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American Journal of  Applied 
Statistics and Economics (AJASE)

Analyzing University Students’ Attitude and Behavior Toward AI Using the Extended 
Unifi ed Theory of  Acceptance and Use of  Technology Model

Brandon Nacua Obenza1*, John Harry S. Caballo1, Ria Bianca R. Caangay2, Trisha Eunice C. Makigod1, Sharldawn M. Almocera1, John 
Lawrence M. Bayno1, Joseph Jr.  R. Camposano1, Sandy Jean G. Cena1, Judy Ann Kyll Garcia1, Bea Faye M. Labajo1, Athena Grace Tua1

Volume 3 Issue 1, Year 2024
ISSN: 2992-927X (Online)

DOI: https://doi.org/10.54536/ajase.v3i1.2510
https://journals.e-palli.com/home/index.php/ajase

Article Information ABSTRACT

Received: April 06, 2024

Accepted: May 09, 2024

Published: May 13, 2024

This quantitative study using Partial Least Square Structural Equation Modeling (PLS-SEM) 
examined a structural model of  the attitudes and behaviors of  university students toward AI 
in higher education. The results obtained using SmartPLS 4.0 indicate that the constructs 
exhibit validity and reliability (λ ≥ 0.708, α=0.767-0.948, AVE=0.584-0.777, HTMT=< 3.3). 
Further, the analysis of  the hypothesized extended Unifi ed Theory of  Acceptance and Use 
(UTAUT) model reveals that AI Awareness signifi cantly impacts Attitude toward AI (β = 
0.156, p = 0.003) and Behavioral Intention to Use AI (BIU) (β = 0.337, p < 0.001). AI Trust 
also signifi cantly infl uences Attitude toward AI (β = 0.366, p < 0.001) and BIU-AI (β = 0.173, 
p = 0.007). Additionally, Attitude toward AI is a strong predictor of  BIU-AI (β = 0.457, p 
< 0.001). Social Infl uence signifi cantly affects Attitude toward AI (β = 0.21, p < 0.001), 
while Effort Expectancy and Performance Expectancy do not show signifi cant effects in 
this context. The link between Facilitating Conditions and BIU-AI is also insignifi cant. The 
model explained a substantial portion of  the variance in attitude (R2 =0.612) and behavior 
(R2 =0.710). Fit indices indicate good model fi t, and predictive relevance metrics were 
satisfactory. 

Keywords

Attitude Toward Artifi cial 
Intelligence, Behavioral Toward 
AI, UTAUT Model, Partial 
Least Square Structural Equation 
Modeling (PLS-SEM), Philippines 

1 University of  Mindanao, Davao City, Philippines
2 Ateneo De Davao University, Davao City, Philippines
* Corresponding author’s e-mail: bobenza@umindanao.edu.ph

INTRODUCTION
Artifi cial Intelligence (AI), which automates tasks and 
emulates human intelligence (Geetha & Bhanu Sree 
Reddy, 2018; Jarrett & Choo, 2021; Khanagar et al., 2021; 
Saravanan et al., 2017), is rapidly growing (Barton et al., 
2017; Beig & Qasim, 2023; Hassani et al., 2020; Hilale, 
2021; Olhede & Wolfe, 2018). It has now become an 
important technology that benefi ts society and the 
economy (Cockburn et al., 2018; Hall & Pesenti, 2017; 
Lu et al., 2018) and pervades many aspects of  people’s 
daily lives (Hilale, 2021; Loble et al., 2017; Mintz & 
Brodie, 2019; Olhede & Wolfe, 2018; Tahiru, 2021). As 
AI has been prominent in various sectors (Berdiyorova 
et al., 2021; Hall & Pesenti, 2017; Jindal et al., 2021; Paul 
et al., 2021), this technology has also been integrated into 
the field of  higher education (Crompton & Burke, 2023; 
Pedro et al., 2019; Zawacki-Richter et al., 2019; Zhang 
& Aslan, 2021). The use of  Artifi cial Intelligence (AI) 
has been implemented in language learning to improve 
instruction and address the learners’ needs (Chen, 2021), 
facilitate interaction between instructors and learners (Seo 
et al., 2021), and foster learners’ educational experience 
(Alam, 2021; Kavitha & Lohani, 2018). Although 
artifi cial intelligence is thriving (Shao et al., 2020; Tan & 
Ran, 2022; Zhou et al., 2019), it is also seen as a threat 
to education (Humble & Mozelius, 2022; Xie & Wang, 
2023). Some college students were concerned with ethical 
issues (Farhi et al., 2023; Ghotbi et al., 2022) and were 
wary of  an unnatural learning environment (Kushmar 
et al., 2022). The majority of  college students have no 
intention of  using AI to complete assignments or exams 

in the near future (Welding, 2023), and according to Skeat 
and Ziebell (2023), a signifi cant number of  students still 
strongly oppose such technology utilization. In a similar 
study, it was also revealed that although the respondents 
understand the essence of  AI technology and how it 
benefi ts their daily lives, they are not entirely clear about 
the benefi ts of  incorporating artifi cial intelligence-
enhanced technologies in learning and teaching (Slavov 
et al., 2023). 
Prior studies explored people’s attitudes toward and 
behavioral intention to use artifi cial intelligence. In the 
study of  Yadrovskaia et al. (2023), the respondents have 
a positive attitude towards the use of  AI despite not 
fully grasping the fundamentals of  these technologies. 
Additionally, some students believe that AI will positively 
benefi t the fi eld of  education (Kairu, 2020; Marrone et 
al., 2022), and they also have a positive attitude toward 
using it because it engages students and accommodates 
their varying cognitive levels (Obenza et al., 2023b; 
Pande et al., 2020). These attitudes regarding AI affect 
people’s level of  trust in AI technology (Liehner et al., 
2023). Moreover, artifi cial intelligence, such as chatbots, 
is found appealing to language learners since they can 
use them without the teachers’ assistance, which helps 
them develop into independent learners (Mohamed & 
Alian, 2023). According to Chen et al. (2021), students’ 
behavioral intention to study a language was positively 
correlated with their knowledge of  AI-enabled language 
applications, attitude to use AI, perceived ease of  use, 
subjective norm, and behavioral intention. Romero-
Rodriguez et al. (2023) used the Unifi ed Theory of  



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Acceptance and Use of  Technology (UTAUT) model 
for technology adoption to fi nd that university students 
accept artifi cial intelligence like ChatGPT because they 
think it could help them learn. Usefulness, performance 
expectancy, hedonic motivation, private value, and habits 
also affect students’ AI chatbot prototype use.
Kim (2017) found using the UTAUT model that 
expectation, social infl uence, work usefulness, and 
anxiety signifi cantly affected healthcare university 
students’ intention to use AI technology. Kim (2017) 
found that the use intention factor partially mediated the 
direct effect of  the anxiety factor on the attitude factor 
and the task’s usefulness factor on the attitude factor 
after verifying the indirect effect. Kaya et al. (2022) also 
reported that AI anxiety is a problem that can hinder the 
adoption, use, or acceptance of  technology and can cause 
people to underestimate the usefulness of  AI technology, 
fail to recognize its simplicity and fail to recognize its 
benefi ts. Moreover, in the study of  Gado et al. (2022), the 
perceived usefulness of  AI, attitude towards AI, perceived 
social norm regarding AI, and AI literacy proved to be 
signifi cant indicators of  students’ intent to use artifi cial 
intelligence. Additionally, Alzahrani (2023) discovered 
that while students’ attitudes were adversely affected by 
perceived risk, their behavioral intention to utilize AI in 
education was signifi cantly infl uenced by performance 
expectancy and facilitating conditions. Additionally, 
the results indicate that effort expectation has no 
substantial effect on attitudes toward the use of  AI in 
higher education. In light of  the empirical investigations 
delineated within extant literature, the pivotal function 
of  AI literacy in shaping attitudes towards artifi cial 
intelligence (AI) emerges prominently. In a recent study 
conducted by Obenza et al. (2024), it was discerned that 
the cultivation of  cognitive absorption among students 
represents a viable strategy for enhancing AI literacy, 
given its established status as a signifi cant predictor 
thereof.
In addition to the above studies, UTAUT model has been 
used to study students’ adoption of  artifi cial intelligence-
enabled e-learning systems (Lin et al., 2021), intelligence-
based robots (Roy et al., 2022), and AI-powered web-
based English writing assistance software (Intiser et al., 
2023). However, despite studies and existing literature 
concerning people’s attitudes and behavioral intention 
to use AI, a particular study using UTAUT delving into 
AI trust and AI awareness in explaining and creating 
the structural model of  college students’ attitudes and 
behavior towards AI has been none. Therefore, this study 
is conducted to address this research gap. The results of  
this study can contribute specifi cally to academic sectors 
that are progressively integrating artifi cial intelligence in 
pedagogical approaches, as well as aid the technology 
sectors in enhancing AI tools that increase people’s 
positive view and adoption of  AI. This can also benefi t 
future researchers in further in-depth exploration of  
factors infl uencing the students’ attitude and behavioral 
intention to use artifi cial intelligence. 

MATERIALS AND METHODS
This study utilized a quantitative research design, and 
more specifi cally, the non-experimental correlational 
approach was utilized all throughout the research process. 
In accordance with the defi nition provided by Creswell 
and Creswell (2023), quantitative studies make use of  
inquiry methodologies such as surveys and experiments, 
and the data collected is gathered on predetermined 
instruments that generate statistical measurements.
Researchers distributed Google Forms survey 
questionnaires to random participants. A stratifi ed random 
sampling method was utilized to select participants for 
the study. Utilizing this method allowed for the study 
variables to be represented in an equitable manner. For 
latent variable path models, the estimation of  complex 
cause-effect relationships can be accomplished through 
the use of  partial least squares path modeling (PLS-PM) 
or structural equation modeling (PLS-EM). As PLS-SEM 
gains popularity, more researchers are using it (Hair et 
al., 2019a; Hair et al., 2017b; Ringle et al., 2015; Sarstedt 
et al., 2019b). Using PLS-SEM, researchers are able to 
estimate large models that contain multiple constructs, 
indicator variables, and structural paths without making 
any assumptions about distributional relationships.
More importantly, PLS-SEM emphasizes prediction in 
statistical model estimation and is designed to explain 
causality (Wold, 1982; Sarstedt et al., 2017a). This partial 
data analysis method allows for smaller sample sizes. For 
the purpose of  extrapolating sample results to the relevant 
population, larger sample sizes should be used whenever 
possible.(Hair et al., 2022b; Kock & Hadaya, 2018).
The researchers used adapted questionnaires in the 
form of  5-point Likert Scales to gather the data. The 
attitude toward AI scale (Suh & Ahn, 2022), facilitating 
condition, performance expectancy, effort expectancy, 
and behavioral intention to use scales (Chatterjee & 
Bhattacharjee, 2020), social infl uence scale (Kandoth & 
Shekhar, 2022), AI awareness scale (Isaac et al., 2017), and 
AI trust scale (Choung et al., 2022). 
The study applied the 10-times rule proposed by Hair 
et al. (2011) to determine the total number of  samples 
gathered. This method is commonly utilized in PLS-SEM 
to determine the minimal sample size. This strategy relies 
on the premise that the sample size must exceed ten times 
the highest number of  inner or outer model linkages 
directed at any latent variable in the model (Hair et al., 
2017).The minimal sample size computed based on this 
criteria is 90. The study selected 322 college students from 
different universities in Region XI by stratifi ed random 
sampling, exceeding the recommended sample size to 
ensure accurate results, particularly as we are suggesting 
a concise model. 
A further evaluation of  the validity and reliability of  the 
measurement model was carried out using Cronbach’s 
alpha. The method known as the Average Variance 
Extracted (AVE) was utilized in order to assess the 
convergent validity of  the model. On the other hand, 
the Hetero-Monotrait Ratio (HTMT) method was 



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utilized in order to assess the discriminant validity of  the 
model. The VIF was also utilized in this process. For the 
purpose of  evaluating the hypothesized structural model, 
the bootstrapping standardized algorithm was utilized 
through the SmarPLS 4.0 software.

RESULTS AND DISCUSSION
Assessment of  Measurement Model 
Cronbach alpha and composite reliability are the two 
measurements that are most frequently used to determine 
internal consistency. These measurements determine 
reliability based on the interrelationship of  the variables 
that are observed in the items (Hamid et al., 2017). The 
reliability of  the instruments that were utilized in the 
research is presented in Table 1. It was determined that 
Cronbach’s alpha was the most reliable method for 
evaluating the instruments. Cronbach’s alpha values for 
the questionnaires are as follows: 0.854 for AI Awareness 
(AI-A), 0.937 for AI Trust (AI-T), 0.951 for Attitude 
towards AI (At-AI), 0.904 for Behavioral Intention to 
Use (BIU), 0.767 for Effort Expectancy (EE), 0.864 

for Facilitating Conditions (FC), 0.873 for Performance 
Expectancy (PE), and 0.896 for Social Infl uence. These 
values indicate that the questionnaires have a high degree 
of  internal consistency (SI). Composite reliability and 
Cronbach alpha values that fall within the range of  0.60 
to 0.70 are considered acceptable; however, in the more 
advanced stage, the value absolutely must be greater than 
0.70. (Hair et al., 2014).
The evaluation of  the instruments’ convergent validity was 
conducted by calculating the AVE. Convergent validity is 
the degree of  agreement regarding the correlation between 
multiple indicators of  the same construct (Hamid et al., 
2017). BIU (0.777), AI-A (0.696), AI-T (0.613), SI (0.766), 
EE (0.682), FC (0.647), and PE (0.664) all had AVE values 
that surpassed the 0.5 threshold. This is deemed acceptable 
in light of  the fact that an acceptable minimum acceptable 
AVE is 0.50. An AVE value of  0.50 or greater signifi es 
that the construct accounts for a minimum of  50 percent 
of  the variance exhibited by the items comprising the 
construct (Bagozzi & Yi, 1988; Fornell & Larcker, 1981; 
Hair et al., 2014; Henseler et al., 2009).

Table 1: Construct Reliability and Validity
Cronbach’s Alpha Composite Reliability (rho_a) Average Variance Extracted (AVE)

AI-A 0.854 0.855 0.696
AI-T 0.937 0.937 0.613
At-AI 0.951 0.952 0.584
BIU 0.904 0.906 0.777
EE 0.767 0.767 0.682
FC 0.864 0.869 0.647
PE 0.873 0.878 0.664
SI 0.896 0.899 0.766

Table 2: Heterotrait-Monotrait Ratio (HTMT)
AI-A AI-T At-AI BIU EE FC PE SI

AI-A
AI-T 0.723
At-AI 0.691 0.742
BIU 0.824 0.762 0.834
EE  0.709 0.573 0.645 0.611
FC 0.519 0.517 0.566 0.497 0.570
PE 0.682 0.638 0.662 0.650 0.823 0.725
SI 0.623 0.649 0.682 0.668 0.662 0.570 0.654

The next test employed was the HTMT values. This test 
evaluated the discriminant validity of  the scales which 
pertains to the extent to which the items discriminate from 
one another empirically (Hamid et al., 2017). The HTMT 
ratios of  the constructs spans between 0.50 to 0.60 in the 
following construct pairs: AI-A and AI-T (0.723), AI-T 
and At-AI (0.742), At-AI and BIU (0.834), BIU and EE 
(0.611), EE to FC (0.570), FC and PE (0.725), PE and SI 
(0.654), AI-A and At-AI (0.691), AI-T and BIU (0.762), 
At-AI and EE (0.645),  BIU and FC (0.497), EE and PE 
(0.823), FC and SI (0.570), AI-A and BIU (0.824), AI-T 

and EE (0.573), BIU and PE (0.650), EE and SI (0.662), 
AI-A and EE (0.709), AI-T and FC (0.517), At-AI and 
PE (0.662), BIU and SI (0.668), AI-A and FC (0.519), 
AI-T and PE (0.638), At-AI and SI (0.682), AI-A and PE 
(0.682), AI-T and SI (0.649), and AI-A and SI (0.623). The 
fact that all ratios are lower than the threshold of  0.85 
demonstrates that there is strong discriminant validity 
between the constructs (Kline, 2011). Additionally, Gold 
et al. (2001) suggested that a value of  0.90 should be 
proposed as the threshold.



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Prior to evaluating the structural relationships, it is 
necessary to investigate the collinearity in order to 
guarantee that it does not introduce any bias into the 
regression results. VIF values that are greater than fi ve 
are indicative of  probable collinearity issues among 
the predictor constructs, as stated by Hair et al. (2019). 
However, collinearity issues can also occur at lower 
VIF values, which range from three to fi ve (Mason & 
Perreault, 1991; Becker et al., 2014). In an ideal situation, 
the values of  the VIF should be close to three or lower. 
The creation of  higher-order models that are capable of  
being supported by theory is a common solution that is 
utilized in situations where collinearity is a problem (Hair 
et al., 2017b).

association both with Attitude toward AI (coeffi cient = 
0.366) and Behavioral Intention to Use (coeffi cient = 
0.173). These fi ndings suggest that as students’ trust in 
AI increases, so does their favorable attitude toward it, 
as well as their inclination to use AI technology. These 
relationships are statistically signifi cant, underscoring the 
signifi cance of  trust in shaping attitudes and intentions 
toward AI adoption (T = 5.621, p = 0.000 and T = 
2.679, p = 0.007, respectively). The results of  this study 
lend credence to the fi ndings of  Obenza et al. (2023a), 
which indicated that trust in artifi cial intelligence had a 
signifi cant impact on attitudes toward AI.
The fi ndings are also consistent with the assertions 
made by Choung et al. (2022), who stated that trust acts 
as a precursor to positive attitudes, which in turn affects 
usage intentions. The level of  trust that an individual 
has in artifi cial intelligence (AI) is a signifi cant factor in 
determining their attitude toward AI technology as well as 
their willingness to interact with it, as stated by Schepman 
and Rodway (2023). Furthermore, the fi ndings of  the 
research carried out by Emon et al. (2023) and Cook 
(2023) demonstrated that trust in artifi cial intelligence 
(AI) plays a signifi cant part in determining whether or 
not an individual intends to make use of  using it.
The path from Attitude toward AI to Behavioral 
Intention to Use exhibits a robust positive relationship 
(coeffi cient = 0.457). This implies that a positive attitude 
toward AI among students signifi cantly infl uences their 
intention to use it. This relationship is highly signifi cant 
(T = 8.597, p = 0.000), indicating its substantial impact. 
These fi ndings are consistent with multiple research that 
have demonstrated a substantial correlation between 
attitude toward AI and the intention to employ it (Hasan 
Emon, 2023; Saxena, 2023).
However, the paths from Effort Expectancy to Attitude 
toward AI and from Facilitating Conditions to Behavioral 
Intention to Use demonstrate weaker relationships, as 
evidenced by their non-signifi cant p-values (p > 0.05). 
The results of  this study suggest that the students’ 
perceptions of  the amount of  effort required to use 
artifi cial intelligence and the presence of  favorable 
conditions do not signifi cantly impact the attitudes and 
intentions of  the students regarding the adoption of  
AI in this particular environment. These fi ndings are in 
direct opposition to the fi ndings of  other research such 
those of  Hasan Emon and Alzahrani (2023) that have 
demonstrated that enabling environments have an effect 
on the intention to engage in certain behaviors. 
Similarly, while Performance Expectancy exhibits a 
positive relationship with Attitude toward AI, the 
relationship is not statistically signifi cant (T = 1.711, 
p = 0.087), indicating that perceptions regarding the 
performance benefi ts of  AI may not strongly infl uence 
attitudes toward AI among university students. According 
to Alzahrani (2023), performance expectancy signifi cantly 
infl uences students’ attitudes toward AI. 
Finally, Social Infl uence proves to be a substantial 
indicator of  Attitude towards AI, with a coeffi cient of  

Table 3: Variance Infl ation Factor (VIF) 
VIF

AI Awareness -> Attitude toward AI 2.107
AI Awareness -> Behavioral Intention to Use 1.959
AI Trust -> AI Awareness 1.000
AI Trust -> Attitude toward AI 2.114
AI Trust -> Behavioral Intention to Use 2.342
Attitude toward AI -> Behavioral Intention to 
Use

2.343

Effort Expectancy -> Attitude toward AI 2.096
Facilitating Conditions -> Behavioral Intention 
to Use

1.441

Performance Expectancy -> Attitude toward AI 2.329
Social Infl uence -> Attitude toward AI 1.891

Assessment of  Structural Model
Examining the path from AI-Awareness to Attitude 
toward AI, the coeffi cient of  0.156 indicates a positive 
relationship. This suggests that as students’ awareness of  
AI increases, their attitude toward AI tends to become 
more positive. 
These fi ndings indicate that as students’ knowledge of  
AI grows, their perception of  AI tends to become more 
favorable. The statistical analysis reveals a signifi cant 
connection (T = 3.004, p = 0.003), emphasizing its 
signifi cance. Similarly, the association between AI 
Awareness and Behavioral Intention to Use is larger, with 
a value of  0.337.
This implies that higher levels of  AI awareness among 
students are associated with a greater intention to use 
AI technology. This relationship is not only statistically 
signifi cant but also notably stronger (T = 6.009, p = 
0.000). This corroborates the fi ndings of  Marrone et 
al. (2022), who discovered that students with a greater 
comprehension of  AI expressed more favorable 
attitudes toward incorporating AI into their educational 
environments. Students with limited comprehension 
of  AI exhibited a tendency to experience apprehension 
towards AI.
Moving on to AI Trust, the results indicate a positive 



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0.21. Consequently, the impact of  classmates, professors, 
or societal norms is crucial in molding students’ attitudes 
toward AI. The statistical analysis reveals a signifi cant 

association (T = 4.051, p = 0.000), indicating that social 
variables play a crucial role in shaping attitudes towards 
the adoption of  AI.

Figure 1: Partial least squares structural equation modeling (PLS-SEM) Results using Smart PLS 4.0 

Table 3 presents various statistical measures used to 
evaluate the fi t and predictive power of  a structural 
equation model (SEM) that aims to analyze university 
students’ attitudes and behavior toward AI using the 
UTAUT model. The Bayesian Information Criterion 
(BIC) values of  both endogenous variables, ‘At-AI’ and 
‘BIU,’ have negative BIC values, suggesting a good fi t. 
Lower and Negative BIC values can occur and generally 
indicate a very strong model according to the likelihood 
function.
The R-squared (R²) and Adjusted R² values represent the 
proportion of  variance explained by the model. For At-
AI, 61.2% of  the variance is explained, and for BIU, 71.0% 
is explained. The high values of  both R² and adjusted 
R² indicate a strong model. The  Predictive Relevance 
(Q²) value shows the model’s predictive relevance. A 
value larger than zero suggests the model has predictive 
relevance for the construct. Both of  both endogenous 
variables, At-AI and BIU, show values well above zero, 
indicating good predictive power.
The Root Mean Square Error (RMSE) and Mean Absolute 
Error (MAE) are measurements of  the average error that 
occurs between the predicted and observed values. Lower 
values are preferable because they demonstrate that the 
model’s predictions are relatively close to the values 
that actually occur. Because the values of  the model are 

relatively low, it appears that the model is able to make 
accurate predictions.The Standardized Root Mean Square 
Residual (SRMR) is a measure of  fi t used in structural 
equation models. Values less than 0.08 are generally 
considered good. The model shows SRMR values close 
to this threshold, suggesting an acceptable fi t.
The Unweighted Least Squares discrepancy (d_ULS) and 
Geodesic discrepancy (d_G) are discrepancy functions 
based on unweighted least squares and geodesic distances. 
The smaller these values, the better the model fi t. Based 
on the fact that the values of  the saturated model, which 
is the most complex model, and the estimated model, 
which is the proposed model, are relatively close to one 
another, it is possible to draw the conclusion that the 
proposed model fi ts almost as well as the most complex 
model that is possible. Utilizing the Chi-square statistic, 
one can perform an analysis of  the disparity that exists 
between the covariance matrices that were observed 
and those that were anticipated. However, a lower value 
indicates a better fi t between the data and the model. 
The chi-square value is sensitive to the sample size; yet, a 
lower value indicates a better fi t. However, this should be 
interpreted in the context of  other fi t indices and sample 
sizes. The model has a high chi-square value, which may 
indicate that it does not fi t the data well. However, this 
should be taken into consideration.



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To determine how well the estimated model fi ts the data, 
the Normed Fit Index (NFI) compares it to a null model. 
This comparison is made in order to determine how well 
the model fi ts the data. A more satisfactory fi t is indicated 
by values that are closer to the number one. Each of  the 

models has an NFI of  approximately 0.72, which is lower 
than the threshold of  0.95 that is typically recommended. 
This indicates that there is room for improvement in the 
model.

Table 4: Model Fit
Endogenous Variables BIC  R2 Adjusted R2 Q2 predict RMSE MAE
Attitude toward AI -271.195 0.612 0.606 0.588 0.646 0.450
Behavioral Intention to Use -370.864 0.710 0.706 0.622 0.619 0.432

Saturated model Estimated model
SRMR 0.059 0.061
d_ULS 5.221 5.308
d_G 2.501 2.506
Chi-square 4293.77 4300.13
NFI 0.723 0.722

CONCLUSION
Based on the UTAUT model and actual results, this study’s 
theoretical implications reveal a complex understanding 
of  university students’ views and behavior toward artifi cial 
intelligence (AI). This section examines how the fi ndings 
correlate with and extend the UTAUT model, providing 
insights into the elements that infl uence AI adoption in 
an academic setting. The positive relationship between AI 
awareness and students’ attitudes and behavioral intentions 
to use AI underscores the critical role of  knowledge and 
exposure in shaping perceptions of  technology. This 
fi nding is consistent with the UTAUT model’s emphasis 
on performance expectancy and effort expectancy as 
signifi cant factors of  technology adoption, suggesting 
that improved awareness can reduce perceived efforts 
and improve performance expectations (Venkatesh et al., 
2016). Furthermore, the signifi cant impact of  AI trust 
on both attitude and behavioral intention emphasizes the 
importance of  credibility and reliability in the adoption 
process, which is consistent with the model’s suggestion 
that social infl uence and enabling conditions are critical in 
technology acceptance.
However, the weaker/non-signifi cant connections 
between effort expectancy and facilitating conditions with 
attitude and behavioral intention, respectively, call into 
question the UTAUT model’s assertions about student 
AI adoption. This shows that other factors, presumably 
related to AI technology, such as ethical issues or the type 
of  AI applications, may have a greater impact on students’ 
views and intents. The robust association between attitude 
toward AI and behavioral intention to utilize AI further 
reinforces the main premise of  the UTAUT model: 
good attitudes toward technology greatly contribute to 
its acceptance and utilization (Venkatesh & Davis, 2000; 
Venkatesh et al., 2003). This suggests a direct channel for 
educators and policymakers to infl uence AI adoption 
by instilling a positive attitude towards AI in students. 
The signifi cant predictive power of  social infl uence on 
attitude toward AI emphasizes the model’s assertion 

that social factors are crucial in technology adoption 
(Venkatesh et al., 2003). This underscores the need for 
educational institutions to foster a culture that supports 
and encourages AI learning and exploration. In light of  
these fi ndings,  this study expands upon the UTAUT 
model by highlighting the subtle impacts of  AI awareness, 
trust, and social infl uence on university students’ views 
and actions toward AI. It implies that although the basic 
elements of  the UTAUT model are still applicable, the 
distinct features of  AI technology and its specifi c usage 
in educational environments require modifi cations to the 
model in order to understand the process of  AI adoption 
in educational settings comprehensively. 

RECOMMENDATIONS 
The fi ndings initiate a discourse regarding the strategic 
emphasis of  educational programs and interventions. The 
primary focus of  efforts to improve AI acceptance should 
be on establishing trust and promoting awareness while 
also creating an environment that supports good social 
infl uence. Future research should investigate the specifi c 
reasons why performance and effort expectancy are not 
signifi cant and further examine how social infl uence 
works in the acceptability of  technology in educational 
contexts. The correlation between AI Awareness and the 
inclination to utilize AI indicates a pressing requirement 
for educational initiatives focused on enhancing AI 
literacy among students. This entails instructing not 
only the technical facets of  AI but also its ethical, social, 
and practical ramifi cations. Integrating AI subjects into 
the curriculum, covering fundamental principles to 
sophisticated applications, can cultivate a better-informed 
and favorable disposition towards AI technologies. The 
impact of  AI Trust on attitude, while not directly on 
behavioral intention, suggests that trust plays a crucial 
role in shaping favorable views, but it may not be 
enough to solely drive actual usage. Hence, establishing 
confi dence should be a comprehensive undertaking, 
encompassing not just the dependability and openness 



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Am. J. Appl. Stat. Econ. 3(1) 99-108, 2024

of  AI but also addressing students’ apprehensions and 
misunderstandings around AI. Given the increasing 
importance of  AI across different industries, providing 
students with education on AI equips them with 
the necessary skills for future employment. Gaining 
profi ciency in AI will be an essential aptitude, and early 
familiarity can provide pupils with a distinct advantage. 

LIMITATIONS AND FUTURE RESEARCH 
DIRECTIONS
Although this study offers valuable information, it 
does have limits. Further inquiry is needed to explore 
additional moderating or mediating variables due to 
the lack of  a signifi cant association between most 
dimensions of  the UTAUT model and At-AI and BIU. 
Subsequent investigations could examine how elements 
such as AI effi cacy, aspects of  TAM (Technology 
Acceptance Model), AI ethics, or specifi c AI features 
infl uence students’ attitudes and behavioral intentions. 
The swift advancement of  AI technology may surpass 
the conclusions of  the study, thus requiring ongoing 
research. Furthermore, longitudinal studies have the 
potential to offer a more profound comprehension of  
the progression of  students’ perspectives and intentions 
as they acquire increased familiarity with AI technologies. 
Subsequent investigations ought to overcome these 
constraints by broadening the range of  participants, 
consistently incorporating the most recent advancements 
in artifi cial intelligence, and potentially integrating 
supplementary constructs, theories, and methodologies 
to enhance comprehension of  students’ attitudes and 
behaviors toward technology acceptance. 

Acknowledgments
The researchers are grateful to and dedicate this research 
to the Almighty God.  

REFERENCES
Alam, A. (2021, December). Should robots replace 

teachers? Mobilisation of  AI and learning analytics 
in education. In 2021 International Conference on 
Advances in Computing, Communication, and Control 
(ICAC3) (pp. 1-12). IEEE. https://doi.org/10.1109/
ICAC353642.2021.9697300.

Alzahrani, L. (2023). Analyzing students’ attitudes and 
behavior toward artifi cial intelligence technologies 
in higher education. International Journal of  Recent 
Technology and Engineering (IJRTE), 11(6), 65-73.

Bagozzi, R. P., & Yi, Y. (1988). On the evaluation of  
structural equation models. Journal of  the Academy of  
Marketing Science, 16, 74–94.

Bagozzi, R. P., & Yi, Y. (1988). On the evaluation of  
structural equation models. Journal of  the Academy of  
Marketing Science, 16, 74–94.

Barton, D., Woetzel, J., Seong, J., & Tian, Q. (2017). Artifi cial 
intelligence: Implications for China (Discussion 
Paper). Retrieved from McKinsey&Company 
website: http://dln.jaipuria.ac.in:8080/jspui/

bitstream/123456789/1888/1/MGI-Artificial-
intelligence-implications-for-China.pdf

 Becker, J., Ringle, C. M., Sarstedt, M., & Völckner, F. 
(2014). How collinearity affects mixture regression 
results. Marketing Letters, 26(4), 643–659. https://doi.
org/10.1007/s11002-014-9299-9

Beig, S., & Qasim, S. H. (2023). Attitude towards 
artifi cial intelligence: Change in the educational 
era.   International Journal of  Creative Research Thoughts, 
11(8), 718–b721. http://ijcrt.org/viewfull.php?&p_
id=IJCRT2308192

Berdiyorova, I., Akhtamova, P., & Ganiev, I.M. 
(2021). Artifi cial intelligence in various industries. 
Proceedings from International scientifi c-practical 
conference, 2021 March 25-26 (pp. 186-193). 

Chatterjee, S., & Bhattacharjee, K. K. (2020). Adoption of  
artifi cial intelligence in higher education: a quantitative 
analysis using structural equation modeling. Education 
and Information Technologies, 25(5), 3443–3463. https://
doi.org/10.1007/s10639-020-10159-7

Chen, M. Siu-Yung, M., Chai, C.S., Zheng, C., & Park, M.Y. 
(2021). A pilot study of  students’ behavioral intention 
to use AI for language learning in higher education.
Proceedings of  International Symposium on Educational 
Technology (ISET) Tokai Nagoya Japan (pp. 182-184). 
https://doi.org/10.1109/ISET52350.2021.00045.

Choung, H., David, P., & Ross, A. (2022). Trust in AI 
and its role in the acceptance of  AI technologies. 
International Journal of  Human-Computer Interaction, 
39(9), 1727–1739. https://doi.org/10.1080/1044731
8.2022.2050543

Cockburn, I.M., Henderson, R., & Stern, S. (2018). 
The impact of  artifi cial intelligence on innovation 
(Working Paper 24449). https://www.nber.org/
system/fi les/working_papers/w24449/w24449.pdf

Cook, J. (2023, November 21). How can we make AI 
adoption more human? https://www.linkedin.com/
pulse/how-can-we-make-ai-adoption-more-human-
jo-cook-zl0sf/

Creswell, J. W., & Creswell, J. D. (2023). Research design: 
“Qualitative, Quantitative, and Mixed Methods 
Approaches” (6th ed.). SAGE Publications.

Crompton, H., & Burke, D. (2023). Artifi cial intelligence 
in higher education: the state of  the fi eld. International 
Journal of  Educational Technology in Higher Education, 20 
(22). https://doi.org/10.1186/s41239-023-00392-8

Emon, M. M. H., Hassan, F., Nahid, M. H., & 
Rattanawiboonsom, V. (2023). Predicting Adoption 
Intention of  Artifi cial Intelligence CHATGPT. The 
AIUB Journal of  Science and Engineering, 22(2), 189–199. 
https://doi.org/10.53799/ajse.v22i2.797

Farhi, F., Jeljeli, R., Aburezeq, I., Dweikat, F.F., Al-shami, 
S.A. & Slamene, R. (2023). Analyzing the students’ 
views, concerns, and perceived ethics about chat GPT 
usage. Computers and Education: Artifi cial Intelligence, 5,
1-8.  https://doi.org/10.1016/j.caeai.2023.100180.

Fornell CG and Larcker DF. (1981) Evaluating Structural 
Equation Models with Unobservable Variables and 



Pa
ge

 
10

6

https://journals.e-palli.com/home/index.php/ajase

Am. J. Appl. Stat. Econ. 3(1) 99-108, 2024

Measurement Error. Journal of  Marketing Research 
18(1), 39-50. 

Gado, S., Kempen, R., Lingelbach, K., & Bipp, T. (2022). 
Artifi cial intelligence in psychology: How can we 
enable psychology students to accept and use artifi cial 
intelligence? Psychology Learning & Teaching, 21(1), 37-
56. https://doi.org/10.1177/14757257211037149

Geetha, R. & BhanuSree Reddy, D. (2018). Recruitment 
through artifi cial intelligence: A conceptual study.
International Journal of  Mechanical Engineering and Technology, 
9(7), 63–70.  http://www.iaeme.com/IJMET/issues.
asp?JType=IJMET&VType=9&IType=7

Ghotbi, N., Ho, T., & Mantello, P. (2022). The attitude 
of  college students towards ethical issues of  artifi cial 
intelligence in an international university in Japan. 
AI & Society, 37(2), 1-8. https://doi.org/10.1007/
s00146-021-01168-2

Gold, A. H., & Malhotra, A. H. (2001). Title of  the article.
Journal of  Management Information Systems, 18, 185-214.

Hair JF, Hult GTM, Ringle CM, et al. (2017a) A Primer on 
Partial Least Squares Structural Equation Modeling 
(PLS-SEM), Thousand Oaks, CA: Sage. 

Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. 
(2019). When to use and how to report the results of  PLS-
SEM. European Business Review, 31(1), 2–24. https://
doi.org//10.1108/EBR-11-2018-0203

Hair, J., Hult, G. T. M., Ringle, C., & Sarstedt, M. 
(2014). A Primer on Partial Least Squares Structural 
Equation Modeling (PLS-SEM). Los Angeles: SAGE 
Publications, Incorporated.

Hair, J., Hult, G. T. M., Ringle, C., & Sarstedt, M. 
(2014). A Primer on Partial Least Squares Structural 
Equation Modeling (PLS-SEM). Los Angeles: SAGE 
Publications, Incorporated.

Hall, J. & Pesenti, J. (2017). Growing the artifi cial 
intelligence industry in the UK. GOV.UK. https://
www.gov.uk/government/publications/growing-the-
artifi cial-intelligence-industry-in-the-uk

Hamid, M. R. A., Sami, W., & Sidek, M. H. M. (2017). 
Discriminant Validity Assessment: Use of  Fornell 
& Larcker criterion versus HTMT Criterion. Journal 
of  Physics: Conference Series, 890, 012163. https://doi.
org/10.1088/1742-6596/890/1/012163

Hassani, H., Silva, E.S., Unger, S., Ta jMazinani, M., & 
Mac Feely, S. (2020). Artifi cial intelligence (AI) or 
intelligence augmentation (IA): What is the future? 
AI 2020, pp. 1, 143–155. https://doi.org/10.3390/
ai1020008

Henseler, J., Ringle, C. M., & Sinkovics, R. R. (2009). 
The use of  partial least squares path modeling in 
international marketing. Journal of  the Academy of  
Marketing Science, 20, 227–319.

Hilale, N. (2021). The evolution of  artifi cial intelligence 
(AI) and its impact on women: how it nurtures 
discrimination towards women and strengthens 
gender inequality. International Journal of  Human Rights, 
1(2), 141–150. http://www.humanrights.periodikos.
com.br/article/61489565a9539526b5418543

Hulland, J. (1999). Title of  the Article. Strategic 
Management Journal, pp. 20, 195–204.

Humble, N., Mozelius, P. The threat, hype, and promise 
of  artifi cial intelligence in education. Discover Artifi cial 
Intelligence, 2(22).   https://doi.org/10.1007/s44163-
022-00039-z

Intiser, R., Nahid, M. H., Anwar, M. A., & Nahar, R. 
(2023). Adoption of  AI-powered web-based English 
writing assistance software: An Exploratory Study. 
AIUB Journal of  Business and Economics, 20(1), 90–
101. https://ajbe.aiub.edu/index.php/ajbe/article/
view/194

Isaac, O., Abdullah, Z., Ramayah, T. and Mutahar, A.M. 
(2017). “Internet usage, user satisfaction, task-
technology fi t, and performance impact among public 
sector employees in Yemen,” International Journal of  
Information and Learning Technology, 34(3), 210–241. 
https://doi.org/10.1108/IJILT-11-2016-0051

Jarrett, A, Choo, K.-K. (2021). The impact of  automation 
and artifi cial intelligence on digital forensics. WIREs 
Forensic Science, pp. 1–17.  https://doi.org/10.1002/
wfs2.1418

Jindal, H., Kumar, D., Ishika, Kumar, S., & Kumar, R. 
(2021). Role of  artifi cial intelligence in the distinct 
sector: a study. Asian Journal of  Computer Science and 
Technology, 10(1), 18–28. https://doi.org/10.51983/
ajcst-2021.10.1.2696

Kairu, C. (2020). Students’ attitude towards the use of  
artifi cial intelligence and machine learning to measure 
classroom engagement activities. In Association for the 
Advancement of  Computing in Education (AACE) (Ed.). 
Proceedings of  EdMedia + Innovate Learning, 23 
June 2020 (pp. 793–802).  https://www.learntechlib.
org/primary/p/217382/.

Kandoth, S. ., & Kushe Shekhar, S. . (2022). Social 
infl uence and intention to use AI: the role of  personal 
innovativeness and perceived trust using the parallel 
mediation model. Forum Scientiae Oeconomia, 10(3), 
131–150. https://doi.org/10.23762/FSO_VOL10_
NO3_7

Kavitha, V., & Lohani, R. (2019). A critical study on the 
use of  artifi cial intelligence, e-learning technology, 
and tools to enhance the learner’s experience. Cluster 
Computing, 22, 6985–6989. https://doi.org/10.1007/
s10586-018-2017-2

Kaya, F., Aydin, F., Schepman, A., Rodway, P., Yetişensoy, 
O., & Demir Kaya, M. (2022). The roles of  personality 
traits, AI anxiety, and demographic factors in attitudes 
towards artifi cial intelligence. International Journal of  
Human–Computer Interaction. https:// doi.org/10.1080
/10447318.2022.2151730

Khanagar, S., Al-ehaideb A., Maganur, P., Vishwanathaiah, 
S., Patil, S., Baeshen, H., S., S., & Bhandi, S. (2021). 
Developments, application, and performance of  
artifi cial intelligence in dentistry – A systematic review.
Journal of  Dental Sciences, 16(1), 508–522. https://doi.
org/10.1016/j.jds.2020.06.019.

Kim, J. M. (2017). Study on intention and attitude of  



Pa
ge

 
10

7

https://journals.e-palli.com/home/index.php/ajase

Am. J. Appl. Stat. Econ. 3(1) 99-108, 2024

using artifi cial intelligence technology in healthcare.
Journal of  Convergence for Information Technology, 7(4), 53-
60. https://doi.org/10.22156/CS4SMB.2017.7.4.053

Kline, R. B. (2011). Principles and Practice of  Structural 
Equation Modeling (3rd ed.). New York: The 
Guilford Press.

Kock N and Hadaya P. (2018). Minimum Sample Size 
Estimation in PLS-SEM: The Inverse Square Root 
and Gamma-Exponential Methods. Information Systems 
Journal 28(1), 227- 261. 

Kushmar, L.V., Vornachev, A.O Korobova.I.O., & 
Kaida,N.O. (2022). Artifi cial Intelligence in Language 
Learning: What Are We Afraid of? Arab World 
English Journal (AWEJ) Special Issue on CALL, (8), 
262-273. https://dx.doi.org/10.24093/awej/call8.18 

Liehner, G.L., Biermann, H., & Hick, A., Brauner, P. & 
Ziefl e, M. (2023). Perceptions, attitudes, and trust 
towards artifi cial intelligence — an assessment of  the 
public opinion. Artifi cial Intelligence and Social Computing, 
72, 32–41. https://doi.org/10.54941/ahfe1003271

Lin, H.C., Ho, C.F., & Yang, H. (2021). Understanding 
the adoption of  artifi cial intelligence-enabled 
language e-learning system: an empirical study of  
UTAUT model. Home International Journal of  Mobile 
Learning and Organisation, 16(1), 79-94. https://
www.inderscienceonline.com/doi/epdf/10.1504/
IJMLO.2022.119966

Loble, L., Creenaune, T., & Hayes, J. (2017). Future 
frontiers education for an AI world. Melbourne 
University Press.

Lu, H., Li, Y., Chen, M., Kim, Hyoungseop, K., & 
Serikawa, S. (2017). Brain intelligence: Go beyond 
artifi cial intelligence. Mobile Networks and Applications, 
23, 368–375. https://doi.org/10.1007/s11036-017-
0932-8

Marrone, R., Taddeo, V., & Hill, G. (2022). Creativity and 
artifi cial intelligence—a student perspective. Journal 
of  Intelligence, 10(3), 1-11. https://doi.org/10.3390/
jintelligence10030065

Mason, C. H., & Perreault, W. D. (1991). Collinearity, 
power, and interpretation of  multiple regression 
analysis. Journal of  Marketing Research, 28(3), 268–280. 
(PDF) How Collinearity Affects Mixture Regression 
Results. 

Mintz, Y. & Brodie, R. (2019). Introduction to artifi cial 
intelligence in medicine. Minimally Invasive Therapy & 
Allied Technologies, 28(2), 73-81. https://doi.org/10.10
80/13645706.2019.1575882

Mohamed, S.S.A. & Alian, E.M.I. (2023). Students’ 
attitudes toward using a chatbot in EFL Learning. 
Arab World English Journal (AWEJ), 14(3), 15–27. 
https://dx.doi.org/10.24093/awej/vol14no3.2 

Obenza, B. N.,  Salvahan, A., Rios, A. N.,  Solo, A., Alburo, 
R. A., & Gabila, R. J. (2023b). University Students’ 
Perception and Use of  ChatGPT Generative Artifi cial 
Intelligence (AI) in Higher Education. International 
Journal of  Human Computing Studies, 5(12), 5–18. 
https://doi.org/10.5281/zenodo.10360697

Obenza, B. N., Baguio, J. S. I. E., Bardago, K. M. W., 
Granado, L. B., Loreco, K. C. A., Matugas, L. P., 
Talaboc, D. J., Zayas, R. K. D. D., Caballo, J. H. S., & 
Caangay, R. B. R. (2023a). The Mediating Effect of  AI 
Trust on AI Self-Effi cacy and Attitude Toward AI of  
College Students. International Journal of  Metaverse, 2(1), 
1–10. https://doi.org/10.54536/ijm.v2i1.2286

Obenza, B. N., Go, L. E., Francisco, J. A. M., Buit, E. E. 
T., Mariano, F. V. B., Cuizon Jr, H. L., Cagabhion, A. J. 
D., & Agbulos, K. A. J. L. (2024). The Nexus between 
Cognitive Absorption and AI Literacy of  College 
Students as Moderated by Sex. American Journal of  
Smart Technology and Solutions, 3(1), 32–39. https://doi.
org/10.54536/ajsts.v3i1.2603

Olhede, S. C., & Wolfe, P. J. (2018). The AI Spring of  2018. 
Signifi cance, 15(3), 6–7. https://doi.org/10.1111/
j.1740-9713.2018.01140.x

Pande, K., Sonawane, S., Jadhava, V., & Malia, M. (2023). 
Artifi cial intelligence: exploring the attitude of  
secondary students. Journal of  e-learning and knowledge 
society, 19(3), 43-48. https://www.je-lks.org/ojs/
index.php/Je-LKS_EN/article/view/1135865

Paul, D., Sanap, G., Shenoy, S., Kalyane, D.,  Kalia,  K., 
& Tekade,  R.K. (2021). Artifi cial intelligence in drug 
discovery and development. Drug Discov, 26(1), 80-93. 
doi: 10.1016/j.drudis.2020.10.010.

Pedró, F., Subosa, M., Rivas, A., & Valverde, P. (2019). 
Artifi cial intelligence in education: challenges and 
opportunities for sustainable development. United 
Nations Educational, Scientifi c and Cultural Organization, 
7, 1-48. https://unesdoc.unesco.org/ark:/48223/
pf0000366994

Ringle CM, Wende S and Becker J-M. (2015) SmartPLS 3. 
Bö nningstedt: SmartPLS.

Romero-Rodriguez, J.M., Ramirez-Montoya, M.S., 
Buenestado-Fernández, M. & Lara-Lara, F. (2023). 
Use of  ChatGPT at university as a tool for complex 
thinking: Students’ perceived usefulness. Journal of  
New Approaches in Education Research, 12(2), 323-339. 
https://doi.org/10.7821/naer.2023.7.1458

Roy, R., Babakerkhell, M.D., Mukherjee, S., Pal, D., 
& Funilkul, S. (2022). Evaluating the intention 
for the adoption of  artifi cial intelligence-based 
robots in the university to educate the students. 
IEEE Access, 10, 125666-125678. doi: 10.1109/
ACCESS.2022.3225555.

Saravanan, K., Sreedevi, E., & Subhamathi, V. (2017). A 
Review of  Artifi cial Intelligence Systems. International 
Journal of  Advanced Research in Computer Science, 8(9), 
418–421.  DOI 10.26483/ijarcs.v8i9.5095

Sarstedt M, Ringle CM and Hair JF. (2017a) Partial Least 
Squares Structural Equation Modeling. In: Homburg 
C, Klarmann M and Vomberg A (eds) Handbook of  
Market Research. Heidelberg: Springer. 

Sarstedt M, Ringle CM, Cheah J-H, et al. (2019b). 
Structural Model Robustness Checks in PLS-SEM. 
Tourism Economics is forthcoming. 

Schepman, A. & Rodway, P. (2023). The general attitude 



Pa
ge

 
10

8

https://journals.e-palli.com/home/index.php/ajase

Am. J. Appl. Stat. Econ. 3(1) 99-108, 2024

towards artifi cial intelligence scale (GAAIS): 
Confi rmatory validation and associations with 
personality, corporate distrust, and general trust. 
International Journal of  Human–Computer Interaction, 
39(13), 2724–2741. https://doi.org/10.1080/104473
18.2022.2085400

Seo, K., Tang, J., Roll, I., Fels, S., & Yong, D. (2021). The 
impact of  artifi cial intelligence on learner–instructor 
interaction in online learning. International Journal 
of  Educational Technology in Higher Education, 18 (54). 
https://doi.org/10.1186/s41239-021-00292-9

Shao, Z., Yuan, S., & Wang, Y., (2020). Institutional 
collaboration and competition in artifi cial 
intelligence. IEEE Access, 8, 69734-69741. https://
doi.org/10.1109/ACCESS.2020.2986383.

Skeat, J. & Ziebell, N. (2023). University students are 
using AI, but not how you think. The University of  
Melbourne. https://pursuit.unimelb.edu.au/articles/
university-students-are-using-ai-but-not-how-you-think

Slavov, V., Yotovska, K. & Asenova, A. (2023, March 11-
13). Research on the attitudes of  high school students 
toward the application of  artifi cial intelligence in 
education. 19th International Conference on Mobile 
Learning 2023, Lisbon Portugal. 

Suh, W., & Ahn, S. (2022). Development and validation 
of  a scale measuring student attitudes toward artifi cial 
intelligence. SAGE Open, 12(2), 215824402211004. 
https://doi.org/10.1177/21582440221100463

Tahiru, F. (2021). AI in education: A systematic literature 
review. Journal of  Cases on Information Technology (JCIT), 
23(1), 1–20. DOI: 10.4018/JCIT.2021010101

Tan, L. & Ran, N. (2022). Applying artifi cial intelligence 
technology to analyze the athletes’ training under 
a sports training monitoring system. International 
Journal of  Humanoid Robotics, 20(06). https://doi.org/ 
10.1142/S0219843622500177

Venkatesh, M., Davis, & Davis (2003). User Acceptance 
of  Information Technology: Toward a Unifi ed View. 
MIS Quarterly, 27(3), 425.

Venkatesh, V. & Davis, F.D. (2000). A Theoretical 

Extension of  the Technology Acceptance Model: 
Four Longitudinal Field Studies. Management Science, 
46(2), 186–204.

Venkatesh, V., Thong, J. & Xu, X. (2016). Unifi ed Theory 
of  Acceptance and Use of  Technology: A Synthesis 
and the Road Ahead. Journal of  the Association for 
Information Systems, 17(5), 328-376.

Welding, L. (2023). Half  of  college students say using AI 
on schoolwork is cheating or plagiarism. BestColleges. 
https://www.bestcolleges.com/research/college-
students-ai-tools-survey/

Wold HOA. (1982) Soft Modeling: The Basic Design and 
Some Extensions. In: Jö reskog KG and Wold HOA 
(eds) Systems Under Indirect Observations: Part II. 
Amsterdam: North- Holland, 1–54.

Xie, X. & Wang, T. (2023). Artifi cial intelligence: A help 
or threat to contemporary education. Should students 
be forced to think and do their tasks independently? 
Education and Information Technologies. https://
doi.org/10.1007/s10639-023-11947-7

Yadrovskaia, M., Porksheyan, M., Petrova, A., 
Dudukalova, D., & Bulygin, Y. (2023). About the 
attitude towards artifi cial intelligence technologies. 
E3S Web of  Conferences, 376, 05025. https://doi.
org/10.1051/e3sconf/202337605025

Zawacki-Richter, O., Marín, V.I., Bond, M. & Gouverneur, 
F. (2019). Systematic review of  research on artifi cial 
intelligence applications in higher education – where 
are the educators? International Journal of  Educational 
Technology in Higher Education, 16 (39). https://doi.
org/10.1186/s41239-019-0171-0

Zhang, K. & Aslan, A. B. (2021). AI technologies for 
education: Recent research & future directions. 
Computers and Education: Artifi cial Intelligence, 2. https://
doi.org/10.1016/j.caeai.2021.100025.

Zhou, Z., Chen, X., Li E., Zeng, L., Luo, K., & Zhang, 
J. (2019). Edge intelligence: Paving the last mile of  
artifi cial intelligence with edge computing. Proceedings 
of  the IEEE, 107(8), 1738-1762. https://doi.
org/10.1109/JPROC.2019.2918951


