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Managing Academic Integrity in the AI Era: ChatGPT Usage and
Plagiarism among Malaysian Undergraduates
Haslina Binti Hassan Basri
1
*, Aidanazima Binti Abashah
2
, Mohd Sufino Zuhaily bin Mohd Sufian
3
Department of Business, Faculty of Business & Communication, Universiti Malaysia Perlis.Sungai
Chuchuh, 02100 Padang Besar, Perlis, Malaysia
*Corresponding author
DOI: https://dx.doi.org/10.47772/IJRISS.2025.914MG00218
Received: 02 November 2025; Accepted: 10 November 2025; Published: 22 November 2025
ABSTRACT
This study plans investigate the factors influencing plagiarism behaviour among undergraduate from Faculty of
Busines and Communication at Universiti Malaysia Perlis (UniMAP). With the advance of technology students
are prompt to use AI tools like ChatGPT in completing their assignment. By referring of the Extended Theory
of Planned Behaviour (TPB), this study has examined the roles of attitudes, subjective norms, perceived
behavioural control, moral obligations, and past violations in contributing to plagiarism behaviour using
ChatGPT. A survey was distributed with 331 responses. Data was analyzed using Statistical Pack for Social
Science (SPSS). The findings showed that attitudes, perceived behavioural control, moral obligations, and past
violations had an influence of plagiaristic behaviour using AI tools such as ChatGPT However, no significant
effect was found for subjective norms. This confirmed that policies and guidelines to address the challenge of
AI-assisted plagiarism was required to uphold academic integrity in a university setting.
Keyword- Academic Integrity, Artificial Intelligence in Education, ChatGPT, Plagiarism, Extended Theory of
Planned Behaviour (TPB).
INTRODUCTION
ChatGPT was developed by OpenAI and launched in November 2022 to the world. Within 2 months it has
reached 100 million users and as August 2023 it was used by 180.5 users both the public and academic
communities. ChatGPT has assisted users by taking on tasks such as translation, summarization and content
creation. Approximately 4.61 billion users visit ChatGPT website monthly (Duarte, 2025). With its ability to
assist researchers in preparing and writing the content, ChatGPT has raised concerns as to whether it
jeopardize academic integrity. AI-facilitated plagiarism and cheating has increased such as academic integrity
risks as more and more students are using AI to write essays, assignments and articles (Lo, 2023; Alser &
Waisberg, 2023). While some university banned AI tools like ChatGPT, other has taken initiatives to promote
ethical usage of AI in academia (Tlili et al., 2023; Jimenez, 2023).
Debates regarding academic authorship, originality, accountability and proper acknowledgement has influences
institutions and scholars to call for clearer ethical guidelines and policy revisions in order to address the usage
of AI in academic writing and research (Van Dis et al., 2023; Lund et al., 2023; Perkins, 2023). Concerns
regarding plagiarism that was associated with ChatGPT also has become the top related search topics in
Google Trends, as public and scholars become aware about academic dishonesty related to AI facilitated
publication.
There is therefore an immediate need to understand the behavioural factors that drive students to use AI
facilitated tools like ChatGPT in order to prepare for their assignments that may violate academic integrity. By
referring to the Extended Theory of Planned Behaviour (TPB), this study will examine whether attitudes,
subjective norms, perceived behavioural control, moral obligation and past violations can influence plagiarism
behaviour among undergraduate students in the Faculty of Business & Communication (FPK), Universiti
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Malaysia Perlis (UniMAP). The study therefore aims to identify predictors of AI assisted academic dishonesty
and to inform academic institutions policies and interventions in order to promote ethical academic practices in
digital era.
LITERATURE REVIEW
ChatGPT has gained widespread popularity in both general and scientific disciplines in higher education.
Previous studies have ChatGPT can be used to personalize instruction to enhance student involvement.
ChatGPT adaptation by academicians to help with all their tasks, however, should be approached with cautions
as it may affect academic integrity (Rudolph et al., 2023). This created the need to understand and explore the
influence of AI tools in education which was emphasized by past research from Williamson et al. (2023) and
Cotton et al. (2023). Meanwhile Tlili et al. (2023) has stressed the importance of maintaining academic
integrity while using AI tools. At the same time Crawford et al. (2023), Sullivan et al. (2023), and Cooper
(2023) propose for clear ethical guidelines with robust frameworks. Naumowa (2023) study further addressed
the need for critical thinking application when it comes to the use of AI tools.
Extended Theory of Planned Behaviour
Intention to act that predicts actual behaviour generally are shaped by attitudes, subjective norms and
perceived behavioural control (Beck & Ajzen, 1991; Cronan et al., 2018). This theory will be used to explain
about students’ ethical decision making while undertaking the tasks, with extensions to include factors like
moral obligation and past behaviour in order to predict the accuracy of such behaviour. Previous study by
Alleyne and Phillips (2011) has showed that Theory of Planned Behaviour has predicted students’ involvement
in plagiarism behaviour. Palmer et al. (2019) has used the same theory by including two extra predictors that
are peer and institutional influences. This study therefore will adopts an extended TPB model to examine
plagiarism behaviour involving ChatGPT.
Plagiarism Behaviour
Plagiarism can be defined as "copying (or using) others' work that (accidentally or otherwise)” which resulting
the third party being deceived about the authorship or ownership of the work (Yeo, 2007). With the
advancement of AI tools like ChatGPT, students’ method to commit plagiarism was made easy and convenient.
The original contents are easily modified and shared without the consent of the original author. AI tools has
made it easier to users to plagiarize online materials (Stephens, Young, & Calabrese, 2007). ChatGPT used by
students can easily copy and paste online materials into their assignments, reports or projects without proper
credits (Balbay & Kilis, 2019). Duplication or online paraphrasing tools are also used by students to commit
such plagiarism behaviour (Elkhatat et al., 2021). ChatGPT that is capable to use natural language processing
in order to generate text therefore has raised concerns about academic dishonesty (Foltýnek et al., 2020).
ChatGPT has also been criticized for generating inaccurate, fake and outdated content. Elkhatat (2022) and
Foltýnek et al. (2020) believe that academic institutions must implement effective strategies to hinder
dishonest academic practices in order to preserve authentic learning.
Attitude Towards Plagiarism
Attitude is defined as "the degree to which a person has a favorable or unfavorable evaluation or appraisal of
the behaviour in question" (Ajzen, 1991). Attitudes play an important role in making ethical decision as
individuals can evaluate ethicality of an action based on their personal belief and values (Namlu & Odabasi,
2007). Previous study such as Bolin (2004) found that attitude, self-control, and perceived opportunity have
approximately 40% contributed to academic dishonesty. Hosny and Fatima (2014) had explained that
although plagiarism is known to be unethical, students still commit it. Various psychological factors are said to
influence students’ attitudes toward plagiarism but scholars didn’t agree on their exact impact to their learning
(Moss et al., 2018). However, students with bigger attention to commit plagiarism have more favourable
attitudes toward such behaviour. Motivation can play an important role as if such plagiarism behaviour is
accepted among peer, such students will accept it as normal behaviour (Camara et al., 2017).
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Subjective Norms
Subjective norms refer to a person's "perceived social pressure to perform or not to perform the behaviour"
(Ajzen, 1991). Fattahi Ardakani et al. (2020) explained that subjective norms occur when on individua adopts
specific behaviour due to social pressure. Therefore, peer influences play an important role in shaping student
ethical behaviour. This conclusion was supported by McCabe and Trevino (1997), that emphasized that peer
influences were among the most powerful influences to commit academic dishonesty. Engler, Landau and
Epstein (2008) further explained peer relationship had created temptations to cheat as unethical behaviour is
normalized within the group. Only when such unethical behaviour was disapproved by peer will deter students
from committing such conduct.
Recently, researchers began exploring the role of subjective norms in relation to AI usage. For example, Gundu
(2023), investigated the relationship of subjective norms and information security behaviour involving
ChatGPT. Similarly, Jo (2023) also researched the ethical implication of AI tools within an academic setting.
Ofosu-Ampong et. Al. (2023) also explored the impacts of social influence in using ChatGPT in education.
Perceived Behavioural Control Towards Plagiarism
Ajzen (1991) defined perceived behavioural control (PBC) as "the perceived ease or difficulty of performing
the behaviour." According to him, people tend to have greater control over their actions when they believe they
have sufficient resources, opportunities, and minimal obstacles. Research has identified PBC as one of the
significant factors influencing cheating behaviour (Camara et al., 2017; Cronan et al., 2018).
The introduction of AI tools like ChatGPT has provided students with powerful resources and assistance to
produce content. They can easily submit their assignments and scholarly publications even without making
significant contributions to the work (Oravec, 2023). The latest AI systems can complete assignments by
generating text dynamically, taking into account the context and tone (Floridi, 2023). This technological
capability gives students a heightened sense of control and efficiency in academic tasks. Such advantages have
raised concerns regarding ethics, particularly the authenticity and integrity of AI-assisted work (Jimenez,
2023). Obviously, the ease of using tools like ChatGPT can significantly influence students’ perceived ability
to cheat.
Moral Obligations
Moral obligation is one of the variables proposed to extend the Theory of Planned Behaviour (TPB) model in
order to improve its predictive power. Moral obligation is defined by Beck and Ajzen (1991) as a personal
moral responsibility to perform or refuse to perform certain behaviours. This definition explains an individual’s
personal moral standard of duty or guilt regarding the implications of their actions, which is relevant in the
context of academic dishonesty, such as cheating and plagiarism, since moral considerations have the tendency
to influence an individual’s decision-making process (Cronan et al., 2018).
Studies have supported that moral obligation should be included in TPB, since it could enhance the model’s
ability to predict an individual’s ethical intention (Beck & Ajzen, 1991; Cronan et al., 2018). In the research by
Armitage and Conner (1999) they suggested that moral norms should be used in relation to other TPB
components. The study by Passow et al. (2006) found that students who viewed cheating as immoral were less
likely to engage in it, regardless of the situation. Similarly, students who considered immoral actions were less
likely to perform them, which showed the significance of moral obligation in shaping ethics (Stephens, 2018).
Past Violations
Another predictor that supplements the Theory of Planned Behaviour (TPB) is past violation (Armitage &
Conner, 1999; Bagozzi & Kimmel, 1995). Ajzen (2002) explained that habitual actions might clarify how past
behaviour influences future conduct regardless of intention. However, he also argued that the frequency of past
behaviour does not necessarily offer insight into behavioural determinants. Nevertheless, several studies have
found a strong relationship between prior and future misconduct in academic writing, such as cheating (Passow
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et al., 2006). Cronan et al. (2018) found past behaviour to be the second strongest predictor of plagiarism
intention, following attitude.
Sullivan et al. (2023) had reviewed 100 studies relating to ChatGPT’s impact on academic integrity. They
found that cheating using ChatGPT in essay writing could even pass a medical licensing exam. Another
research, conducted by Intelligent (2023) also found that 30% of the students in their study were using
ChatGPT for written homework, while 60% of them had used it for more than half of their assignments with
5% of students using it for every assignment in an academic setting. This happened despite 75% of them
viewed using ChatGPT was considered cheating indicating that most students in the study used ChatGPT as a
supplementary tool instead of a substitute, out of the fear of being caught cheating.
Figure 1: Research Framework
Figure 1 shows the factors influencing plagiarism behaviour using ChatGPT framework. This framework
serves as an extension of Azjen’s Theory of Planned Behaviour (TPB) by including moral obligation, and past
violations into the mix. This is important as it would be appropriate for academic integrity research, since
influence on behaviour encompasses attitude, norms, control, morality and prior action. Therefore, based on
the research framework, five hypotheses were developed to examine the factors influencing plagiarism
behaviour related to the use of ChatGPT among university students. The hypotheses are as below: -
H₁: Students’ positive attitude toward using ChatGPT to cheat will positively influence plagiarism behaviour.
H₂: Students’ stronger subjective norms of using ChatGPT to cheat will positively influence plagiarism
behaviour.
H₃: Students’ stronger perceived behavioural control of using ChatGPT to cheat will positively influence
plagiarism behaviour.
H₄: Students’ stronger moral obligation regarding the use of ChatGPT to cheat will negatively influence
plagiarism behaviour.
H₅: Students with past violations involving the use of ChatGPT to cheat will positively influence plagiarism
behaviour.
The purpose of these hypotheses is to explore the psychological and behavioural factors that lead to student’s
misappropriate use of AI tools (ChatGPT) within an academic setting.
RESEARCH METHODOLOGY
This study used a quantitative approach by measuring the outcomes based on an online questionnaire (via
Google form) which consisted of 45 items that were related to the dependent and independent variables. Data
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collected used a 5-point Likert scale which measures from 1 (strongly disagree) to 5 (strongly agree) and
presented through tables, charts and graphs for the end analysis. The questionnaire used were in English based
which consisted of three sections. Section A collects respondents' demographic information and ChatGPT
usage experience, 34 questions in Section B examine factors influencing plagiarism behaviour using ChatGPT,
focusing on variables such as cheating attitude, subjective norms, perceived behavioural control, moral
obligation, and past violations. Lastly, 6 questions in Section C investigate students' plagiarism behaviour as
the dependent variable. The research targets are undergraduate students from the Faculty of Business &
Communication (FPK), UniMAP, as the sampling frame. These students are selected due to their awareness
and exposure to ChatGPT usage, while completing the assignments that are multidisciplinary in nature. The
pilot test was conducted on 30 students to establish the clarity and reliability of the instruments used. The
reliability of measure shows high internal consistency of the data collected. By referring to Table 1, the results
are as follows: -
Table 1 Cronbach Alpha Test
Construct
Cronbach Alpha
Plagiarism Behaviour
0.917
Attitudes Towards Plagiarism
0.884
Subjective Norms
0.904
Perceived Behavioural Control Towards Plagiarism
0.954
Moral Obligations
0.857
Past Violations
0.881
The construct ‘Perceived Behavioural Control Towards Plagiarism’, Plagiarism Behaviour and Subjective
Norms recorded the alpha value (α = 0.954), (α = 0.917) and (α = 0.904) demonstrates excellent reliability.
While Cheating Attitudes (α = 0.842), Subjective Norms (α = 0.899), and Moral Obligations (α = 0.825) also
show good to high reliability. These results suggest that the measurement scales are reliable and suitable for
further analysis.
RESULT AND DISCUSSION
Table 2 Demographic Characteristics of Respondents
Variable
Categories
Frequency
Percentage (%)
Gender
Male
111
33.5
Female
220
66.5
Race
Malay
121
36.6
Chinese
111
33.5
Indian
87
26.3
Others
12
3.6
Bachelor of Business (International
Business) with Honours
141
42.6
Bachelor of New Media
83
25.1
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Communication with Honours
Year of Study
Year 1
Year 2
Year 3
63
98
170
19.0
29.6
51.4
Do you know
about ChatGPT?
Yes
No
331
0
100.0
0
Do you have
experience using
ChatGPT in your
assignment?
Yes
No
331
0
100.0
0
The data for this study were collected through Google Forms and the link to the online survey was distributed
to undergraduate students across FPK within the university. Purposed of the study was briefed in the header
part of the form. Voluntarily out of the 2822 FPK students, a total of 331 students participated in the study.
This meant sample size met the minimum sample size based of the Krejcie and Morgan (1970) table with a
95% confidence level and a 5% margin of error. Results as in Table 2, shows that 220 (66.5%) of the
respondents were female and 111 (33.5%) were male. The participants were split into three main programs
offered which was International Business (42.6%), Engineering Entrepreneurship (32.3%), and New Media
Communication (25.1%). Most students are in Year 3 (51.4%), followed by Year 2 (29.6%) and Year 1
(19.0%). All respondents (100%) are aware of and have used ChatGPT for assignments. Their answers to the
question are tested using the Social Sciences Statistical Package (SPSS) program.
Descriptive Statistics Results
Table 3 Descriptive Analysis
Factor
Mean
Standard Deviation
Plagiarism Behaviour
3.6520
0.76335
Cheating Attitudes using ChatGPT
3.7432
0.55898
Subjective norms of cheating using ChatGPT
3.7966
0.58567
Perceived Behavioural Control of Cheating Using ChatGPT
3.8586
0.59945
Moral Obligation
3.7437
0.56275
Past Violations
3.7266
0.78142
An analysis of the central tendencies indicated that the mean scores between all the factors were clustered
closely to each other due to the narrow range from 3.65 to 3.86 as shown in Table 3. This suggested there a
slight disagreement between the students across the measures used. Factor with the highest reported mean
score was Perceived Behavioural Control of Cheating Using ChatGPT (M = 3.8586, SD = 0.549945),
suggested that students on average felt a high degree of control of their use of ChatGPT for cheating. The
lowest observed mean scores was for Plagiarism Behaviour (M = 3.6521, SD = 0.76335), The means of
Cheating Attitude (M = 3.7432, SD =.055898), Subjective Norms (M = 3.7966, SD = 0.58567), Moral
Obligation (M =3.7437, SD =
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0.56275) and Past Violations (M = 3.7266, SD = 0.78142) falls between these two means.
On variability, the standard deviation revealed that Cheating Attitudes using ChatGPT (SD = 0.55898) had the
lowest standard deviation which indicated the high degree of homogeneity response in the students. The
greatest variability of responses was from two measures which Past Violations (SD = 0.78142) and Plagiarism
Behaviour (SD = 0.76335) which suggested that self-reported engagement of these behaviours varied
significantly across the student sample. In conclusion, the descriptive analysis serves as the basis for the next
analysis which is the Pearson’s Correlation Coefficient analysis.
Table 4 Pearson’s Correlation Coefficient Analysis
ATT
SN
PBC
MO
PV
PB
ATT
Pearson Correlation
Sig. (2-tailed)
N
1
331
.724**
< .001
331
.665**
< .001
331
.768**
< .001
331
.692**
< .001
331
.715**
< .001
331
SN
Pearson Correlation
Sig. (2-tailed)
N
.724**
< .001
331
1
331
.573**
< .001
331
.699**
< .001
331
.533**
< .001
331
.495**
< .001
331
PBC
Pearson Correlation
Sig. (2-tailed)
N
.665**
< .001
331
.573**
< .001
331
1
331
.649**
< .001
331
.622**
< .001
331
.606**
< .001
331
MO
Pearson Correlation
Sig. (2-tailed)
N
.768**
< .001
331
.699**
< .001
331
.649**
< .001
331
1
331
.674**
< .001
331
.659**
< .001
331
PV
Pearson Correlation
Sig. (2-tailed)
N
.692**
< .001
331
.533**
< .001
331
.622**
< .001
331
.674**
< .001
331
1
331
.821**
< .001
331
PB
Pearson Correlation
Sig. (2-tailed)
N
.715**
< .001
331
.495**
< .001
331
.606**
< .001
331
.659**
< .001
331
.821**
< .001
331
1
331
Table 4 shows the Pearson’s Correlation Coefficient analysis which indicated that all the variables were
positively and significantly correlated p < 0.001 level.Plagiarism Behaviour (PB) demonstrated a strong
positive relationship with all the other factors. The strongest correlation observed was with Past Violations
(PV) (r =0.821 p <0.001) which indicated that past violation was strong predictor to future plagiarism
behaviour.
Moreover, PB also demonstrated a strong positive correlation with Cheating Attitudes using ChatGPT (ATT)
(r=.715, p<.001), Moral Obligation (MO) (r=.659, p<.001), and Perceived Behavioural Control of Cheating
(PBC) (r=.606, p<.001). This suggested that students that engaged in plagiarism, were likely to hold favorable
attitude towards using ChatGPT, since they perceived that using ChatGPT was within their control and more
likely had lower morality stance in using I (Moral Obligations were reversed coded). Subjective Norm (SN) (r
=0.495, p = 0.001) was found to have a moderate to strong significant relationship with PB suggesting that
perceived social pressure had a weaker influence than the student’s own attitudes, perceive control and past
actions.
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Significant inter-correlation was also noted amongst the predictor variables. The strongest between these
predictors were ATT and MO (r = 0.768, p = 0.001) that created an overlap between what the students believe
as acceptable and what they believe was morally right. Strong relationships were also found between ATT and
SN (r = 0.742, p <0.001) and MO and SN (r = 0.669, p < 0.001) which suggested consistency in the student’s
attitudinal and normative construct.
Table 5 Model Summary
Model
R
R Square
Adjusted R Square
Std. Error of the Estimate
Durbin-
Watson
1
0.851
a
0.724
0.720
0.40386
1.995
Table 6 ANOVA
Model
Sum of
Squares
df
Mean Square
F
Sig.
Regression
139.282
5
27.856
170.790
< 0.001
b
1
Residual
53.009
325
0.163
Total
192.291
330
Table 7 Coefficients
Variable
Beta
t-Ratio
Significance
(Constant)
-1.224
< 0.001
Cheating Attitudes using
ChatGPT
0.619
10.265
< 0.001
Subjective Norms of cheating using
ChatGPT
-0.095
-1.739
0.083
Perceived Behavioural Control of cheating
using ChatGPT
0.249
4.916
< 0.001
Moral Obligation
0.193
4.677
< 0.001
Past Violations
0.691
16.721
< 0.001
The model summary as shown in Table 5, indicated that the model had a strong fit for the data. The multiple
correlation coefficient was high (R=.851), and the model were able to explain 72.4% of the variance in
Plagiarism Behaviour (𝑹
𝟐
=.724). The adjusted R2 value suggested (Adjusted 𝑹
𝟐
=.720) confirmed the model's
robustness in explanatory power. The Durbin-Watson statistics of 1.995 indicated that the assumptions errors
were met. The ANOVA results as shown in Table 6, further confirms the statistically significant of the model
with F (5, 325) = 170.790, p < 0.001. This indicated that the predictors combined were able to predict the
dependent variable.
By referring to Table 7, past violations appeared as the most significant predictor to Plagiarism Behaviour, with
largest standard coefficient (β = 0.691, t = 16.721, p < 0.001). Cheating Attitudes using ChatGPT, also
demonstrated a strong, significant positive relationship with the dependent variable (β = 0.619, t = 10.265, p <
0.001). Perceived Behavioural Control of Cheating using Chat GPT (β = 0.249, t = 4.916, p < 0.001) and
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Moral Obligations (β = 0.193, t = 4.677, p < 0.001) were also found to be significant positive predictors to the
dependent variable with less influence than past behaviours and attitudes.
Meanwhile, Subjective Norms of Cheating using ChatGPT (β = -0.095, t = -1.739, p < 0.083) was not
statistically significant predictor to this model. This suggested that when controlling personal attitudes,
perceived control, morality, and past behaviour was stronger in predicting plagiarism behaviours, while social
pressures from others did not contribute to explanation of plagiarism.
Therefore, the regression coefficient provided a direct test to the hypotheses that were presented in this study.
H5 was strongly supported since Past Violation (β = 0.691, t = 16.721, p < 0.001) was presented as the most
powerful predictor which meant that past behaviour as the most dominant factor of future behaviour, which
surpasses all attitudinal and normative factors in the model. H1 was also supported with Cheating Attitudes
using ChatGPT since it showed a strong and significant positive influence on plagiarism (β = 0.619, t = 10.265,
p < 0.001), which showed that positive attitudes towards cheating predict plagiarism, even when taking
account for social norms, controls, morality, and past behaviours. H3 was supported, as Perceived Control of
Cheating using
ChatGPT was also a significant predictor (β = 0.249, t = 4.916, p < 0.001), where it showed that student
confidence in their ability to use ChatGPT for cheating increases the significance of them conducting
plagiarism. In addition, H4 was also supported as Moral Obligation was a significant predictor (β = 0.193, t =
4.677, p < 0.001). This showed students with higher levels of morality were less likely to participate in
plagiarism using AI tools such as ChatGPT
CONCLUSION
In conclusion this highlighted the efficacy of TPB in explaining plagiaristic behaviour amongst students in
FPK, in the context of using AI Tools such as ChatGPT. The key findings of this research had demonstrated
that attitude towards cheating, perceived behavioural control, moral obligation, and past behaviour had a
significant impact on plagiaristic behaviour amongst students. Prominently, lackadaisical attitudes towards the
implications of abusing AI tools increased the likelihood of behavioural misconduct, while morality functioned
as deterrent. On the other hand, subjective norms had no significant interaction with plagiaristic intent. This
showcased the need for interventions to mitigate risk of using AI tools, shift student’s attitudes, enhance
integrity, and foster ethical code of conduct. This could be achieved by training students in improving citations,
information literacy, and maintaining ethical integrity. This meant that early intervention should be a campus
wide strategy rather than emphasizing only punitive measures. This is to ensure a culture of honesty and ethical
integrity could be maintained in an academic setting. Social desirability biased may have influenced the overall
results, however, consistency of the findings with previous research validates this research. Therefore, future
research should be focused on expanding the research to include other academic disciplines, institutions, and
diverse demographics to improve the overall findings. Other than that, more efforts are needed in the study of
plagiaristic behaviour, and a specific discipline approach should be developed to further improve academic
integrity in a digital era.
REFERENCES
1. Ajzen, I. (1991). The theory of planned behaviour. Organizational Behaviour and Human Decision
Processes, 50(2), 179–211.
2. Ajzen, I. (2002). Perceived behavioural control, self-efficacy, locus of control, and the theory of planned
behaviour. Journal of Applied Social Psychology, Vol. 32, 665-683.
3. Alleyne, P., & Phillips, K. (2011). Exploring academic dishonesty among university students in
Barbados: An extension to the theory of planned behaviour. Journal of Academic Ethics, 9, 323-338
4. Alser. M & Waisberg. E. (2023). Concerns with the usage of ChatGPT in academia and medicine: a
viewpoint. American Journal of Medicine Open, 9 (2023)
5. Armitage, C. J., & Conner, M. (1999). The theory of planned behaviour: Assessment of predictive
validity and perceived control. British Journal of Social Psychology, 38(1), 35–54.
Page 2838
www.rsisinternational.org
INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)
ISSN No. 2454-6186 | DOI: 10.47772/IJRISS | Volume IX Issue XIV October 2025 | Special Issue on Management
6. Beck, L., & Ajzen, I. (1991). Predicting dishonest actions using the theory of planned behaviour. Journal
of Research in Personality, 25(3), 285–301.
7. Bagozzi, R. P., & Kimmel, S. K. (1995). A comparison of leading theories for the prediction of
goaldirected behaviours. British Journal of Social Psychology, 34(4), 437–461.
8. Balbay. S, Kilis. S, (2019). Perceived Effectiveness of Turnitin® in Detecting Plagiarism in Presentation
Slides. Contemporary Educational Technology 10(10: 25-36.
9. Bolin, A. U. (2004). Self-Control, Perceived Opportunity, and Attitudes as Predictors of Academic
Dishonesty. The Journal of Psychology, 138(2), 101–114.
10. Camara, S. K., Eng-Ziskin, S., Wimberley, L., Dabbour, K. S., & Lee, C. M. (2017). Predicting students’
intention to plagiarize: An ethical theoretical framework. Journal of Academic Ethics, 15(1), 43–58.
11. Cooper, G. (2023). Examining science education in ChatGPT: An exploratory study of generative
artificial intelligence. Journal of Science Education and Technology, 32(3), 444–452.
12. Cotton, D. R. E., Cotton, P. A., & Shipway, J. R. (2023). Chatting and cheating: Ensuring academic
integrity in the era of ChatGPT. Innovations in Education and Teaching International, 1–12.
13. Crawford, J., Cowling, M., & Allen, K.-A. (2023). Leadership is needed for ethical ChatGPT: Character,
assessment, and learning using artificial intelligence (AI). Journal of University Teaching and Learning
Practice, 20(3).
14. Cronan, T. P., Mullins, J. K., & Douglas, D. E. (2018). Further understanding factors that explain
freshman business students' academic integrity intention and behaviour: Plagiarism and sharing
homework. Journal of Business Ethics, 147(1), 197–220.
15. Duarte. F. (2025). Number of ChatGPT users. Exploding Topics. Retrieved from:
https://explodingtopics.com/blog/chatgpt-users
16. Elkhatat, A. M. (2022). Practical randomly selected question exam design to address replicated and
sequential questions in online examinations. International Journal for Educational Integrity, 18(1).
17. Elkhatat, A. M., Elsaid, K., & Almeer, S. (2021). Some students’ plagiarism tricks, and tips for effective
check. International Journal for Educational Integrity, 17(1).
18. Engler, J. N., Landau, J. D., & Epstein, M. (2008). Keeping up with the Joneses: Students' perceptions
of academically dishonest behaviour. Teaching of Psychology, 35(2), 99–102
19. Fattahi Ardakani, M., Salehi-Abargouei, A., Sotoudeh, A., Esmaeildokht, S., & Bahrevar, V. (2020). Do
subjective norms predict the screening of cancer patients’ first-degree relatives? A systematic review and
meta-analysis. Asian Pacific Journal of Cancer Prevention, 21(6), 1521–1530.
20. Floridi, L. (2023). AI as agency without intelligence: On ChatGPT, large language models, and other
generative models. Philosophy & Technology, 36(1).
21. Foltýnek T, Meuschke N, Gipp B (2020) Academic Plagiarism Detection. ACM Comput Surv 52(6):1–
42.
22. Gundu, T. (2023). Chatbots: A framework for improving information security behaviours using
ChatGPT. In S. Furnell & N. Clarke (Eds.), Human Aspects of Information Security and Assurance (pp.
418-431). Springer Nature.
23. Hosny, M., & Fatima, S. (2014). Attitude of students towards cheating and plagiarism: University case
study. Journal of Applied Sciences, 14(8), 748–757
24. Intelligent. (2023). Nearly 1/3 college students have used ChatGPT on written assessments. Intelligent.
Retrieved from: https://www.intelligent.com/nearly-1-in-3-college-students-have-used-chatgpt-
onwritten-assignments
25. Jimenez, K. (2023). Professors are using ChatGPT detector tools to accuse students of cheating. But
what if the software is wrong? USA Today. Retrieved from:
https://www.usatoday.com/story/news/education/2023/04/12/how-ai-detection-tool-spawned-
falsecheating-case-uc-davis/11600777002/
26. Jo, H. (2023). Decoding the ChatGPT mystery: A comprehensive exploration of factors driving AI
language model adoption. Information Development. Retrieved from:
https://doi.org/10.1177/02666669231202764
27. Krejcie, R. V., & Morgan, D. W. (1970). Determining sample size for research activities. Educational
and Psychological Measurement, 30(3), 607-610.Retrievedfrom:
https://doi.org/10.1177/001316447003000308
Page 2839
www.rsisinternational.org
INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)
ISSN No. 2454-6186 | DOI: 10.47772/IJRISS | Volume IX Issue XIV October 2025 | Special Issue on Management
28. Lo. C.K. (2023). What is the impact of ChatGPT on education? A rapid review of the literature. Educ.
Sci., 13 (4), p. 410.
29. Lund, B. D., & Wang, T. (2023). Chatting about ChatGPT: How may AI and GPT impact academia and
libraries? Library Hi Tech News, 40(3), 26–29.
30. Lund, B. D., Wang, T., Mannuru, N. R., Nie, B., Shimray, S., & Wang, Z. (2023). ChatGPT and a new
academic reality: Artificial Intelligence-written research papers and the ethics of the large language
models in scholarly publishing. Journal of the Association for Information Science and Technology,
74(5), 570–581.
31. McCabe, D. L., & Trevino, L. K. (1997). Individual and contextual influences on academic dishonesty:
A multicampus investigation. Research in Higher Education, 38(3), 379–396.
32. Moss, S. A., White, B., & Lee, J. (2018). A systematic review into the psychological causes and
correlates of plagiarism. Ethics & Behaviour, 28(4), 261–283.
33. Namlu, A. G., & Odabasi, H. F. (2007). Unethical computer using behaviour scale: A study of reliability
and validity on Turkish university students. Computers & Education, 48, 205–215.
34. Naumova, E. N. (2023). A mistake-find exercise: a teacher’s tool to engage with information
innovations,
35. ChatGPT, and their analogs. Journal of Public Health Policy, 44,173-178.
https://doi.org/10.1057/s41271-023-00400-1
36. Ofosu-Ampong, K., Acheampong, B., Kevor, M. O, & Amankwah-Sarfo, F. (2023). Acceptance of
Artificial Intelligence (ChatGPT) in education: Trust, innovativeness and psychological need of
students. Information and Knowledge Management, 13(4), 37-47.
37. Oravec, J. (2023). Artificial Intelligence Implications for Academic Cheating: Expanding the
Dimensions of Responsible Human-AI Collaboration with ChatGPT and Bard. Jl. Of Interactive
Learning Research, 34(2), 213–237.
38. Palmer, A., Pegrum, M., & Oakley, G. (2019). A wake-up call? Issues with plagiarism in transnational
higher education. Ethics & Behaviour, 29(1), 23–50.
39. Passow, H. J., Mayhew, M. J., Finelli, C. J., Harding, T. S., & Carpenter, D. D. (2006). Factors
influencing engineering students' decisions to cheat by type of assessment. Research in Higher
Education, 47(6), 643– 684.
40. Perkins, M. (2023). Academic integrity considerations of AI large language models in the post-pandemic
era: ChatGPT and beyond. Journal of University Teaching and Learning Practice, 20(2).
41. Rudolph, J., Tan, S., & Tan, S. (2023). ChatGPT: Bullshit spewer or the end of traditional assessments in
higher education? Journal of Applied Learning & Teaching, 6(1), 1–22.
42. Stephens, J. M. (2018). Bridging the divide: The role of motivation and self-regulation in explaining the
judgment-action gap related to academic dishonesty. Frontiers in Psychology, 9(246), 1–15.
43. Stephens, Jason M., Young, Michael F., and Calabrese, Thjomos. (2007). Does Moral Judgement Go
Offline When Students Are Online? A Comparative Analysis of Undergraduates’ Beliefs and Behaviours
Related to Conventional and Digital Cheating. Retrievedfrom:
https://doi.org/10.1080/10508420701519197\
44. Sullivan, M., Kelly, A., & McLaughlan, P. (2023). ChatGPT in higher education: Considerations for
academic integrity and student learning. Journal of Applied Learning & Teaching, 6(1), 1–10.
45. Tlili, A., Shehata, B., Adarkwah, M. A., Bozkurt, A., Hickey, D. T., Huang, R., & Agyemang, B.
46. (2023). What if the devil is my guardian angel: ChatGPT as a case study of using chatbots in education?
Smart Learning Environments, 10(1), 15.
47. Van Dis, E. A. M., Bollen, J., Zuidema, W., van Rooij, R., & Bockting, C. L. (2023). ChatGPT: Five
priorities for research. Nature, 614(7947), 224–226.
48. Williamson, B., Macgilchrist, F., & Potter, J. (2023). Re-examining AI, automation and datafication in
education. Learning, Media and Technology, 48(1), 1–5.
49. Yeo, S. (2007). First‐year university science and engineering students’ understanding of plagiarism.
50. Higher Education Research & Development, 26(2), 199–216.
