Is it harmful or helpful? Examining the causes and consequences of generative AI usage among university students

Abstract

While the discussion on generative artificial intelligence, such as ChatGPT, is making waves in academia and the popular press, there is a need for more insight into the use of ChatGPT among students and the potential harmful or beneficial consequences associated with its usage. Using samples from two studies, the current research examined the causes and consequences of ChatGPT usage among university students. Study 1 developed and validated an eight-item scale to measure ChatGPT usage by conducting a survey among university students (N = 165). Study 2 used a three-wave time-lagged design to collect data from university students (N = 494) to further validate the scale and test the study’s hypotheses. Study 2 also examined the effects of academic workload, academic time pressure, sensitivity to rewards, and sensitivity to quality on ChatGPT usage. Study 2 further examined the effects of ChatGPT usage on students’ levels of procrastination, memory loss, and academic performance. Study 1 provided evidence for the validity and reliability of the ChatGPT usage scale. Furthermore, study 2 revealed that when students faced higher academic workload and time pressure, they were more likely to use ChatGPT. In contrast, students who were sensitive to rewards were less likely to use ChatGPT. Not surprisingly, use of ChatGPT was likely to develop tendencies for procrastination and memory loss and dampen the students’ academic performance. Finally, academic workload, time pressure, and sensitivity to rewards had indirect effects on students’ outcomes through ChatGPT usage.

Full text

Is it harmful orhelpful? Examining
thecauses andconsequences ofgenerative AI
usage amonguniversity students
Muhammad Abbas1*, Farooq Ahmed Jam2,3 and Tariq Iqbal Khan4
Introduction
"e ChatGPT software is raising important questions for educators and researchers
all around the world, with regards to fraud in general, and particularly plagiarism," a
spokesperson for Sciences Po told Reuters (Reuters, 2023).
“I don’t think it [ChatGPT] has anything to do with education, except undermining it.
ChatGPT is basically high-tech plagiarism…and a way of avoiding learning.” said Noam
Chomsky, a public intellectual known for his work in modern linguistics, in an interview
(EduKitchen & January21, 2023).
Abstract
While the discussion on generative artificial intelligence, such as ChatGPT, is making
waves in academia and the popular press, there is a need for more insight into the use
of ChatGPT among students and the potential harmful or beneficial consequences
associated with its usage. Using samples from two studies, the current research
examined the causes and consequences of ChatGPT usage among university students.
Study 1 developed and validated an eight-item scale to measure ChatGPT usage
by conducting a survey among university students (N = 165). Study 2 used a three-
wave time-lagged design to collect data from university students (N = 494) to further
validate the scale and test the study’s hypotheses. Study 2 also examined the effects
of academic workload, academic time pressure, sensitivity to rewards, and sensitivity
to quality on ChatGPT usage. Study 2 further examined the effects of ChatGPT usage
on students’ levels of procrastination, memory loss, and academic performance. Study
1 provided evidence for the validity and reliability of the ChatGPT usage scale. Further-
more, study 2 revealed that when students faced higher academic workload and time
pressure, they were more likely to use ChatGPT. In contrast, students who were
sensitive to rewards were less likely to use ChatGPT. Not surprisingly, use of ChatGPT
was likely to develop tendencies for procrastination and memory loss and dampen
the students’ academic performance. Finally, academic workload, time pressure,
and sensitivity to rewards had indirect effects on students’ outcomes through ChatGPT
usage.
Keywords: Workload, Time pressure, Sensitivity to quality, Sensitivity to rewards,
ChatGPT usage, Procrastination, Memory loss, Academic performance
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RESEARCH ARTICLE
Abbasetal. Int J Educ Technol High Educ (2024) 21:10
https://doi.org/10.1186/s41239-024-00444-7
International Journal of Educational
Technology in Higher Education
*Correspondence:
pirthegreat@gmail.com
1 FAST School of Management,
National University of Computer
and Emerging Sciences,
Islamabad, Pakistan
2 Global Illuminators, Kuala
Lumpur, Malaysia
3 Department of Science &
Technology Studies, Faculty
of Science, University of Malaya,
Kuala Lumpur, Malaysia
4 Institute of Management
Sciences, The University
of Haripur, Haripur, Pakistan

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Abbasetal. Int J Educ Technol High Educ (2024) 21:10
In recent years, the use of generative artificial intelligence (AI) has significantly influ-
enced various aspects of higher education. Among these AI technologies, ChatGPT
(OpenAI, 2022) has gained widespread popularity in academic settings for a variety of
uses such as generation of codes or text, assistance in research, and the completion of
assignments, essays and academic projects (Bahroun etal., 2023; Stojanov, 2023; Str-
zelecki, 2023). ChatGPT enables students to generate coherent and contextually appro-
priate responses to their queries, providing them with an effective resource for their
academic work. However, the extensive use of ChatGPT brings a number of challenges
for higher education (Bahroun etal., 2023; Chan, 2023; Chaudhry etal., 2023; Dalalah &
Dalalah, 2023).
Scholars have speculated that the use of ChatGPT may bring many harmful conse-
quences for students (Chan, 2023; Dalalah & Dalalah, 2023; Dwivedi etal., 2023; Lee,
2023). It has the potential to harmfully affect students’ learning and success (Korn &
Kelly, 2023; Novak, 2023) and erode their academic integrity (Chaudhry etal., 2023).
Such lack of academic integrity can damage the credibility of higher education institu-
tions (Macfarlane etal., 2014) and harm the achievement motivation of students (Krou
etal., 2021). However, despite the increasing usage of ChatGPT in higher education, very
rare empirical research has focused on the factors that drive its usage among university
students (Strzelecki, 2023). In fact, majority of the prior studies consist of theoretical
discussions, commentaries, interviews, reviews, or editorials on the use of ChatGPT
in academia (e.g., Cooper, 2023; Cotton etal., 2023; Dwivedi etal., 2023; King, 2023;
Peters etal., 2023). For example, we have avery limited understanding of the key driv-
ers behind the use of ChatGPT by university students and how ChatGPT usageaffects
theirpersonal and academic outcomes. Similarly, despite many speculations, very lim-
ited research has empirically examined the beneficial or harmful effects of generative
AI usage on students’ academic and personal outcomes (e.g., Yilmaz & Yilmaz, 2023a,
2023b). Even these studies provide contradictory evidence on whether ChatGPT is help-
ful or harmful for students.
erefore, an understanding of the dynamics and the role of generative AI, such as
ChatGPT, in higher education is still in its nascent stages (Carless etal., 2023; Strzelecki,
2023; Yilmaz & Yilmaz, 2023a). Such an understanding of the motives behind Chat-
GPT usage and its potentially harmful or beneficial consequences is critical for educa-
tors, policymakers, and students, as it can help the development of effective strategies to
integrate generative AI technologies into the learning process and control their misuse
in higher education (Meyer etal., 2023). For the same reasons, scholars have called for
future research to delve deeper into the positives and negatives of ChatGPT in higher
education (Bahroun etal., 2023; Chaudhry etal., 2023; Dalalah & Dalalah, 2023).
Taken together, the current study has several objectives that aim to bridge these gaps
and significantly contribute to the body of knowledge and practice in higher education.
First, responding to the call of prior research on the development of ChatGPT usage
scale (Paul etal., 2023), we develop and validate a scale for ChatGPT usage in study 1.
Next, we conduct another study (i.e., study 2) to investigate several theoretically rel-
evant factors—such as academic workload, time pressure, sensitivity to rewards, and
sensitivity to quality—which may potentially affect the use of ChatGPT by university
students. In addition, concerns have been raised regarding the impact of ChatGPT on

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students’ academic performance and creativity. For example, scholars consider the use
of ChatGPT as “deeply harmful to a social understanding of knowledge and learning”
(Peters etal., 2023, p. 142) and having the potential to “kill creativity and critical think-
ing” (Dwivedi etal., 2023, p. 25). However, empirical evidence regarding the harmful or
beneficial consequences of ChatGPT usage remains largely unavailable. erefore, we
investigate the effects of ChatGPT usage on students’ procrastination, memory reten-
tion/loss, and academic performance (i.e., CGPA). Together, this research aims to pro-
vide valuable insights for educators, policymakers, and students in understanding the
factors that encourage the use of ChatGPT by students and the beneficial or deleterious
effects of such usage in higher education.
Literature andhypotheses
Academic workload and use of ChatGPT
Academic workload refers to the number of academic tasks, responsibilities, and activ-
ities that students are required to complete during a specific period, usually a semes-
ter. e workload encompasses the volume and complexity of assignments or projects
(Bowyer, 2012). Students are put under high stress when they have an excessive amount
of academic work to complete (Yang etal., 2021).
Studies indicate that overburdened students are more likely to rely on unethical
means to complete their academic tasks instead of relying on their own abilities and
learning. For example, Devlin and Gray (2007) found that students engage in unethi-
cal academic practices such as cheating and plagiarism when they are exposed to heavy
workload. Similarly, Koudela-Hamila etal. (2022) found a significantly positive relation-
ship between academic workload and academic stress among university students. In
another study, Hasebrook etal. (2023) found that individuals were more likely to accept
and adopt technology when their workload was high. Consistently, when students are
faced with high workload, they look for ways to cope with this demanding situation. As a
result, they use easy means or shortcuts (e.g., ChatGPT) to cope with such stressful situ-
ations (i.e., heavy workload). Consequently, we suggest:
Hypothesis 1 Workload will be positively related to the use of ChatGPT.
Time pressure and use of ChatGPT
Time pressure is described as the perception that an impending deadline is becom-
ing closer and closer (Carnevale & Lawler, 1986). Under time pressure, individuals use
simple heuristics in order to complete tasks (Rieskamp & Hoffrage, 2008). Under high
time pressure, students may consider the available time as insufficient to accomplish
the assignments, and thereforethey may rely on ChatGPT to complete these tasks. Pre-
liminary research indicates that time pressure to complete academic tasks encourages
plagiarism among students (Koh etal., 2011). Devlin and Gray (2007) also found that
students engage in cheating and plagiarism under time pressure to complete their aca-
demic tasks. Similarly, those students who are exposed to time pressure adopt a surface
learning approach (Guo, 2011), which indicates that the students may use shortcuts such
as ChatGPT to complete their tasks within deadlines. erefore, we argue that under

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high levels of time pressure, students are more likely to use ChatGPT for their academic
activities. Consequently, we suggest:
Hypothesis 2 Time pressure will be positively related to the use of ChatGPT.
Sensitivity to rewards and use of ChatGPT
Sensitivity to rewards is the degree to whicha student is worried or concerned about his
or her academic rewards such as grades. As far as the relationship between sensitivity to
rewards and ChatGPT usageis concerned, prior research does not help to make a clear
prediction. For example, on the one hand, it is possible that students with higher sensi-
tivity to rewards may be more inclined to use ChatGPT, as they perceive it as a means
to obtain better academic results. ey may see ChatGPT as a resource to enhance their
academic performance and get good grades. Evidence indicates that individuals, who are
highly sensitive to rewards or impulsive, have a tendency to engage in risky behaviors
such as texting on their cell phones while driving (Hayashi etal., 2015; Pearson etal.,
2013). is indicates that students, who are reward sensitive, may engage in risky behav-
iors such as the misuse of ChatGPT for academic activitiesor plagiarism.
On the other hand, it is also possible that students who arehighly worried about their
rewards may not use ChatGPT for the fear of losing their grades. Since the use of Chat-
GPT for academic activities is usually considered as an unethical mean (Dalalah etal.,
2023; Dwivedi et al., 2023), highly reward sensitive individuals may be more cautious
about using technologies that their teachers perceive as ethically questionable or could
jeopardize their academic integrity and grades. Consequently, we suggest competing
hypotheses:
Hypothesis 3a Sensitivity to rewards will be positively related to the use of ChatGPT.
Hypothesis 3b Sensitivity to rewards will be negatively related to the use of ChatGPT.
Sensitivity to quality and use of ChatGPT
Sensitivity to quality or quality consciousness refers to the extent to which students are
perceptive when evaluating the standard and excellence of their educational activities.
is sensitivity involves the students’ consciousness of the quality of learning they are
having (Olugbara etal., 2020) or the quality of contents (e.g., assignments or projects)
they are working on. We suggest that students, who are sensitive to the quality of the
contents, are more likely to use different tools to enhance the quality of their academic
work.
ChatGPT can be used by quality-conscious students for numerous reasons. Students,
who are sensitive to quality, may want to ensure excellence, accuracy, and reliability in
their work—and they may recognize the potential benefits of using ChatGPT to meet
their expectations for high-quality academic work (Haensch etal., 2023; Yan, 2023). Sim-
ilarly, students high in sensitivity to quality often pay great attention to grammar, style,
and language precision. ChatGPT can assists in refining their written work by providing
suggestions for sentence structure, word choice, and grammar (Abbas, 2023; Dwivedi
etal., 2023). erefore, students with a strong sensitivity to quality are more likely to

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use ChatGPT in order to enhance the quality of their academic work (e.g., assignments,
projects, essays, or presentations), as compared to those who are not sensitive to quality.
Consequently, we suggest:
Hypothesis 4 Sensitivity to quality will be positively related to the use of ChatGPT.
Use of ChatGPT and procrastination
Procrastination occurs when people “voluntarily delay an intended course of action
despite expecting to be worse off for the delay” (Steel, 2007, p. 66). Some individuals
are predisposed to put off doing things until later (i.e., chronic procrastinators), whereas
others only do so in certain circumstances (Rozental etal., 2022). Academic procrastina-
tion, which refers to the practice of routinely putting off academic responsibilities to the
point that the delays become damaging to performance, is an important issue both for
students and educational institutions (Svartdal & Løkke, 2022).
Studies suggest that procrastination occurs very frequently in students (Bäulke & Dre-
sel, 2023) and it may be influenced by a variety of environmental and personal factors
(Liu etal., 2023; Steel, 2007). We argue that the use of generative AI may influence the
tendencies for procrastination among students. Using short-cuts, which may help stu-
dents to complete the academic tasks without putting much efforts, will eventually make
the students habitual. As a result, these short cuts—such as theuse of ChatGPT—may
cause procrastination among students. For example, a student who is addicted to Chat-
GPT usage maybelieve that he or she can complete an academic assignment or a project
within less time and without putting much efforts. Such feelings of having control over
the tasks are likely to encourage the students to delay those tasks till the last moment,
thereby resulting in procrastination. More recent evidence alsoindicates that ChatGPT
usage may cause laziness among students (Yilmaz & Yilmaz, 2023a). Consequently, we
suggest:
Hypothesis 5 Use of ChatGPT will be positively related to procrastination.
Use of ChatGPT and memory loss
Memory loss refers to a condition or a state in which an individual experiences difficulty
in recalling information or events from the past (Mateos etal., 2016). Scholars indicate
that cognitive, emotional, or physical conditions affect memory functioning among indi-
viduals (Fortier-Brochu etal., 2012; Schweizer etal., 2018). We argue that excessiveuse
of ChatGPT may result in memory loss among students. Continuous use of ChatGPT
for academic tasks may develop laziness among the students and weaken their cognitive
skills (Yilmaz & Yilmaz, 2023a) leading to a memory loss.
Over time, overreliance on generative AI tools for academic tasks, instead of critical
thinking and mental exertion, may damage memory retention, cognitive functioning,
and critical thinking abilities (Bahrini etal., 2023; Dwivedi et al., 2023). Active learn-
ing, which involves active cognitive engagement with the content, is crucial for memory
consolidation and retention (Cowan etal., 2021). Since ChatGPT can quickly respond to
any questions asked by auser (Chan etal., 2023), students who excessivelyuse ChatGPT

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may reduce their cognitive efforts to complete their academic tasks, resulting in poor
memory.
Related evidence demonstrates that daily mental training helps to improve cognitive
functions among individuals (Uchida & Kawashima, 2008). Similarly,fast simple numer-
ical calculation training (FSNC) was associated with improvements in performance
on simple processing speed, improvedexecutive functioning, and better performance
in complex arithmetic tasks (Takeuchi et al., 2016). Moreover, Nouchi et al. (2013)
found that brain training games helped to boost working memory and processing speed
in young adults. erefore, the extensive use of ChatGPT may yield an absence of such
cognitive trainings, thereby leading to memory loss among students. Consequently, we
suggest:
Hypothesis 6 Use of ChatGPT will be positively related to memory loss.
Use of ChatGPT and academic performance
Academic performance refers to the level of accomplishment that a student demon-
strates in his or her educational pursuits. e objective measure of a student’s academic
performance is indicated by cumulative grade point average (CGPA), which is a grading
system used in educational institutions to measure a student’s overall academic perfor-
mance in a specific period, usually a semester.
If students effectively leverage the insights gained from ChatGPT to improve
theirunderstanding of a subject, it may positively influence their academic performance.
However, if they rely solely on ChatGPT without putting in the necessary efforts, critical
thinking, and independent study, it may harm their academic performance. Over-reli-
ance on external sources, including generative AI tools, without personal engagement
and active learning, can hinder the development of essential skills and the depth of
knowledge required for academic success (Chan etal., 2023). erefore, students who
habitually use ChatGPT may end up demonstrating poor academic performance. Conse-
quently, we suggest:
Hypothesis 7 Use of ChatGPT will be negatively related to academic performance.
The mediating role of ChatGPT usage
We further suggest that ChatGPT usage will mediate the relationships of workload,
time pressure, sensitivity to quality, and sensitivity to rewards with students’ outcomes.
Specifically, students who experience heavy workload and time pressure to complete
their academic tasks are likely to engage in ChatGPT usage to cope with these stress-
ful situations. In turn, reliance on ChatGPT may lead to delays in the accomplishment
of the tasks (i.e., procrastination) because the students may believe that they can com-
plete the tasks at any time without putting much efforts. Similarly, the excessively reli-
ance on ChatGPT, as a substitute for their critical thinking and problem-solving skills,
may hinder their ability to develop a deeper understanding of the subject matter, which
can harmfully impact their academic performance (Abbas, 2023). Further, the high use
of ChatGPT for academic tasks could potentially lead to reduced mental engagement,

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thereby exacerbating the risk of memory impairment (Bahrini etal., 2023; Dwivedi etal.,
2023).
Moreover, ChatGPT usage will mediate the relationships of rewards sensitivity and
quality sensitivity with procrastination, memory loss, and academic performance. e
fear of losing marks (i.e., reward sensitivity) and the consciousness towards quality of
academic work (i.e., sensitivity to quality), may influence the use of ChatGPT. In turn,
the excessive (or less) use of ChatGPT may affect students’ procrastination, memory
loss, and academic performance. Together, we suggest:
Hypothesis 8 Use of ChatGPT will mediate the relationships of workload with procras-
tination, memory loss, and academic performance.
Hypothesis 9 Use of ChatGPT will mediate the relationships of time pressure with pro-
crastination, memory loss, and academic performance.
Hypothesis 10 Use of ChatGPT will mediate the relationships of sensitivity to rewards
with procrastination, memory loss, and academic performance.
Hypothesis 11 Use of ChatGPT will mediate the relationships of sensitivity to quality
with procrastination, memory loss, and academic performance.
Methods (Study 1)
ChatGPT usage scale development procedures
Item generation: We used scale development procedures proposed in prior research
(Hinkin, 1998).We first defined ChatGPT usage as the extent to which students use
ChatGPT for various academic purposes including completion of assignments, projects,
or preparation of exams. Based on this definition, initially 12 items were developed for
further scrutiny.
Initial item reduction: Following the guidelines of Hinkin (1998), we performed an
item-sorting process during the early stages of scale development. In order to establish
content validity of the ChatGPT usage scale, we conducted interviews from five experts
of the relevant field. e experts were asked to evaluate each item intended to measure
ChatGPT usage. e experts agreed that 10 of the 12 items measured certain aspects of
the academic use of ChatGPT by the students. Based on the content validity, these 10
items were finalized for further analyses.
Sample and data collection
e 10-item scale for the use of ChatGPT was distributed among 165students from
numerous university across Pakistan. e responses were taken on a 6-point Likert
type scale with anchors ranging from 1 = never to 6 = always. A cover letter clearly
communicated that the participation was voluntary, and the student could decline
participation at any point during data collection. e respondents were also ensured
complete confidentiality of their responses. e sample consisted of 53.3% males. e
average age was 23.25year (S.D = 4.22). Around 85% universities were from public
sector and the remaining belonged to private sector. Similarly, around 59% students

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were enrolled in business studies, 6% were enrolled in computer sciences, 9% were
enrolled in general education, 5% were enrolled in psychology, 4% were enrolled in
English language, 4% were enrolled in public administration, 9% were enrolled in
sociology, and 4% were enrolled in mathematics. Furthermore, around 74% were
enrolled in bachelor’s programs, 22% were enrolled in master’s programs, and 4%
were enrolled in doctoral programs.
Exploratory factor analysis
Next, we conducted an exploratory factor analysis (EFA) to determine the fac-
tor structure of the proposed scale (Field, 2018; Hinkin, 1998). Principal component
analysis (Nunnally & Bernstein, 1994) was used for extraction and the varimax rota-
tion with Kaiser normalization was used as a rotation method. To ascertain the num-
ber of variables, the parameters of eigenvalue > 1 and the total percentage of variance
explained > 50% were used. e results revealed that the Bartlett’s test of sphericity was
significant (p < 0.001) and the Kaiser–Meyer–Olkin (KMO) sampling adequacy was
0.878 (p < 0.001), which was greater than the threshold value of 0.50, thereby considered
acceptable for sample adequacy (Field, 2018). Further, factor loadings and communali-
ties above 0.5 are usually considered acceptable (Field, 2018). As shown in Table1, the
results revealed that item 4 and item 9 had low factor loadings and communalities.
We then dropped these two items and conducted another EFA on the remaining
eight items. As presented in Table2, all 8 items exceeded the threshold criteria. Also,
a one-factor structure accounted for 62.65% of the cumulative variance with all item
loadings above 0.50. erefore, the final scale to measure use of ChatGPT consisted
of eight items. e Cronbach’s alpha (CA) for the 8-item scale was α = 0.914 and the
composite reliability (CR) was 0.928. ese scores of CA and CR exceeded the thresh-
old value of 0.7, thereby indicating construct reliability (Nunnally & Bernstein, 1994).
Finally, as shown in Table2, the average variance extracted (AVE) score was 0.618,
which was above the threshold value of 0.5, thus indicating convergent validity (Hair
et al., 2019).Together, these results established good reliability and validity of the
8-item scale to measure ChatGPT usage.
Table 1 Use of ChatGPT scale: factor loadings, communalities, and total variance extracted (Study 1)
Items Factor loading Communalities Total
variance
extracted
I use ChatGPT for my course assignments 0.82 0.67 54.721
I use ChatGPT for my course projects 0.78 0.60
I use ChatGPT for my academic activities 0.82 0.67
I can’t think of studies without ChatGPT 0.58 0.34
I rely on ChatGPT for my studies 0.79 0.63
I use ChatGPT to learn course-related concepts 0.76 0.57
I am addicted to ChatGPT when it comes to studies 0.79 0.63
I use ChatGPT to prepare for my tests or quizzes 0.76 0.58
Use of ChatGPT is common nowadays 0.44 0.19
ChatGPT is part of my campus life 0.77 0.59

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Methods (Study 2)
Sample and data collection procedures
e objective of study 2 was to further validate the 8-item ChatGPT scale developed
in study 1. In addition, we tested the study’s hypotheses in study 2. Figure1 presents
the theoretical framework of study 2. e study used a time-lagged design, whereby the
data were collected using online forms in three phases with a gap of 1–2weeks after
each phase. e data were collected from individuals who were currently enrolled in a
university.
We used procedural and methodological remedies recommended by scholars (see,
Podsakoff etal., 2012) to address issues related to common method bias. First, we clearly
communicated to our participants that their involvement was voluntary, and they
retained the right to decline participation at any point during data collection. In addition,
we ensured complete confidentiality of their responses, emphasizing that there were no
right or wrong responses to the questions. Finally, we used a three-wave time-lagged
design to keep a temporal separation between predictors and outcomes (Podsakoff etal.,
2012). In each phase, the students were asked to assigned a code initially generated by
them so that the survey forms for each respondent could be matched. Moreover, ethi-
cal clearance and approvals from the ethics committees of the authors’ institutions were
Table 2 Revised use of ChatGPT scale: factor loadings, communalities, and total variance extracted
(Study 1)
CA Cronbach’s Alpha, CR composite reliability, AVE average variance extracted
Items Factor loading Communalities Total
variance
extracted
CA CR AVE
I use ChatGPT for my course assign-
ments 0.837 0.701 62.650 0.914 0.928 0.618
I use ChatGPT for my academic activi-
ties 0.824 0.680
I use ChatGPT for my course projects 0.798 0.637
I am addicted to ChatGPT when it
comes to studies 0.792 0.627
I rely on ChatGPT for my studies 0.780 0.608
I use ChatGPT to prepare for my tests
or quizzes 0.775 0.601
I use ChatGPT to learn course-related
concepts 0.769 0.592
ChatGPT is part of my campus life 0.752 0.566
Fig. 1 Theoretical framework of the study

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also obtained. Since English is the official language in all educational institutions, the
survey forms were distributed in English. Past research has also used English language
for survey research (e.g., Abbas & Bashir, 2020; Fatima etal., 2023; Malik etal., 2023).
In the first phase, around 900 participants were contacted to fill the survey on work-
load, time pressure, sensitivity to quality, sensitivity to rewards, and demographics. At
the end of the first phase, a total of 840 surveys were received. In the second phase,
after 1–2weeks, the same respondents were contacted to fill the survey on the use of
ChatGPT. Around 675 responses were received at the end of the second phase. Finally,
another two weeks later, these 675 respondents were contacted again to collect data on
memory loss, procrastination, and academic performance. At the end of the third phase,
around 540 survey forms were returned. After removing surveys which contained miss-
ing data, the final sample size consisted of 494 complete responses which were then used
for further analyses.
Of these 494 respondents, 50.8% were males and the average age of the respondents
was 22.16 (S.D. = 3.47) years. Similarly, 88% of the respondents belonged to public sec-
tor and 12% belonged to private sector universities. Around 65% students were enrolled
in business studies, 3% were enrolled in computer sciences, 12% were enrolled in gen-
eral education, 1% were studying English language, 9% were studying public administra-
tion, and 10% were studying sociology. Finally, around 74% were enrolled in bachelor’s
programs, 24% were enrolled in master’s programs, and 2% were enrolled in doctoral
programs.
Measures
All variables, except for the use of ChatGPT, were measured on a 5-point Likert type
scale with anchors ranging from 1 = strongly disagree to 5 = strongly agree. Use of Chat-
GPT was measured on a 6-point Likert type scale with anchors ranging from 1 = ne ver
to 6 = always. e complete items for all measures are presented in Table3.
Academic workload: A 4-item scale by Peterson etal. (1995) was adapted to measure
academic workload. A sample item included, ‘I feel overburdened due to my studies.’
Academic time pressure: A 4-item scale by Dapkus (1985) was adapted to measure time
pressure. A sample item was, ‘I don’t have enough time to prepare for my class projects.’
Sensitivity to rewards: We measured sensitivity to rewards with a 2-item scale. e
items included, ‘I am worried about my CGPA’ and ‘I am concerned about my semester
grades.’
Sensitivity to quality: Sensitivity to quality was measured with a 2-item scale. e
items were, ‘I am sensitive about the quality of my course assignments’ and ‘I am con-
cerned about the quality of my course projects.’
Use of ChatGPT: We used the 8-item scale developed in study 1 to measure the use of
ChatGPT. A sample item was, ‘I use ChatGPT for my academic activities.’
Procrastination: A 4-item scale developed by Choi and Moran (2009) was used to
measure procrastination. A sample item included, ‘I’m often running late when getting
things done.’
Memory loss: We used a 3-item scale to measure memory loss. A sample item was,
‘Nowadays, I can’t retain too much in my mind.’

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Abbasetal. Int J Educ Technol High Educ (2024) 21:10
Academic performance: We used an objective measure of academic performance
to avoid self-report or social desirability bias. Each student reported his or her latest
CGPA. The CGPA score ranges between 1 = lowest to 4 = highest. Since CGPA for
each respondent was obtained as a single score, there was no need to calculate its
reliability or validity.
Table 3 Factor loading, reliability, and validity (Study 2)
The CA, CR, and AVE for academic performance was not calculated because it was measured using a single item/score (i.e.,
CGPA)
CA Cronbach’s Alpha, CR composite reliability, AVE average variance extracted
Items Loadings CA CR AVE
Workload (Peterson et al., 1995) 0.845 0.895 0.680
My academic workload is too heavy 0.861
I feel overloaded by the work my studies require 0.838
I feel overburdened due to my studies 0.810
The teacher(s) give too much work to do 0.788
Time pressure (Dapkus, 1985) 0.740 0.833 0.562
I don’t have enough time to prepare for my class projects 0.829
I don’t have enough time to complete study-related tasks with appropriate
care 0.810
I find it difficult to submit my assignments and projects within the deadlines 0.804
I am often in hurry when it comes to meeting academic deadlines 0.511
Sensitivity to rewards 0.881 0.944 0.894
I am worried about my CGPA 0.947
I am concerned about my semester grades 0.944
Sensitivity to quality 0.717 0.871 0.773
I am concerned about the quality of my course projects 0.930
I am sensitive about the quality of my course assignments 0.825
Use of ChatGPT 0.903 0.922 0.596
I use ChatGPT for my academic activities 0.812
I use ChatGPT to prepare for my tests or quizzes 0.795
I use ChatGPT for my course projects 0.788
I use ChatGPT to learn course-related concepts 0.778
I rely on ChatGPT for my studies 0.771
I use ChatGPT for my course assignments 0.762
I am addicted to ChatGPT when it comes to studies 0.735
ChatGPT is part of my campus life 0.732
Procrastination (Choi & Moran, 2009) 0.756 0.845 0.577
I often fail to accomplish goals that I set for myself 0.795
I’m often running late when getting things done 0.792
I often start things at the last minute and find it difficult to complete them on
time 0.739
I have difficulty finishing activities once I start them 0.710
Memory loss 0.757 0.860 0.672
Nowadays, I often forget things to do 0.862
Nowadays, I can’t retain too much in my mind 0.829
Nowadays, I feel that I am losing my memory 0.765

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Abbasetal. Int J Educ Technol High Educ (2024) 21:10
Analyses andresults (Study 2)
We used partial least squares (PLS) method to validate the measurements and test the
hypotheses, as PLS is a second-generation structural equation modeling (SEM) tech-
nique that estimates relationships among latent variables by taking measurement errors
into account and it is considered as a superior technique (Hair etal., 2017). e pro-
gram utilizes bootstrapping approaches which entails the process of resampling from
the dataset to provide standard errors and confidence intervals, yielding a more precise
assessment of the model’s stability (Hair etal., 2017, 2019). Further, partial least squares
(PLS) are often favored insituations with limited sample numbers and non-normal dis-
tributions (Hair etal., 2019).
Measurement model
e measurement model is presented in Fig.2. In the measurement model, first, we ran
all the constructs together and examined the commonly used indicators of standardized
factor loading, CA, CR, and AVE. e measurement model exhibited adequate levels of
validity and reliability. As shown in Table3, the standardized factor loadings for each
item of each measure were above the threshold level of 0.70 (Hair etal., 2019). Similarly,
CA and CR scores for each measure were above 0.70 and the AVE also surpassed 0.5. All
scores exceeded the cut-off criteria, thereby establishing reliability and convergent valid-
ity of each construct (Hair etal., 2019).
Furthermore, discriminant validity ensures that each latent construct is distinct from
other constructs. As per Fornell and Larcker’s (1981) criteria, discriminant validity is
established if the squared root of the AVE for each construct is larger than the correla-
tion of that construct with other constructs. As shown in Table4, the squared root of
the AVE for each construct (the value along the diagonalpresented in bold) exceeded
Fig. 2 Measurement indicators outer-loadings and AVE (Study 2)

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Abbasetal. Int J Educ Technol High Educ (2024) 21:10
Table 4 Discriminant validity (Study 2)
Fornell and Larcker criteria Heterotrait-monotrait ratio
1 2 3 4 5 6 7 1 2 3 4 5 6
1. Workload 0.825
2. Time pressure 0.560 0.750 0.695
3. Sensitivity to rewards 0.174 0.051 0.945 0.208 0.072
4. Sensitivity to quality 0.266 0.104 0.489 0.879 0.346 0.161 0.611
5. Use of ChatGPT 0.216 0.236 − 0.051 0.038 0.772 0.233 0.266 0.088 0.086
6. Procrastination 0.276 0.366 0.062 0.050 0.307 0.760 0.336 0.493 0.075 0.089 0.361
7. Memory loss 0.278 0.246 0.111 0.053 0.273 0.551 0.820 0.345 0.334 0.139 0.084 0.322 0.724

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Abbasetal. Int J Educ Technol High Educ (2024) 21:10
the correlation of that construct with other constructs, thereby establishing discriminant
validity of all constructs. Similarly, Henseler etal. (2015) consider Heterotrait-Monotrait
(HTMT) ratio as a better tool to establish discriminate validity, as a large number of
researchers have also used it (e.g., Hosta & Zabkar, 2021). HTMT values below 0.85 are
considered good to establish discriminant validity (Henseler etal., 2015). As shown in
Table4, all of the HTMT values were below the threshold, thereby establishing discrimi-
nant validity among the study’s constructs.
Furthermore, in order to test multicollinearity, we calculated variance inflation factor
(VIF), which should be less than5 to rule out the possibility of multicollinearity among
the constructs (Hair etal., 2019). In all analyses, VIF scores were less than 5, indicating
that multicollinearity was not a problem.
Structural model
We then tested the study’s hypotheses for direct and indirect effect using bootstrapping
procedures with 5,000 samples in SmartPLS (Hair etal., 2017). e structural model is
presented in Fig.3.
As presented in Table5, the findings revealed that workload was positively related to
the use of ChatGPT (β = 0.133, t = 2.622, p < 0.01). ose students who experienced high
levels of academic workload were more likely to engage in ChatGPT usage. is result
supported hypothesis 1. Similarly, time pressure also had a significantly positive rela-
tionship with the use of ChatGPT (β = 0.163, t = 3.226, p < 0.001), thereby supporting
hypothesis 2. In other words, students who experienced high time pressure to accom-
plish their academic tasks also reported higher use of ChatGPT. Further, the effect of
sensitivity to rewards on the use of ChatGPT was negative and marginally significant
(β = −0.102, t = 1.710, p < 0.10), thereby suggesting that students who are more sensitive
Fig. 3 Structural model (Study 2)

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Abbasetal. Int J Educ Technol High Educ (2024) 21:10
to rewards are less likely to use ChatGPT. ese results supported hypothesis 3b instead
of hypothesis 3a. Finally, we found that sensitivity to quality was not significantly related
to the use of ChatGPT (β = 0.033, t = 0.590, n.s). us, hypothesis 4 was not supported.
Consistent with hypothesis 5, the findings further revealed that the use of ChatGPT
was positively related to procrastination (β = 0.309, t = 6.984, p < 0.001). ose stu-
dents who frequently used ChatGPT were more likely to engage in procrastination
than those who rarely used ChatGPT. Use of ChatGPT was also found to be positively
related to memory loss (β = 0.274, t = 6.452, p < 0.001), thus hypothesis 6 was also
supported. Students who frequently used ChatGPT also reported memory impair-
ment. Furthermore, use of ChatGPT was found to have a negative effect on academic
performance (i.e., CGPA) of the students (β = −0.104, t = 2.390, p < 0.05). Students
who frequently used ChatGPT for their academic tasks had poor CGPAs. ese find-
ings rendered support for hypothesis 7.
Table6 presents the results for all indirect effects. As shown in Table6, workload
had a positive indirect effect on procrastination (indirect effect = 0.041, t = 2.384,
p < 0.05) and memory loss (indirect effect = 0.036, t = 2.333, p < 0.05) through the
use of ChatGPT. Students who experienced higher workload were more likely to use
ChatGPT which in turn developed the habits of procrastination among them and
caused memory loss. Similarly, workload had a negative indirect effect on academic
performance (indirect effect = − 0.014, t = 1.657, p < 0.10) through the use of Chat-
GPT. In other words, students who experienced higher workload were more likely to
use ChatGPT. As a result, the extensive use of ChatGPT dampened their academic
performance. ese results supported hypothesis 8.
In addition, time pressure had a positive indirect effect on both procrastination (indi-
rect effect = 0.050, t = 2.607, p < 0.01) and memory loss (indirect effect = 0.045, t = 2.574,
p < 0.01), through an increased utilization of ChatGPT. Students facing higher time con-
straints were more inclined to use ChatGPT, ultimately fostering procrastination habits
and experiencing memory issues. Similarly, time pressure had a negative indirect effect
on academic performance (indirect effect = − 0.017, t = 1.680, p < 0.10), mediated by
the increased use of ChatGPT. us, students experiencing greater time pressure were
more likely to rely heavily on ChatGPT, consequently leading to a dampening of their
academic performance. Together, these results supported hypothesis 9.
Furthermore, sensitivity to rewards had a negative indirect relationship with pro-
crastination (indirect effect = − 0.032, 1.676, p < 0.10) and memory loss (indirect
Table 5 Direct effects (Study 2)
Hypothesis Path Coecient T Statistics P-value Status
H1 Workload -> Use of ChatGPT 0.133 2.622 0.009 Supported
H2 Time Pressure -> Use of ChatGPT 0.163 3.226 0.001 Supported
H3a, H3b Sensitivity to Rewards -> Use of ChatGPT − 0.102 1.710 0.087 H3b supported
H4 Sensitivity to Quality -> Use of ChatGPT 0.033 0.590 0.555 Not supported
H5 Use of ChatGPT -> Procrastination 0.309 6.984 0.000 Supported
H6 Use of ChatGPT -> Memory Loss 0.274 6.452 0.000 Supported
H7 Use of ChatGPT -> Academic Performance − 0.104 2.390 0.017 Supported

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Abbasetal. Int J Educ Technol High Educ (2024) 21:10
effect = −0.028, t = 1.668, p < 0.10) through the use of ChatGPT. Students who were
sensitive to rewards were less likely to use ChatGPT and thus experience lower levels
of procrastination and memory loss. However, the findings revealed that the indirect
effect of sensitivity to rewards on academic performance was insignificant (indirect
effect = 0.011, t = 1.380, p = 0.168). ese findings supported hypothesis 10 for pro-
crastination and memory loss only. Finally, the indirect effects of sensitivity to qual-
ity on procrastination (indirect effect = 0.010, t = 0.582, n.s), memory loss (indirect
effect = 0.009, t = 0.582, n.s), and academic performance (indirect effect = −0.003,
t = 0.535, n.s) through the use of ChatGPT were all insignificant. erefore, hypoth-
esis 11 was not supported.
Overall discussion
Major ndings
e recent emergence of generative AI has brought about significant implications for
various societal institutions, including higher education institutions. As a result, there
has been a notable upswing in discussions among scholars and academicians regarding
the transformative potential of generative AI, particularly ChatGPT, in higher education
and the risks associated with it (Dalalah & Dalalah, 2023; Meyer etal., 2023; Peters etal.,
2023; Yilmaz & Yilmaz, 2023a). Specifically, the dynamics of ChatGPT are still unknown
in the context that no study, to date, has yet provided any empirical evidence on why
students’ use ChatGPT. e literature is also silent on the potential consequences, harm-
ful or beneficial, of ChatGPT usage (Dalalah & Dalalah, 2023; Paul etal., 2023) despite a
ban in many institutions across the globe. Responding to these gaps in the literature, the
current study proposed workload, time pressure, sensitivity to rewards, and sensitivity
Table 6 Indirect effects via use of ChatGPT (Study 2)
Hypothesis Path Coecient T Statistics P-value Status
H8 Workload -> Use of ChatGPT -> Procrastina-
tion 0.041 2.384 0.017 Supported
H8 Workload -> Use of ChatGPT -> Memory Loss 0.036 2.333 0.020 Supported
H8 Workload -> Use of ChatGPT -> Academic
Performance − 0.014 1.657 0.098 Supported
H9 Time Pressure -> Use of ChatGPT -> Procras-
tination 0.050 2.607 0.009 Supported
H9 Time Pressure -> Use of ChatGPT -> Memory
Loss 0.045 2.574 0.010 Supported
H9 Time Pressure -> Use of ChatGPT -> Aca-
demic Performance − 0.017 1.680 0.093 Supported
H10 Sensitivity to Rewards -> Use of ChatGPT
-> Procrastination − 0.032 1.676 0.094 Supported
H10 Sensitivity to Rewards -> Use of ChatGPT
-> Memory Loss − 0.028 1.668 0.095 Supported
H10 Sensitivity to Rewards -> Use of ChatGPT
-> Academic Performance 0.011 1.380 0.168 Not supported
H11 Sensitivity to Quality -> Use of ChatGPT
-> Procrastination 0.010 0.582 0.561 Not supported
H11 Sensitivity to Quality -> Use of ChatGPT
-> Memory Loss 0.009 0.582 0.561 Not supported
H11 Sensitivity to Quality -> Use of ChatGPT
-> Academic Performance − 0.003 0.535 0.593 Not supported

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Abbasetal. Int J Educ Technol High Educ (2024) 21:10
to quality as the potential determinants of the use of ChatGPT. In addition, the study
examined the effects of ChatGPT usage on students’ procrastination, memory loss, and
academic performance.
e findings suggested that those students who experienced high levels of academic
workload and time pressure to accomplish their tasks reported higher use of ChatGPT.
Regarding the competing hypotheses on the effects of sensitivity to rewards on ChatGPT
usage, the findings suggested that the students who were more sensitive to rewards were
less likely to use ChatGPT. is indicates that rewards sensitive students might avoid the
use ChatGPT for the fear of getting a poor grade if caught. Surprisingly, we found that
sensitivity to quality was not significantly related to the use of ChatGPT. It appears that
quality consciousness might not determine the use of ChatGPT because some quality
conscious students might consider the tasks completed by personal effort as having high
quality. In contrast, other quality conscious students might consider ChatGPT written
work as having a better quality.
Furthermore, our findings suggested that excessive use of ChatGPT can have harmful
effects on students’ personal and academic outcomes. Specifically, those students who
frequently used ChatGPT were more likely to engage in procrastination than those who
rarely used ChatGPT. Similarly, students who frequently used ChatGPT also reported
memory loss. In the same vein, students who frequently used ChatGPT for their aca-
demic tasks had a poor CGPA. e mediating effects indicated that academic workload
and time pressure were likely to promote procrastination and memory impairment
among students through the use of ChatGPT. Also, these stressors dampened students’
academic performance through the excessive use of ChatGPT. Consistently, the findings
suggested that higher reward sensitivity discouraged the students to use ChatGPT for
their academic tasks. e less use of ChatGPT, in turn, helped the students experience
lower levels of procrastination and memory loss.
Theoretical implications
e current study responds to the calls for the development of a novel scale to measure
the use of ChatGPT and an empirical investigation into the harmful or beneficial effects
of ChatGPT in higher education for a better understand of the dynamics of generative
AI tools. Study 1 uses a sample of university students to develop and validate the use of
ChatGPT scale. We believe that the availability of the new scale to measure the use of
ChatGPT may help further advancement in this field. Moreover, study 2 validates the
scale using another sample of university students from a variety of disciplines. Study 2
also examines the potential antecedents and consequences of ChatGPT usage. is is
the first attempt to empirically examine why students might engage in ChatGPT usage.
We provide evidence on the role of academic workload, time pressure, sensitivity to
rewards, and sensitivity to quality in encouraging the students to use ChatGPT for aca-
demic activities.
e study also contributes to the prior literature by examining the potential delete-
rious consequences of ChatGPT usage. Specifically, the study provides evidence that
the excessive use of ChatGPT can develop procrastination, cause memory loss, and
dampen academic performance of the students. e study is a starting point that paws

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Abbasetal. Int J Educ Technol High Educ (2024) 21:10
path for future research on the beneficial or deleterious effects of generative AI usage in
academia.
Practical implications
e study provides important implications for higher education institutions, policy
makers, instructors, and students. Our findings suggest that both heavy workload and
time pressure are influential factors driving students to use ChatGPT for their academic
tasks. erefore, higher education institutions should emphasize the importance of
efficient time management and workload distribution while assigning academic tasks
and deadlines. While ChatGPT may aid in managing heavy academic workloads under
time constraints, students must be kept aware of the negative consequences of exces-
sive ChatGPT usage. ey may be encouraged to use it as a complementary resource
for learning instead of a tool for completing academic tasks without investing cognitive
efforts. In the same vein, encouraging students to keep a balance between technological
assistance and personal effort can foster a holistic approach to learning.
Similarly, policy makers and educators should design curricula and teaching strategies
that engage students’ natural curiosity and passion for learning. While ChatGPT’s ease
of use might be alluring, fostering an environment where students derive satisfaction
from mastering challenging concepts independently can mitigate overreliance on gen-
erative AI tools. Also, recognizing and rewarding students for their genuine intellectual
achievements can create a sense of accomplishment that may supersede the allure of
quick AI-based solutions. As also noted by Chaudhry etal. (2023), in order to discourage
misuse of ChatGPT by the students, the instructors may revisit their performance evalu-
ation methods and design novel assessment criteria that may require the students to use
their own creative skills and critical thinking abilities to complete assignments and pro-
jects instead of using generative AI tools.
Moreover, given the preliminaryevidence that extensive use of ChatGPT has a nega-
tive effect on a students’ academic performance and memory, educators should encour-
age students to actively engage in critical thinking and problem-solving by assigning
activities, assignments, or projects that cannot be completed by ChatGPT. is can
mitigate the adverse effects of ChatGPTon their learning journey and mental capabili-
ties. Furthermore, educators can create awareness among students about the potential
pitfalls of excessive ChatGPT usage. Finally, educators and policy makers can develop
interventions that target both the underlying causes (e.g., workload, time pressure,
sensitivity to rewards) and the consequences (e.g., procrastination, memory loss, and
academic performance). ese interventions could involve personalized guidance, skill-
building workshops, and awareness campaigns to empower students to leverage genera-
tive AI tools effectively while preserving their personal learning.
Limitations and future research directions
Like other study, this study also has some limitations. First, although we used a time-
lagged design, as compared to cross-sectional designs used by prior research (e.g.,
Strzelecki, 2023), we could not completely rule out the possibility of reciprocal rela-
tionships. For example, it is also possible that ChatGPT usage may also help lessen
the subsequent perceptions of workload. Future research may examine these causal

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Abbasetal. Int J Educ Technol High Educ (2024) 21:10
mechanisms using a longitudinal design. Second, in order to provide a deeper under-
standing of generative AI usage, future studies may examine how personality factors,
such as trust propensity and the Big Five personality traits, relate to ChatGPT usage.
Also, an understanding of how these traits shape perceptions of ChatGPT’s reliability,
trustworthiness, and effectiveness may shed light on the dynamics of user-machine
interactions in the context of generative AI.
Moreover, our finding regarding the insignificant effect of quality consciousness on
ChatGPT usage warrants further investigation. While some quality conscious stu-
dents might consider personal effort as a condition to produce quality work, other
quality conscious individuals might believe that ChatGPT can help achieve quality
in academic tasks. Perhaps, some contextual moderators (e.g., propensity to trust
generative AI) may play their role in determining the effects of quality conscious-
ness on ChatGPT usage. In the same vein, fear of punishment may also discourage
the use of ChatGPT for plagiarism. As noted by an anonymous reviewer, future stud-
ies may probe the benefits associated with the use of generative AI and also compare
the dynamics of ChatGPT usage across numerous fields of knowledge (e.g., computer
sciences, social sciences) or across gender to examine any differential effects. Finally,
future research may probe the effects of ChatGPT usage on students’ learning and
health outcomes. By investigating how ChatGPT usage impacts cognitive skills, men-
tal health, and learning experiences among students, researchers can contribute to
the growing discourse on the role of generative AI in higher education.
Acknowledgements
Not applicable.
Author contributions
MA contributed to the conceptualization of the idea, theoretical framework, methodology, analyses, and the writeup. FAJ
and TIK contributed to the data collection, methodology, analyses, and the write-up. All authors read and approved the
final manuscript.
Funding
There was no funding received from any institution for this study.
Availability of data and materials
The data associated with this research is available upon a reasonable request.
Declarations
Ethics approval and consent to participate
The research was explicitly approved by the ethical review committee of the authors’ university. All procedures per-
formed in studies involving human participants were in accordance with the ethical standards of the institution.
Informed consent
Participants were informed about the study’s procedures, risks, benefits, and other aspects before their participation.
Only those who gave their consent were allowed to participate in the research.
Competing interests
The authors declare that they do not have any competing interests.
Received: 14 September 2023 Accepted: 22 January 2024
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