Finally, students mostly held a neutral view on professors’ knowledge of ChatGPT. Nearly halfof them were completely or mostly confident in professors’ ability to appraise the authenticityof student papers, although a nearly equal percentage expressed concern about the objectivityof future assessment practices. Additionally, 22% of students agreed that it is the students’
responsibility to find their own way in doing assignments and the professors’ role to catch any
misconduct. Given this attitude, if students are made aware that the consequences forcommitting academic dishonesty were little to non-existent, then students are likely to commit
such acts (DiPietro, 2010). According to San Jose (2022), students interpreted teachers’leniency during the pandemic as implicit consent to cheat on exams and even plagiarize theirsubmitted outputs. This lack of action or tolerance from teachers is likely to lead to an increasein academic dishonesty.
Study 2: Professors’ Survey
MethodMain goal of the professor’s survey was to estimate their (subjective) self-efficacy and actual(objective) efficacy in determining the authorship of student homework. Also, the aim was toraise awareness about possibilities and ease of utilization of AI for unethical purposes among
students, encourage mutual discussion and to initiate certain steps at the institutional level toeffectively address this challenge.
For practical reasons, focus of recruitment was all full-time faculty members of VERN’
University. They were sent an invitation to participate in “the workshop related to the use of
artificial intelligence". Although initially more of them applied, the final workshop had 12
professors from various disciplines. Among them 2 were males and 10 females, and majorityof them have at least 15 years of teaching experience at the same institution (besides one
younger male professor with “only” 8 years of teaching experience).

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Weeks before the study, professors were requested to provide homework instructions fromtheir courses, contributing to the final grade. They were also asked to submit several examples
of student homework of varying quality, focusing on assignments submitted before November
2022 (when ChatGPT became widely used). Based on their contributions, materials for thesurvey were created.
The following eight courses were selected for the study: Academic Writing, Basics ofMarketing, Organizational Psychology, Management, Research Methodology, ICT in
Hospitality, Transmedia Storytelling, and Basics of Entrepreneurship. For each course, 2 to 5
variants of assignments were created (25 in total), including both original student work andassignments written by ChatGPT. The proportion of ChatGPT-generated papers ranged from
50% to 66% per course. Most ChatGPT papers were created using the free 3.5 version, with
the instructions from the professors directly copied as a prompt. More complex papers,
including graphical displays of fictitious results or summaries of scientific papers, were craftedwith a prepaid version 4. In some cases, ChatGPT was instructed to write poorly to simulate astruggling student’s work.
The workshop began with an overview of the survey results conducted on students (describedearlier), with the aim of raising awareness of the presence of AI use and discussion about its
threats to academic integrity. The main part of it was professors’ evaluation of the authorshipof different course papers. Each professor received a folder containing mixed (student and AIgenerated) papers written for a particular course. All texts were formatted uniformly (font, size,alignment), and professors were instructed not to judge based on these elements.
For each paper professors answered three questions:
1) Who wrote the text (AI or student)?
2) How confident are they (on a scale from 1 to 5) in their appraisal?
3) How did they determine (open-ended question) the papers’ authorship?
After reviewing and grading all the papers in the folder, they took another folder, with adifferent course, and passed their folder to the next evaluator. Professors were instructed tokeep their grades sealed in a closed folder to avoid influencing the subsequent assessors. Theyevaluated the texts at their own pace and were not required to review all 8 courses but wereencouraged to select courses related to their teaching area. After about 45 minutes it wasdeemed that the professors were saturated, and the exercise concluded, with each course being
evaluated by at least 5 professors, and up to 9 in some cases.
Results
Before being provided with materials to assess assignments authorship, professors selfappraised (using the Curi.live platform) their efficacy in this task, on one question with answers
ranging from 1 (very low) to 5 (very high). The average score obtained from 12 teachers was
3, indicating a medium level of self-efficacy.

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A review of the completed evaluation sheets revealed that the accuracy of the assessed paper’s
originality varied significantly, ranging from 17% to 100% across the 8 courses and 25 papers.
The average overall accuracy rate was 53.75% which is akin to a roll of the dice. The average
confidence in one’s assessment also fluctuates, ranging from 3 to 4.33 per individual task;
however, as evident, this confidence is not accompanied by corresponding accuracy. Table 4
presents the main findings.
Table 4.
Results of Professors’ Evaluation of Authorship
Course (task description)

Academic Writing(5 paragraph essays ofdifferent types in English)

Basics of Marketing(1 page, survey questions fordifferent survey topics)

Organizational psychology(2 pages; parts of researchseminar with results section,e.g., graphs on different
survey questions)Management(1 page; movies reviews
written to show a clear
association to management)

Methodology(Research outline - problems,
goals, hypothesis, method,sources- in PPT on 10 slides)

Tasks(author)AW1 (student)
AW2 (student)
AW3 (AI)AW4 (AI as poor student)
AW5 (AI)
Average accuracy /certainty
BM 1 (student)
BM 2 (AI)BM 3 (AI as poor student)
Average accuracy /certainty
OP 1 (student)
OP 2 (AI)OP 3 (AI, asked to write
about known Croatiancompany)
Average accuracy /certainty
MAN 1 (student)
MAN 2 (AI)MAN 3 (student)
MAN 4 (AI)Average accuracy /certainty
MET 1 (student)
MET 2 (AI, as poor student)
MET 3 (AI)Average accuracy /certainty

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Accurately / Average
Total assessed certainty
4/6
3,17
3/6
3,33
6/6
3,67
2/6
4,17
6/6
3,67
70%
3,6
3/5
2/5
1/5
40%

4,2
3,75
3,5
3,82

6/6
3/6
2/5

3,33
3,4
3,33

63%

3,35

4/6
2/6
1/6
1/6
33%

4
3
3,5
3,33
3,82

4/6
1/6
6/6
61%

3,86
4
4,29
4,05

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ICT in hospitality(2 pages of a larger seminar;
ex of ICT in hospitality foundin scientific papers)
Transmedia storytelling(1 page; 2 narrativeextensions of the same
Croatian movie)
Basics of entrepreneurship(1 page; part of a larger paper- an interview with anentrepreneur)

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ICT 1 (student)
ICT 2 (AI)Average accuracy /certainty
TMS 1 (AI)TMS 2 (AI)TMS 3 (student)
Average accuracy /certainty
BE 1 (student)
BE 2 (AI, asked to write
emotionally)
Average accuracy /certainty

4/8
4/8
50%

3,63
3
3,32

5/6
2/5
4/6
63%

4
4
4,33
4,11

6/9
3/9

4,2
3,78

50%

3,99

For the Academic Writing course, the average accuracy is the highest among all courses at
70%. Moreover, one person appraised the authorship of all four essays correctly. On the other
hand, if the AI is instructed to write a poorly crafted paper, professors most frequently misjudge
the originality, confidently assuming it to be a student’s work. This finding is significant, as
students can easily direct AI to produce a paper resembling a student’s work, specifying thedesired educational level and quality.
In the Basics of Marketing, the accuracy of assessment was 40%, worse than guessing by
chance. It might be because, when instructed to mimic a poor student’s work, ChatGPT
produced highly convincing texts. However, one person (not teaching that course) appraised
all three papers correctly. There was a clear correlation between the accuracy of assessment
and the confidence in these judgments. The highest average certainty corresponded with themost accurately assessed work, and vice versa.
For Organizational Psychology, the accuracy was high, but certainty in judgments was low.
All teachers accurately identified the original student work, likely due to numerous typos. One
person (professor of the same course) appraised all three papers correctly. The poorest
estimation was for work where AI was instructed to fabricate research on employees of a
specific Croatian company, leading professors to believe it was genuine student work. The low
certainty in judgments might also stem from ChatGPT 4 creating graphs based on fictionaldata, a feature unfamiliar to many professors (since it is not available in the free 3.5 version).
In the Management course, the lowest accuracy in total was observed (33%) and no one
correctly appraised all four papers. ChatGPT convincingly wrote film reviews, drawing clear
parallels with the requested topic and choosing films akin to typical student selections, leadingteachers to believe these were authentic student works.

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In Methodology, assessment accuracy was among the highest (61%), but no one appraised all
the papers correctly. Also, the poorest accuracy was, once again, visible, when ChatGPT wasinstructed to write a paper as a bad student.
For the ICT in Hospitality course, the average accuracy was 50%, equivalent to guessing, andthree professors (not lecturers of the same course) were correct for both papers. Interestingly,
professors used nearly identical arguments to classify a work as either student or AI-created.
Transmedia Storytelling was evaluated with the highest certainty, and its accuracy was among
the better ones (63%), however only one professor (not of the same course) appraised all thethree papers correctly. A few of the professors noticed that all the nouns in the text were
capitalized, leading them to correctly conclude that the text was AI-generated.
Finally, for the Basics of Entrepreneurship, appraised by most professors (N=9), the average
accuracy was 50%, not any better than guessing. Three professors appraised both papers
accurately (two of them not lecturing the same course, the third one being anonymous). The
AI’s capability to write emotional and personal texts misled professors into believing thesewere student papers, which reflected in a high average certainty of 3.99 in these appraisals.
In Table 5, accurate and inaccurate arguments used by professors to assess the authorship ofpapers are collectively presented. Arguments employed in the evaluation of genuine student
work and ChatGPT-generated work are shown separately. It is evident that professors’
assessment criteria are inconsistent, with identical arguments sometimes used to declaresomething as original student work or to reject its originality.

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Table 5
Correct and Incorrect Professors’ Arguments for Assessing the Papers’ Authorship
Arguments /AuthorStudent

ChatGPT

Correct Arguments

Incorrect Arguments

Content:
personal details; realistic; childish;shortcomings in the description of
results; expression; illogical; imprecise;
typical student mistakes; unrelated; itrefers to examples; specific details; itsounds authentic; expected work for the
student; own opinion.

Content:
no emotions; too professional;
no feelings.

Form:
spelling; spelling errors; grammar;
minor text errors; first-person writing;writing style; simple language; typos;
mistakes; I form; short sentences; nonumbered tables; formatting tables;

Form:language; sentence structure;
no structure, spelling; nosource; a lot of foreign terms;
style; non-standard Croatian
language; advanced language;
too good wording for a student;
good English.
Content:
seems naive; a personal
perspective; gut feeling; as
expected for a student; toorealistic for GPT; like a song;modest; too casual; passionate
with own opinion; short andpersonal; emotional
expressions

Content:
Arguments; no sources; stereotypically;
too professional; it is not the level ofstudents; too good and advanced;
complex expressions; great photos; too
general; too poetically retold;
impeccable.

Form:brief; writing style; flawless; very neat;technical elements; style and language;eloquently; school template; 1st letter
capitalized; uppercase and lowercase
letter; colon then enumeration; pictures;
ChatGPT style; ornate literary style

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Form:
style as a student; writing style;strange constructions; sentence
structure; spelling; language,style;

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Discussion
Before the emergence of ChatGPT and other similar AI tools, various graphic elements – suchas different fonts within the same text or differently colored backgrounds of text paragraphs –were used by professors for quick detection of texts that were copy-pasted directly from theinternet. However, that "old" plagiarism detection argument in AI simply "doesn’t hold water."
As seen, our professors have a medium level of self-efficacy (M=3) in assessing the authorshipof student assignments versus AI-generated papers. As the sample consisted of 12 highly
motivated professors who voluntarily joined the research it is reasonable to presume that these
professors may have been more critically oriented and had a lower level of confidence in their
judgments compared to other professors who did not respond to the invitation to the workshop,
considering that they did not need it. On the other hand, their self-criticism is justified since
the objective assessment accuracy is low (53.75%). In a similar research Busch and Hausvik(2023) found that the authors were correctly identified in 60% percent of both the ChatGPTgenerated and student-written exam answers. However, as they tested the accuracy in rating
exam questions, their results are not fully comparable with ours, as we tested various
homework assignments, that usually produce higher quality and quantity variance among
students. We therefore assume that in more complex tasks the authorship will be lessdetectable, and with AI development this problem will only get bigger.
Busch and Hausvik (2023) also suggested that teaching experience correlates with higher
accuracy rates. In our sample (although small) we only had experienced professors, so we canspeculate whether the accuracy rate would be lower on a more heterogeneous sample orwhether it is impossible to go below the random guessing rate. Similarly, Liu et al. (2023)
suggest that training (previous exposure) can enhance participants’ ability to distinguishbetween student essays and machine-generated text. Likewise, our results indicate (Table 5)that some AI work is more easily noticeable to (some) professors, e.g., the one that follows the
typical format of ChatGPT output, with headings, subheadings, and bullets. This kind ofobservation can only be drawn from personal experience with ChatGPT, which suggests thatprofessors need to familiarize themselves with the AI tools that their students use. Some
professors do not have any experience with ChatGPT and are still surprised with its capabilities
and are therefore easier to deceive. Anyone who experiments with it a little can ascertain its
capability to produce emotional, creative, personal, and witty content, not to mention thecapabilities of premium ChatGPT 4.0 version. Over time, professors will hopefully learn the
typical style of texts generated by ChatGPT (or similar popular tools) and will be able to detect
at least those who have not made the effort to even format the text independently, let alone
write it.
Fleckenstein and colleagues (2024) found that professors were overconfident in their
judgments, particularly when they thought a text was written by a student and when the text
was of low quality. Likewise, in this research, professors overall tend to label papers that are"too good" as plagiarism, while those showing errors, writing clumsiness, or emotional
connotations are deemed original student work. As shown in Table 4, when ChatGPT is
instructed to write papers from the perspective of a student, a poor student, or emotionally,

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professors exhibit the lowest detection accuracy. In other words, it only takes minimal
modification to ChatGPT prompt to make its output almost undetectable. Therefore, it isessential for professors to remain vigilant and continuously update detection methods.
Limitations
This research has a few limitations that need to be considered. Firstly, the sample size of
professors was relatively small and consisted mainly of experienced middle-aged individuals.
Although the invitation to participate in the research was sent to all professors from the chosen
university (33), only 12 responded. This suggests those who participated were particularly
motivated and interested in AI in higher education. Additionally, while the student sample wasmoderately large, it may not fully represent the wider student population as students of social
sciences (58%) and females (61.1%) dominated the sample. To address these limitations, future
research should aim for larger and more diverse samples.
Secondly, while e-survey questionnaires are convenient, cost-effective, and eco-friendly, they
can introduce biases such as self-selection. Students who are more interested or motivated may
be more inclined to participate, skewing the results. Furthermore, there is a risk of social
desirability bias, particularly considering the potential for using ChatGPT as an academic
cheating tool. To delve deeper into this issue, future research should employ a combination ofqualitative and quantitative methods for both students and professors.
Lastly, the rapid evolution of technology may render some findings less relevant over time.
Currently, many students and professors may not fully grasp the possibilities offered by AI,
and institutions may lack comprehensive strategies for addressing academic integrity concerns
related to AI. However, AI will become more integrated into higher education as an additional
educational tool. Therefore, conducting longitudinal research at regular intervals is
recommended to track changes in attitudes and technology usage in higher education.
Conclusion, Implications, and Recommendations
Throughout history, the emergence of new technologies has consistently elicited both
enthusiasm and resistance, from various, and sometimes the same, individuals. The latter seems
to especially apply to the emergence of language models like ChatGPT, which many perceive
as the greatest technological breakthrough since the advent of the internet.
Kelly, Sullivan, and McLaughlan (2023) examined news articles about the influence of
ChatGPT on higher education and highlighted that the media focus was on academic integrity
issues more frequently than educational opportunities. As the authors suggested, reading about
ChatGPT mainly as a new tool for better cheating, more often than a possibility for better
learning, can influence students’ attitudes and behavior. A similar hype occurred in Croatian
media. However, our findings suggest, most Croatian students still do not regularly use
ChatGPT to complete their homework assignments, although, by the time of publication of thispaper, we certainly expect an increase in its usage.

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Currently, among regular users there are more male than female students, and more scienceand technology students compared to social sciences students. On average, students cite fourmain reasons for using ChatGPT in completing their homework assignments, among which thepredominant ones are finding content that interests them, deeper analysis of a subject matter,and, in third place (with almost 50% frequency of selection), for writing textual assignments(essays, seminars, reviews, etc.). The latter can be considered unethical usage, especially if it
involves uncritical copy-pasting. However, there is some hope in the findings, on the level of
acceptability of using this tool in homework assignments, with the majority considering it to
be unacceptable to literally copy more than half content (with gender differences once again
noted in the same direction). Hopefully students mostly perceive it as an auxiliary tool, as itshould be, and not a complete replacement for their own effort.
Our research shows that professors are almost powerless against such content, that is, theycannot accurately assess the authorship of the work – with the average accuracy rate of 53%.
Even the fact that a particular professor teaches the course he/she is evaluating is not aguarantee of accuracy.
A mitigating circumstance for professors is that there will always be a proportion of easily
detectable students, who are investing minimal effort even into plagiarism, delivering papers
they did not edit or even read. Unfortunately, even they can easily be taught how to create
highly convincing papers, just by slightly adjusting the prompt (e.g. “write a paper as if you
were an average student”).
A major institutional challenge here will be managing the damage caused by all of those who
consider plagiarism evidence of their resourcefulness, see no ethical problem in it, and evenboast about it. When this becomes a common occurrence in a system, conscientious individuals
perceive “distributive organizational injustice”. This becomes demotivating and leads tofeelings of injustice, frustration, and loss of trust in the assessment system. Furthermore, it
undermines fundamental principles of education such as honesty, integrity, and effort.
Professors play a significant role in how they will handle this relatively new situation – whether
to deny the existence of a freely available version of the program that can produce work inseconds, which would otherwise take students days, or to clearly communicate to students the"elephant in the room" and institutionally formalize procedures for this.
The fact that students are mostly unsure of what professors know and can do regarding AI
plagiarism detection (as seen in our data) leaves room for educational institutions to manipulatefear, at least until adequate software is developed that can more accurately detect plagiarism inAI-generated papers. Of course, a more ethical and sustainable solution in the long run is tofocus on education and nurture, for example, the development of internalized beliefs that such
behavior is inappropriate and unsustainable. Fostering a culture of integrity and respect for
academic rules should be encouraged. Educational institutions need to have clear policies andprocedures for detecting and penalizing academic dishonesty to ensure fair and equal

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assessment of all students. This includes the necessity for clear guidelines, plagiarism detection
tools, and educational initiatives to promote the ethical use of technology, specifically AI tools.
Acknowledgement
We would like to thank all the participants, especially the professors of VERN’ University whoparticipated in the workshop, and the students of the same institution, whose constructive
contributions helped to compose the questionnaire used in the research. Acknowledgement
should also be given to ChatGPT, which was used for creating materials for professors’ survey,
and for proofreading the text to improve its grammatical and linguistic accuracy.

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References
Abdaljaleel M., Barakat, M., Alsanafi, M. et al. (2024). A multinational study on the factors
influencing university students’ attitudes and usage of ChatGPT. Scientific Reports
14(1), 1983. https://doi.org/10.1038/s41598-024-52549-8
Abdullah, F., and Ward, R. (2016). Developing a general extended technology acceptancemodel for E-learning (GETAMEL) by analysing commonly used external factors.
Computers in Human Behavior 56, 238–256.
https://doi.org/10.1016/j.chb.2015.11.036
Ajzen, I. (1991). The theory of planned behaviour, Organizational Behaviour and Human
Decision Process, 50 (2), 179–212. https://doi.org/10.1016/0749-5978(91)90020-T
Basic, Z., Banovac, A., Kružić, I. & Jerković, I. (2023). ChatGPT-3.5 as writing assistance instudents’ essays. Humanities and Social Sciences Communications, 10(1).
https://doi.org/10.1057/s41599-023-02269-7
Bouzar, A., El Idrissi, K., Ghourdou, T. (2024) Gender differences in perception and usage of
ChatGPT, International Journal of Humanities and Educational Research 6(2), 571–
582. https://doi.org/10.47832/2757-5403.25.32
Busch, P. A., & Hausvik, G. I. (2023). Too Good to Be True? An Empirical Study of
ChatGPT Capabilities for Academic Writing and Implications for Academic
Misconduct. Paper presented at the Twenty-ninth Americas Conference on
Information Systems, Panama.
Chan, C. K. Y. (2023). A comprehensive AI policy education framework for university
teaching and learning. International Journal of Educational Technology in Higher
Education, 20(1), 1–25. https://doi.org/10.1186/s41239-023-00408-3
Chan, C. K. Y., & Hu, W. (2023). Students’ voices on generative AI: Perceptions, benefits,
and challenges in higher education. International Journal of Educational Technology
in Higher Education, 20(1), 43. https://doi.org/10.1186/s41239-023-00411-8
Chiang, F.-K., Zhu, D., & Yu, W. (2022). A systematic review of academic dishonesty inonline learning environments. Journal of Computer Assisted Learning, 38(4), 907–
928. https://doi.org/10.1111/jcal.12656
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of
information technology. MIS Quarterly, 13(3), 319–340.
https://doi.org/10.2307/249008
DiPietro, M. (2010). 14: Theoretical frameworks for academic dishonesty. To Improve theAcademy, 28(1), 250–262. https://doi.org/10.1002/j.2334-4822.2010.tb00606.x
Draxler, F., Buschek, D., Tavast, M., Hämäläinen, P., Schmidt, A., Kulshrestha, J., Welsch,R., (2023). Gender, Age, and Technology Education Influence the Adoption andAppropriation of LLMs. In Manuscript submitted to ACM. ACM, New York, NY,
USA.

55

IAFOR Journal of Education: Technology in Education

Volume 12 – Issue 2 – 2024

Dwivedi, Y. K., Kshetri, N., Hughes, L., Slade, E. L., Jeyaraj, A., Kar, A. K., Baabdullah, A.
M., Koohang, A., Raghavan, V., Ahuja, M., Albanna, H., Albashrawi, M. A., AlBusaidi, A. S., Balakrishnan, J., Barlette, Y., Basu, S., Bose, I., Brooks, L., Buhalis,D., … Wright, R. (2023). “So what if ChatGPT wrote it?” Multidisciplinary
perspectives on opportunities, challenges and implications of generative
conversational AI for research, practice and policy. International Journal of
Information Management, 71. https://doi.org/10.1016/j.ijinfomgt.2023.102642
Farhi, F., Jeljeli, R., Aburezeq, I., Dweikat, F. F., Al-shami, S. A., & Slamene, R. (2023).
Analyzing the students’ views, concerns, and perceived ethics about ChatGPT usage.
Computers and Education: Artificial Intelligence, 5, 100180.
https://doi.org/10.1016/j.caeai.2023.100180
Fleckenstein, J., Meyer, J., Jansen, T., Keller, S., Köller, O. & Möller, J. (2024). Do teachers
spot AI? Evaluating the detectability of AI-generated texts among student essays.
Computers and Education: Artificial Intelligence, 6.
https://doi.org/10.1016/j.caeai.2024.100209
Granić, A., & Marangunić, N. (2019). Technology acceptance model in educational context:
A systematic literature review. British Journal of Educational Technology, 50(5),2572–2593. https://doi.org/10.1111/bjet.12864
Herbold, S., Hautli-Janisz, A., Heuer, U. et al. A large-scale comparison of human-written
versus ChatGPT-generated essays. Scientific Reports 13, 18617.
https://doi.org/10.1038/s41598-023-45644-9
Iqbal, N., Ahmed, H., & Azhar, K. A. (2022). Exploring teachers’ attitudes towards using
ChatGPT. Global Journal for Management and Administrative Sciences 3, 97–111.
https://doi.org/10.46568/gjmas.v3i4.163
Jowarder, M. I. (2023). The Influence of ChatGPT on Social Science Students: Insights
Drawn from Undergraduate Students in the United States. Indonesian Journal of
Innovation and Applied Sciences (IJIAS), 3(2), 194–200.
https://doi.org/10.47540/ijias.v3i2.878
Kasneci, E., Seßler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., …
Kasneci, G. (2023). ChatGPT for good? On opportunities and challenges of large
language models for education. Learning and Individual Differences, 103, Article
102274. https://doi.org/10.1016/j.lindif.2023.102274
Kelly, A., Sullivan, M., & McLaughlan, P. (2023). ChatGPT in higher education:
Considerations for academic integrity and student learning. Journal of Applied
Learning and Teaching, 6(1). https://doi.org/10.37074/jalt.2023.6.1.17
Kiralj, R. (2020). Akademska čestitost studenata – preduvjet gospodarskog razvoja. Radovi
Zavoda za znanstvenoistraživački i umjetnički rad u Bjelovaru, 0(14), 148–182.
https://doi.org/10.21857/m3v76te4ny
Kiryakova, G.; Angelova, N. (2023) ChatGPT—A Challenging Tool for the University
Professors in Their Teaching Practice. Educational Science, 13, 1056.
https://doi.org/10.3390/educsci13101056
Liu, Y., Z. Zhang, W. Zhang, S. Yue, X. Zhao, X. Cheng, Y. Zhang, and H. Hu. 2023.
ArguGPT: Evaluating, Understanding and Identifying Argumentative Essays
Generated by GPT Models. arXiv preprint arXiv:2304.07666; Retrieved fromhttps://arxiv.org/abs/2304.07666.

56

IAFOR Journal of Education: Technology in Education

Volume 12 – Issue 2 – 2024

Lozano, A.& Blanco Fontao, C. (2023). Is the education system prepared for the irruption of
artificial intelligence? A study on the perceptions of students of primary education
degree from a dual perspective: current pupils and future teachers. Educational
Science. 13, 733. https://doi.org/10.3390/educsci13070733
Majstorović, D. (2016). Stavovi studenata korisnika Nacionalne i sveučilišne knjižnice u
Zagrebu o plagiranju i javnoj objavi ocjenskih radova. Vjesnik bibliotekara Hrvatske,
59 (3–4), 131–152.
Memarian, B., & Doleck, T. (2023). ChatGPT in education: Methods, potentials andlimitations. Computers in Human Behavior: Artificial Humans, 1(2), 100022.
https://doi.org/10.1016/j.chbah.2023.100022
Miles, P. J., Campbell, M., & Ruxton, G. D. (2022). Why students cheat and how
understanding this can help reduce the frequency of academic misconduct in higher
education: A literature review. Journal of Undergraduate Neuroscience Education,20(2), A150.
Nguyen, T. C. (2023). University Teachers’ Perceptions of Using ChatGPT in Language
Teaching and Assessment. In Proceedings of the AsiaCALL International
Conference 4, 116–128. https://doi.org/10.54855/paic.2349
Obenza, B., Salvahan, A., Rios, A. N., Solo, A., Alburo, R. A., & Gabila, R. J. (2024).
University Students’ Perception and Use of ChatGPT: Generative Artificial
Intelligence (AI) in Higher Education. International Journal of Human Computing
Studies, 5(12), 5–18.
Pandan, M. V., & Lomibao, L. S. (2022). Integrity of Mathematics Classroom Assessment in
the New Normal. American Journal of Educational Research, 10(5), 282–287.
Rahman, M. S., Sabbir, M. M., Zhang, D. J., Moral, I. H., & Hossain, G. M. S. (2023).
Examining students’ intention to use ChatGPT: Does trust matter? Australasian
Journal of Educational Technology, 39(6), 51–71. https://doi.org/10.14742/ajet.8956
Rasul, T., Nair, S., Kalendra, D., Robin, M., de Oliveira Santini, F., Ladeira, W. J., ... &Heathcote, L. (2023). The role of ChatGPT in higher education: Benefits, challenges,
and future research directions. Journal of Applied Learning and Teaching, 6(1).
https://doi.org/10.37074/jalt.2023.6.1.29
San Jose, A. E. (2022). Academic Integrity of Students during the COVID-19 Pandemic: A
Mixed Method Analysis. European Journal of Education and Pedagogy, 3(4), 97–103.
https://doi.org/10.24018/ejedu.2022.3.4.400
Shaengchart, Y. (2023). A conceptual review of TAM and ChatGPT usage intentions among
higher education students. Advance Knowledge for Executives, 2(3), 1-7.
Scherer, R., Siddiq, F., & Tondeur, J. (2019). The technology acceptance model (TAM): A
meta-analytic structural equation modeling approach to explaining teachers’ adoptionof digital technology in education. Computers & Education, 128, 13–35.
https://doi.org/10.1016/j.compedu.2018.09.009
Siregar, F. H., Hasmayni, B., & Lubis, A. H. (2023). The Analysis of ChatGPT Usage Impact
on Learning Motivation among Scout Students. International Journal of Research andReview, 10(7), 632–638. https://doi.org/10.52403/ijrr.20230774
Sok, S., & Heng, K. (2023). ChatGPT for education and research: A review of benefits andrisks. SSRN 4378735. https://doi.org/10.2139/ssrn.4378735

57

IAFOR Journal of Education: Technology in Education

Volume 12 – Issue 2 – 2024

Sozon M, Mohammad Alkharabsheh O. H., Fong P. W. & Chuan S. B .(2024). Cheating andplagiarism in higher education institutions (HEIs): A literature review [awaiting peer
review]. F1000Research, 13, 788. https://doi.org/10.12688/f1000research.147140.1
Sukackė, V. (2019). Towards extending the original technology acceptance model (TAM) fora better understanding of educational technology adoption. SOCIETY.
INTEGRATION. EDUCATION. Proceedings of the International Scientific
Conference, 5, 525–549. https://doi.org/10.17770/sie2019vol5.3798
Susnjak, T. (2022) ChatGPT: The End of Online Exam Integrity? ArXiv Preprint
ArXiv:2212.09292.
von Garrel, J., & Mayer, J. (2023). Artificial Intelligence in studies—use of ChatGPT and AIbased tools among students in Germany. Humanities and Social Sciences
Communications, 10, 1–9. https://doi.org/10.1057/s41599-023-02304-7
Venkatesh, V. and Davis, F. D. (2000) A Theoretical Extension of the Technology
Acceptance Model: Four Longitudinal Field Studies. Management Science, 46, 186–
204. https://doi.org/10.1287/mnsc.46.2.186.11926
Venkatesh, V. and Bala, H. (2008) Technology Acceptance Model 3 and a Research Agenda
on Interventions, Decision Sciences 39 (2), 273–315.
https://doi.org/10.1111/j.1540-5915.2008.00192.x
Waltzer, T., Cox, R. L., & Heyman, G. D. (2023). Testing the ability of teachers and students
to differentiate between essays generated by ChatGPT and high school students.
Human Behavior and Emerging Technologies, 1–9.
https://doi.org/10.1155/2023/1923981
Waltzer, T., & Dahl, A. (2023). Why do students cheat? Perceptions, evaluations, andmotivations. Ethics & Behavior, 33(2), 130–150.
https://doi.org/10.1080/10508422.2022.2026775
Witmer, H., Johansson, J. (2015). Disciplinary action for academic dishonesty: does thestudent’s gender matter? Internation Journal of Educational Integrity, 11, 6.
https://doi.org/10.1007/s40979-015-0006-2
Zhang, Y., Yin H. & Zheng, L (2017): Investigating academic dishonesty among Chinese
undergraduate students: does gender matter? Assessment & Evaluation in Higher
Education. https://doi.org/10.1080/02602938.2017.1411467
Zhao, L., Mao, H., Compton, B. J., Peng, J., Fu, G., Fang, F., Heyman, G. D., & Lee, K.
(2022). Academic dishonesty and its relations to Peer cheating and culture: A metaanalysis of the perceived peer cheating effect. Educational Research Review, 36,100455. https://doi.org/10.1016/j.edurev.2022.100455
Yeadon W, Inyang O, Mizouri A et al. (2023) The death of the short-form physics essay inthe coming AI revolution. Physics Education 58(3), 035027.
https://doi.org/10.1088/1361-6552/acc5cf
Yilmaz, H., Maxutov, S., Baitekov, A., & Balta, N. (2023). Student attitudes towards
chatGPT: A technology acceptance Model survey. International Educational
Review, 1(1), 57–83. https://doi.org/10.58693/ier.114
Corresponding author: Irena Miljkovic Krecar
Email: irena.miljkovic@vern.hr

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IAFOR Journal of Education: Technology in Education

Volume 12 – Issue 2 – 2024

Appendix A
Exploratory Factor Analysis of Attitude Towards ChatGPT Usage in Education Scale
Items
For tasks I don’t have time for, I plan to use ChatGPT to do themfor me.
For tasks that are too difficult for me, I plan to use ChatGPT to dothem for me.
For tasks that I find uninteresting or don’t feel like doing, I plan
to use ChatGPT to do them for me.
It’s pointless to invest hours and days in writing a paper orassignment when ChatGPT can write it in seconds.
With frequent use of ChatGPT in seminar preparation, students
will not be able to develop academic skills such as paraphrasing
text, compiling various sources, or summarizing text.
With frequent use of ChatGPT, students will not be able todevelop digital literacy skills (skills for finding, evaluating, andusing information found on the internet).
By using ChatGPT for homework assignments, we negatively
impact the development of critical thinking skills on theassignment topic.
If students don’t invest time and effort in attempting to solve theirassignments themselves, but instead simply rely on ChatGPT to
do it for them, they won’t be able to learn the material well.
ChatGPT is a reliable source of information for writing mosthomework assignments.
I trust the accuracy of the information provided by ChatGPT.
ChatGPT is easy to use for most student tasks.
It’s easy to get ChatGPT to do what I want it to do.
Using ChatGPT for homework assignments promotes thedevelopment of students’ computer literacy skills.
Using ChatGPT for homework assignments fosters creativity.
Using ChatGPT for homework tasks is fun and interesting.
If ChatGPT is already freely available, I don’t see why I wouldn’t
use it to help me write a paper or solve a task.
Extraction Sums of Squared Loadings

F1

F2

F3

F4

.85
.79
.76
.73
.82

.75

.75

-.33

.73
.81
.75
.75
.71
.80
.77
.65
.54

5.19

2.70

1.35

1.22

% of Variance32.44 16.86 8.42 7.59
Note. Principal Component Analysis, Oblimin with Kaiser Normalization. KMO=.85, Bartlett’s Test of
Sphericity (Χ2=2285.61, p <.001).
Factor 1: Intention, Factor 2: Risks of Use Factor 3: Easy Reliable Use, Factor 4: Usefulness.

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IAFOR Journal of Education: Technology in Education

Volume 12 – Issue 2 – 2024

Appendix B
Exploratory Factor Analysis of Ethical Issues of Chatgpt Usage in Writing Assignments
Items

Factor loading

Using ChatGPT to complete homework assignments is ethically0.85
unacceptable to me.
The use of ChatGPT in writing assignments should be prohibited.
0.86
By using ChatGPT to write homework assignments, we compromise the
0.82
ethical principles of studying.
I don’t see an ethical issue in submitting to the professor a paper that
-0.54
was entirely or mostly written by ChatGPT.
Extraction Sums of Squared Loadings
2.42% of Variance60.39
2Note. Principal Component Analysis. KMO=.75, Bartlett’s Test of Sphericity (Χ =430.82, p <.001).

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