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American Journal of  Smart 
Technology and Solutions (AJSTS)

The Nexus Between AI Self-Efficacy and Attitude Towards AI of  University Students in 
Davao City as Moderated by Sex 

Jerlan Anthony D. Guipitacio1*, Angelo Vincent B. Aleman1, Cleofe Margarette Bonsubre1, Jessie Mar T. Galleto1, 
Bruce Nolan B. Tapere1, John Harry Caballo1, Ria Bianca Caangay2

Volume 4 Issue 1, Year 2025
ISSN: 2837-0295 (Online)

DOI: https://doi.org/10.54536/ajsts.v4i1.3788
https://journals.e-palli.com/home/index.php/ajsts

Article Information ABSTRACT

Received: September 12, 2023

Accepted: October 16, 2024

Published: January 21, 2025

This study quantitatively explores how sex moderates the relationship between AI self-
efficacy and attitudes toward AI among university students in Davao City, Philippines. Data 
were obtained online via google forms using tailored questionnaires, with respondents 
chosen using stratified random sampling. The measurement model was tested for validity 
and reliability, and the constructs were defined using descriptive statistics. To evaluate the 
suggested moderation model, a moderation analysis was conducted using smartpls 4.0’s 
standard bootstrapping technique. The results showed that the constructs were valid and 
reliable, with university students exhibiting modest levels of  ai self-efficacy and attitude 
toward ai. Furthermore, the study found that sex had a significant moderating role in the 
relationship between AI self-efficacy and attitude toward AI.

Keywords
AI Self-Efficacy, Artificial 
Intelligence in Education, Attitudes 
Toward AI, University Students

1 University of  Mindanao, Davao City, Philippines
2 Ateneo De Davao University, Davao City, Philippines
* Corresponding author’s e-mail: j.guipitacio.548194@umindanao.edu.ph

INTRODUCTION 
Artificial Intelligence (AI) is a fast developing field that 
has become prevalent in modern life, impacting many 
facets of  society, including education. AI has improved 
learning outcomes and enhanced educational experiences 
through intelligent tutoring, automated assessments, and 
adaptive learning systems that offer customized feedback 
(Zawacki-Richter et al., 2019; Ahmad et al., 2021). 
Similar results from a research by Obenza et al. (2023) 
show that students are more likely to utilize ChatGPT 
as an educational supplement, particularly when they 
are enhancing their reading and writing skills. AI may 
modify curriculum to student performance, enhancing 
effectiveness and engagement. One example of  this 
is China’s Squirrel AI (Bourne, 2019). These findings 
suggest that generative AI is frequently seen positively 
in educational environments (Obenza et al., 2023). 
However, personal beliefs about AI and self-efficacy play 
a major role in the effective implementation and use of  
AI in education. Students who think favorably of  AI are 
more likely to use it, according to the strong behavioral 
intention association (Obenza et al., 2024). This study 
demonstrates that how students engage with AI may be 
influenced by their opinions about the technology. The 
degree of  confidence students have in their ability to 
interact with AI technology is determined by their AI 
self-efficacy, even though their attitudes toward AI in 
this context reflect their perceptions generally and their 
willingness to integrate AI into their learning activities 
(Chen et al., 2020; Dogan et al., 2023; Gligorea et al., 2023; 
Zawacki-Richter et al., 2019; Tang et al., 2021; Harry, 2023; 
Hashim et al., 2022; Hamal et al., 2022). Higher technical 
self-efficacy individuals feel they can discriminatively 
impact the results of  interactions by asserting control over 

automated technology use (Montag et al., 2023; Obenza- 
Tanudtanud & Obenza, 2024). Considering this, it is 
vital to investigate how such dynamics change between 
sexes. There may be minor differences between male and 
female students’ views regarding AI and self-efficacy, 
which are influenced by their upbringing. Studies indicate 
that females are less accepting as compared to males 
in the consideration of  AI. It is an explicit and implicit 
disparity, and specific interventions have been sought to 
help improve this area as well (Fietta et al., 2022).
Obenza et al. (2023) found that the concepts of  AI self-
efficacy, AI trust, and attitude toward AI are related to 
one another since they can all be used to predict one 
another quite well. All the hypotheses were confirmed, 
including the mediating hypothesis, which assumed a 
partial mediation to be performed by trust in AI between 
attitudes toward AI and self-efficacy on user acceptance 
to use AI. However, more research is needed because the 
previous study did not look at how sex could act as a 
moderating element in the relationship between AI self-
efficacy and views about AI. Moreover, recognizing these 
distinctions offers important context for understanding 
how AI technologies are being adjusted to better meet 
the individual requirements of  every student and 
enhance their involvement in educational environments. 
Therefore, this study aimed to assess university students’ 
views toward AI and their self-efficacy while accounting 
for the possible influence of  sex in Davao City.

Theoretical Framework
The Technology Acceptance Model (TAM), the Theory 
of  Planned Behavior (TPB), and self-efficacy are the 
three main theories that support this study and shed light 
on how college students view the application of  AI. The 



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Bandura 1977 self-efficacy theory was used to explore the 
extent of  student confidence in using AI tools (Gallagher, 
2012). Greater confidence means more AI engagement, 
which results in being more familiar with and skilled at 
any given technology and consequently more open to 
exploring AI applications. This is supplemented by the 
Technology Acceptance Model (TAM) proposed in 1989 
by Davis, which looks at two key concepts: the usefulness 
of  AI to students and its ease of  use (Charness & Boot, 
2016). The more people view AI as a helpful tool for their 
profession or studies and as something that is simple to 
use, the more positive their overall perception of  the 
technology is. Students are more likely to begin integrating 
AI technologies into their daily activities if  they are less 
complicated. Ajzen’s (1991) Theory of  Planned Behavior 
(TPB) takes one step further, describing how these 
attitudes, combined with impacts from self-efficacy and 
TAM, influence students’ actual usage of  AI, whether 
personally or professionally. TPB implies that if  students 
have AI efficacy and social considerations, they are more 
likely to use it. This refers to attitudes, ideas, and societal 
pressures that interact throughout the AI adoption 
process, according to this theory.
In summary, this study combines self-efficacy theory, 
the acceptance of  technology model, and the theory of  
planned behavior to provide a holistic understanding 
of  how university students perceive and interact with 
AI. Thereby, these theories explain how confidence, 
perceived usefulness, ease of  use, and social influences 
all come together to shape the attitudes of  a student 
toward AI. Educators and decision-makers may facilitate 
students’ adoption of  AI technology and ensure they 
possess the necessary knowledge and outlook to thrive 
in an increasingly AI-dependent world by being aware of  
these factors.

MATERIALS AND METHODS
This study employed the quantitative approach, that is, 
by using the non-experimental approach of  correlation 
in analyzing how sex impacts the relationship between 
AI self-efficacy and attitudes toward AI among university 
students in Davao City. According to Creswell and 
Creswell (2022), quantitative research is a systematic study 
that employs numerical data to answer research questions 
through statistical analysis of  the correlations between 
variables. This approach measures variables using tools, 
hence allowing the application of  statistical methods for 
data analysis. A moderating variable by Ramayah et al. 
(2017) was used to explain how the criterion affects the 
predictor’s effect. This element is essential when doing a 
detailed analysis of  the correlation between criteria and 
predictor variables. Even if  the MV does not affect the 
predictor, it may influence the strength and direction of  
relationships among the components.
The research instruments that were utilized and modified 
were the AI self-efficacy scale developed by Hong (2022) 
and the attitude toward the AI scale created by Suh and 
Ahn (2022).  These variables were measured using 5-point 

Likert scales (5 - Strongly Agree, 4 - Agree, 3 - Neutral, 
2 - Disagree, and 1 - Strongly Disagree). An online survey 
through Google Forms was conducted among the college 
students of  various programs across universities in Davao 
City, Philippines. The participants were 423 selected at 
random from each subdivision using stratified random 
sampling—the division of  populations into subgroups—
to ensure that every section or subgroup is represented. 
By implementing it into the study framework, mediation 
analysis was utilized to investigate how a mediating 
variable affects the connection between two other 
variables. Psychologists are using this method more and 
more, and it usually includes selecting participants at 
random (MacKinnon et al., 2007).
Average Variance Extracted (AVE) was applied to verify 
convergent validity, and Heterotrait-Monotrait Ratio 
(HTMT) confirmed discriminant validity as well as 
Cronbach’s alpha for internal consistency assurance of  
the measurement models. The self-efficacy and attitude 
toward AI descriptive statistics, such as mean and standard 
deviation, were calculated using Jamovi version 2.0. As a 
final step, the bootstrapping results through SmartPLS 
4.0 software verified the moderation effect of  sex in this 
path between self-efficacy and attitude toward AI.

Hypotheses
H0: There is no significant relationship between AI 

self-efficacy and attitude towards AI among university 
students in Davao City, and sex does not moderate this 
relationship.

H1: There is a significant relationship between AI 
self-efficacy and attitude towards AI among university 
students in Davao City, and this relationship is moderated 
by sex.

RESULTS AND DISCUSSION
For establishing internal consistency of  the variables, 
Cronbach’s alpha and composite reliability have been 
selected as major measures to be used in evaluating the 
data, as the reliability of  variables is determined by the 
interrelationship shown between the items, according to 
(Hamid et al., 2017). Table 1 shows the reliability of  the 
study’s instruments.
The artificial intelligence self-efficacy construct shows 
great internal consistency with Cronbach’s alpha value of  
0.875, considerably above the threshold of  0.70 (Wilson 
et al., 2018; Dzin and Lay, 2021; Kukul and Karatas, 
2019). Besides, its rho_c value was 0.902, further proving 
reliability, as the items possessed consistency in measuring 
the same underlying concept (Dzin and Lay, 2021; Kukul 
and Karatas, 2019). AVE = 0.536, which means that this 
construct explains more than 50% of  the variance in its 
indicators and thus strengthens the good convergent 
validity. Compared to this, the construct attitude towards 
AI shows more reliability with Cronbach’s alpha of  0.961 
and rho_c = 0.964. The AVE for this construct is 0.589, 
which shows high convergent validity as it explains a 
huge portion of  the variance in its indicators. Overall, 



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both constructs show very high internal consistency and 
reliability.
To further evaluate discriminant validity between the 
constructs under research, the heterotrait-monotrait ratio 
(HTMT), was used. The HTMT is useful in determining 
if  two latent constructs are sufficiently different from 
each other by comparing relationships between variables 
across different scales (Hamid et al., 2017). Henseler 
et al. (2015) also state the use of  HTMT since it is 
straightforward, performs robustly, and therefore assists 
in this case. This test was selected because, given the 
provided data, it provides a strong tool for assessing 
how well different constructs, or scales, differ from one 
another. According to Ringle et al. (2024), if  the value 
for HTMT is below the threshold limit of  0.85, strong 
discriminant validity exists. Achieving this threshold 
proves to be a critical issue because it reflects discriminant 

validity between constructs (Henseler et al., 2015). In the 
case of  the studied research, the HTMT value of  AI self-
efficacy toward attitude toward AI is 0.534. The value 
is remarkably below 0. threshold, which suggests an 
important difference between the two constructs.
This finding suggests that AI self-efficacy—which is 
defined as belief  or confidence in using AI appropriately—
is not the same as an individual’s general attitude or 
feelings regarding AI. Because of  this, different aspects 
of  students’ interactions with and views toward AI can 
be captured by these factors individually. By ensuring that 
AI self-efficacy and attitude towards AI measure different 
cognitive and affective aspects of  how students perceive 
and interact with AI-related technologies, this distinction 
strengthens the study and provides a more thorough 
understanding of  the students’ varied perspectives and 
competencies about AI.

Table 1: Construct Validity and Reliability.
Variables Cronbach’s alpha Composite reliabi

lity (rho_a)
Composite reliabi
lity (rho_c)

Average variaance extracted
(AVE)

AI Self-Efficacy 0.875 0.889 0.902 0.536
Attitude towards 
AI

0.961 0.964 0.964 0.589

Discriminant Variable Heterotrait -Monotrait Ratio(HTML)
AI Self  -Efficacy <-> Attitude Towards AI 0.534

Table 2 shows that university students, with an average self-
efficacy score of  3.47 (SD = 0.683), are fairly confident in 
their abilities to work with AI. With a score of  3.38 (SD = 
0.809), they also have a moderate overall attitude toward 
AI, leaning slightly toward neutrality. As such, this finding 
resonates well with what Obenza et al. (2023) found 
because this would amount to an overlap of  opinions 
on how students feel towards AI. As pointed out by 
Chen et al. (2022), the study shows how AI programming 
self-efficacy and AI literacy are vital regarding wanting 
to teach students about delving into AI software 
development. This finding is in line with the moderate 
levels of  self-efficacy displayed in Table 2, suggesting that 
university courses and AI training programs significantly 
boost students’ self-confidence. Similarly, Fryer et al. 
(2020) highlight the role that curiosity and self-efficacy 
play in academic performance, stressing the impact that 
past experiences and passions have on students’ future 
success. This theory is consistent with the moderate levels 
of  self-efficacy that was observed, demonstrating the 
importance of  students’ prior experiences and excitement 
for AI in fostering their present confidence. In contrast 
to their emotional reactions (mean = 3.29, SD = 0.810) 
or cognitive views (mean = 3.60, SD = 0.972), students’ 
behaviors toward AI are more neutral (mean = 3.25, SD 
= 0.892) when the specific components of  attitude are 
examined. 
This finding is consistent with multiple research 

examining students’ diverse perspectives on artificial 
intelligence. For example, a study on Chinese secondary 
school students’ impressions of  AI revealed that their 
opinions are influenced by their beliefs about the 
technology’s benefits to society and how useful they 
believe it to be. Thus, what matters is exactly what 
students believe concerning the benefits of  AI (Chai et al., 
2020). Another study with college students proved to be 
similar to the earlier discussion, where it established that 
the amount of  success those higher-education students 
were expecting to have with AI as well as their feeling 
of  support had significantly influenced their attitudes and 
intentions towards AI. This goes a long way in showing 
that cognitive factors are crucial in determining their 
overall perception (Alzahrani, 2023).
The role is also significant with emotional responses to 
AI among the students. It has been indicated through 
research that improved learning experiences as well as 
attitudes in students come with positive engagement of  
the students with AI tools. For example, in the research 
into AI writing tools, it has been observed that students 
who have had interactions with the mentioned AI tools 
had emotional engagements and could still view AI 
positively (Nazari et al., 2021). While emotional reactions 
to AI are more consistent, cognitive attitudes—reflecting 
their views and beliefs—lean somewhat more positively 
but exhibit a larger range of  perspectives. 



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Table 2: Descriptive Statistics
Variable N Mean Median Mode SD

AI Seld-Efficacy 423 3.47 3.40 3.00 0.683
Attitude towards 
AI

423 3.38 3.36 3.00 0.809

Behavioral 423 3.25 3.17 3.00 0.892
Affective 423 3.29 3.30 3.00 0.810
Cognitive 423 3.60 3.50 3.00 0.972

Table 3 demonstrates how an individual’s attitudes toward 
the application of  AI are strongly correlated with their 

level of  self-efficacy in using it. Specifically, for each one-
unit increase in self-efficacy, attitudes toward AI go up by 
0.519 units. This positive connection indicates that people 
who feel capable of  using AI are likely to view it more 
favorably. Furthermore, with a standardized coefficient 
of  0.519 and a mean of  0.526, Figure 1 demonstrates 
that there is a substantial (p < 0.001) direct association 
between views toward AI and AI self-efficacy. These 
results are in line with what has been seen in the travel 
and hospitality industry, where attitudes and desire to use 
AI services are significantly influenced by self-efficacy 
(Ho et al., 2022). 

Table 3: Direct effect
Hypothesis Original

sample(O)
Sample
Mean(M)

Standard Deviatiion 
(STDEV)

F-square T-statistics P-values

AI Seld-Efficacy<->
Attitude towards AI

0.519 0.526 0.048 0.368 10.869 0.000

R2= 0.269 Adjusted R2=0.267

This also shows that fostering self-efficacy can lead 
to more positive attitudes toward AI, which is crucial 
for effective integration into learning environments. 
Research indicates that early development of  self-efficacy 
and interest can have lasting benefits (Fryer et al, 2020). 
However, it’s worth noting that while many express 
positive attitudes, their subconscious feelings may not 
align; one study found that participants often showed 
negative implicit responses despite positive explicit 
attitudes, indicating that self-efficacy alone might not 

capture all concerns about AI (Fietta et al., 2022). 
The substantial t-statistic of  10.869 indicates that self-
efficacy plays a significant role in influencing attitudes, and 
data that are consistent in various circumstances support 
this conclusion (Grassini, 2023; Fryer et al., 2020; Livinūi 
et al., 2021). The impact of  human contact in learning, 
however, does not always translate to other domains- 
where AI is the focal point because the outcomes are 
context-dependent (Fryer et al. 2020).

Figure 1: The Correlation Between AI Attitude  and Self  Efficacy



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The effect size of  0.368, as indicated by Table 3, suggests 
that students’ attitudes toward AI are influenced in a 
moderate to strong way by their level of  AI self-efficacy. 
This points out that attitudes toward the employment of  
AI increase dramatically for students who are becoming 
more confident in their capacity to use it (Chen et al., 2022; 
Ayanwale, 2023; Park, 2023). This therefore implies that 
there is a need to boost self-efficacy as a step to achieve 
more positive views of  AI among the students. Programs 
that involve enhanced AI self-efficacy through hands-on, 
interactive, and supportive learning settings work well in 
enhancing positive attitudes toward AI (Chai et al., 2022; 
Nazari et al., 2021; Alzahrani, 2023). Both helpful and 
enjoyable courses enhance literacy concerning AI and 
self-efficacy, which leads to more positive attitudes and 
higher intentions to engage with AI (Chen et al., 2022; 
Chai et al., 2022).
The R² for attitudes toward AI in Table 3 is 0.269 with 
an adjusted R² of  0.267. This means about 26.9% of  the 
differences in students’ attitudes can be accounted for by 
their self-efficacy in AI. A very minor change in this R² 
means that although self-efficacy is a significant predictor 
of  attitudes toward AI, other factors are also involved 
in forming these perceptions. Therefore, whereas self-
efficacy contributes, clearly other influences remain.

CONCLUSION
This study provides highly compelling evidence 
concerning the positive relationship that AI self-efficacy 
bears with attitudes toward AI among university students. 
These results suggest that AI self-efficacy has a strong 
relationship with attitudes toward AI among university 
students in Davao City. It implies that individuals who 
believe that they are better equipped to use AI possess 
more favorable attitudes toward AI, which would explain 
much of  the variance in those attitudes. The results are 
strengthened with strong construct reliability and validity, 
which increase the confidence in them. Improving 
AI self-efficacy may also significantly strengthen the 
attitudes students have toward AI technologies. As 
a result, educational institutions could then conduct 
practice and training activities that would give students 
a feeling of  confidence in using AI and increase their 
acceptance and use of  the tools in their respective careers. 
Although the data presented in this study does not reveal 
whether sex acts as a mediator in the relation between 
AI self-efficacy and attitudes toward AI, further closer 
examination would be required to shed further light on 
this dimension. Furthermore, given the strong direct 
effect that self-efficacy has on attitudes, a moderated 
mediation analysis might provide light on the ways 
in which sex moderates or even reverses this effect. It 
should be noted that this study is limited to Davao City 
university students, and hence cannot be generalized to a 
larger student population. Future research would help to 
broaden this sample demographically across locations or 
types of  study. In addition, while sex has been considered 
as an essential moderator in this association, other 

demographic factors that were not taken into account 
in this study may possibly influence attitudes regarding 
AI. Studying such implications of  gender difference on 
perceiving AI may therefore provide direct avenues for 
developing targeted educational interventions that boost 
AI literacy and acceptance. Overall, this study emphasizes 
the significance of  AI self-efficacy in education which 
in fact promotes more positive attitudes and higher 
acceptance of  AI in both academic and professional 
settings.

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