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American Journal of  Smart 
Technology and Solutions (AJSTS)

The Nexus between Cognitive Absorption and AI Literacy of  College Students as
Moderated by Sex

Brandon Nacua Obenza1*, Liam E. Go1, Jofrance Ardrian M. Francisco1, Evann Ernest T. Buit1, Frande Vier B. Mariano1, Henry 
L. Cuizon Jr1, Alliah Jane D. Cagabhion1, Karl Axl James L. Agbulos1

Volume 3 Issue 1, Year 2024
ISSN: 2837-0295 (Online)

DOI: https://doi.org/10.54536/ajsts.v3i1.2603
https://journals.e-palli.com/home/index.php/ajsts

Article Information ABSTRACT

Received: February 20, 2024

Accepted: March 25, 2024

Published: March 29, 2024

This study examines how Cognitive Absorption and AI Literacy are related among college 
students, specifically looking at how sex moderates this link. The study uses a quantitative 
research strategy and a non-experimental correlational approach. Data was collected 
through Google Forms utilizing modified questions designed for AI Literacy and cognitive 
absorption. G*Power 3.2 was used for power analysis to determine the necessary sample 
size for the investigation. 372 college students from different higher education institutions 
in Region XI were selected to take part in the study by stratified random sampling. 
Reliability and validity tests, including Cronbach’s alpha, Average Variance Extracted 
(AVE), and Heterotrait-Monotrait Ratio (HTMT), were performed on the dataset before 
undertaking moderation analysis. Cognitive Absorption was identified as a key predictor 
of  AI Literacy, showing a substantial impact size of  0.417. The moderating effect of  sex, 
although statistically significant, had a minor effect size of  0.011. The corrected R-squared 
value of  0.378 indicates that the model, with all covariates, accounts for 37.8% of  the 
variance in AI literacy.

Keywords
Cognitive Absorption, AI 
Literacy, Moderation Analysis, 
SmartPLS, Philippines

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

INTRODUCTION
AI literacy is the essential talent of  effectively engaging 
with and critically assessing AI technology in today’s 
tech-centric society (Long & Magerko, 2020). It involves 
comprehending, utilizing, assessing, and dealing with 
ethical concerns associated with AI (Ng et al., 2021). 
Disparities exist in AI literacy across children from 
various socioeconomic and cultural backgrounds (Druga 
et al., 2019). Initiatives to improve understanding of  AI 
involve a middle school program designed to educate 
kids on AI for them to become knowledgeable citizens 
and discerning users of  AI (Lee et al., 2021), and the 
creation of  an AI-robotics tool to advance AI literacy 
in underdeveloped nations (Eguchi, 2021). AI has the 
capacity to greatly improve communication abilities 
in English language learners, with a primary focus on 
writing, reading, and vocabulary development in language 
education.
Various studies have investigated the AI literacy of  
college students, yielding favorable results. Kong et al. 
(2021) and Lee et al. (2021) discovered that students from 
various backgrounds can gain a conceptual knowledge 
of  AI. They also observed that AI literacy education 
can enhance students’ ethical awareness of  AI. Ng et 
al. (2022) and Lee et al. (2021) showed how pedagogical 
methods, including digital story writing, can enhance 
AI literacy in primary and middle school children. Juma 
(2021) discovered that although higher education students 
acknowledge the significance of  AI in education, they 
possess little knowledge and understanding of  it. These 
results emphasize the necessity of  ongoing initiatives to 
improve AI literacy among college students.

Cognitive absorption, a high level of  engagement with 
software, has been shown to strongly correlate with digital 
literacy in secondary school children (Canan Güngören 
et al., 2022). This is especially important in the realm of  
AI literacy, which is seen as a modern form of  cognitive 
intelligence (Wang & Lu, 2023). Cognitive absorption’s 
impact on establishing user trust and enhancing experience 
in human-machine interactions has been investigated by 
Balakrishnan & Dwivedi (2021). AI literacy is associated 
with metacognition and the capacity to predict an 
unpredictable future (Yi 2021). Literacy has a substantial 
correlation with cognitive performance in well-educated 
older adults, as shown by Barnes et al. (2004). Research 
has examined how cognitive absorption is influenced by 
computer playfulness and perceived quality in the context 
of  fun-oriented information systems utilization (Weniger 
& Löbbecke, 2011). Perceived affective quality has 
been suggested as a precursor to cognitive absorption, 
significantly influencing it (Ping Zhang et al., 2006).
Several research have examined people’s opinions and 
use of  artificial intelligence (AI) (Obenza et al., 2023a, 
2023b). A notable deficiency in the existing research is 
the investigation of  AI literacy in college students and 
the factors that may influence it. Current literature on 
AI literacy, namely on cognitive absorption, still has 
considerable inadequacies and restrictions despite the 
increasing study in this area. Long and Magerko (2020) 
and Ng (2021) have suggested competencies and design 
considerations for AI literacy, but they have not specifically 
addressed sex disparities. Therefore, there is a notable 
absence of  focus on sex-specific patterns or issues in this 
area, highlighting the need for more research. Research 



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must investigate sex discrepancies in the relationship 
between cognitive absorption and learning outcomes in 
AI literacy. The study aimed to examine how cognitive 
absorption and AI literacy of  college students are related, 
with consideration to how this relationship may be 
influenced by their gender. 

Theoretical Framework
Cognitive Absorption Theory (CAT) is a useful 
framework for examining the complex connection 
between cognitive absorption, AI literacy, and gender 
among college students. CAT, first introduced by Agarwal 
and Karahanna in 2000, explains the extreme involvement 
humans display when using technology, marked by 
strong concentration, altered perception of  time, and 
profound immersion (Agarwal & Karahanna, 2000). 
The hypothesis suggests that cognitive absorption is a 
condition in which individuals are completely engrossed 
in technology-mediated activities, surpassing awareness 
of  their surroundings and fostering a heightened sense 
of  cognitive engagement (Agarwal & Karahanna, 2000). 
CAT emphasizes that cognitive absorption is evident in 
different aspects, including absorption, attention, control, 
and immersion (Agarwal & Karahanna, 2000). Absorption 
refers to how deeply individuals engage in a specific task, 
while focus indicates the level of  concentration directed 
towards the activity. Control refers to the sense of  control 
individuals have over the technological interface, while 
immersion indicates the degree of  temporal distortion 
and detachment from the immediate world (Agarwal & 
Karahanna, 2000). 
Cognitive absorption is especially important in the realm 
of  AI literacy for college students. When people interact 
with AI technologies, their level of  cognitive absorption 
can greatly impact how they learn and understand AI-
related information and abilities. CAT provides a detailed 
framework for comprehending the cognitive processes 
involved in the advancement of  AI literacy. It explains 
how individuals’ thorough interaction with AI interfaces 
influences their cognitive schemas and knowledge 
structures (Agarwal & Karahanna, 2000).
Moreover, the influence of  gender as a moderator 
should be taken into account in the context of  CAT. 
Recent findings indicate that there may be gender 
differences in how cognitive absorption is experienced 
and its outcomes, with research showing varying levels of  
technological involvement and immersion between men 
and women (Agarwal & Karahanna, 2000). This study 
aims to investigate how gender, as a moderating variable, 
affects the relationship between cognitive absorption and 
AI literacy in college students. It tries to uncover potential 
gender-related distinctions in technology adoption, 
engagement, and proficiency. 
Cognitive Absorption Theory offers a strong theoretical 
basis for studying the relationship between cognitive 
absorption, AI literacy, and gender in college students. 
CAT provides valuable insights into how individuals 
acquire and internalize AI-related knowledge by 

explaining the cognitive mechanisms involved in 
technology engagement and immersion. This enriches 
our understanding of  the interaction between cognition, 
technology, and gender. 

METHODS AND MATERIALS
The study utilized a quantitative research strategy, 
specifically a non-experimental correlational approach, 
to investigate how sex influences the connection between 
cognitive absorption and AI literacy. Quantitative 
research, according to Creswell & Creswell (2022), is a 
methodical examination of  factual concepts through the 
analysis of  variable relationships. This method allows for 
the measurement of  variables using instruments, making 
it easier to apply statistical tools for data analysis.
Ramayah et al. (2017) used a moderating variable (MV) 
to explain how the predictor’s effect is impacted by the 
criterion. This factor is crucial for thoroughly examining 
the relationship between the predictor and criteria 
variables. The MV does not directly affect the predictor, 
but it can influence the intensity and direction of  the 
association between the basic components. 
The AI Literacy scale, a 5-point scale with 12 items, 
was modified from Wang et al.’s research in 2022 to 
measure people’s daily interaction with, understanding 
of, and assessment of  AI technology. The study used 
the Cognitive Absorption scale, a 7-point scale with 20 
items created by Agarwal and Karahanna in 2000. The 
survey was conducted online. The participants were 
college students from Region 11 who were chosen using 
stratified random sampling. 
A power analysis was performed using G*Power 3.1.9.6 
(Faul et al., 2007) before data collection. It was found that 
a sample size of  N = 89 was needed to obtain 80% power 
for detecting a medium effect (f2 = 0.15) at a significance 
level of  α = 0.05. The sample size of  N = 372 exceeded 
the minimum requirement, which strengthened the 
study’s ability to investigate the complex interactions 
between the variables.
Various strategies were used to guarantee the validity and 
reliability of  the measurement models. The measurements 
used were Average Variance Extracted (AVE) for assessing 
convergent validity, Heterotrait-Monotrait Ratio (HTMT) 
for evaluating discriminant validity, and Cronbach’s 
alpha for determining internal consistency. Descriptive 
statistics, including standard deviation and mean, were 
computed using Jamovi software version 2.0 to describe 
AI literacy and cognitive absorption. The bootstrapping 
standardized algorithm was used using SmarPLS 4.0 
software to assess the postulated moderation model. The 
study aims to thoroughly investigate how sex moderates 
the link between cognitive absorption and AI literacy 
among college students using rigorous approaches.

Hypothesis
Sex has a significant moderating effect on the relationship 
between Cognitive Absorption and AI Literacy of  college 
students.



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RESULTS AND DISCUSSION
Establishing the validity and reliability of  the measurement 
model is paramount when conducting research utilizing 
moderating analysis, as emphasized by Hair et al. (2019). 
Prior to evaluation, potential gaps in some items were 
addressed to ensure the robustness of  the research 
instrument. Table 1 presents the assessment of  reliability 
and validity, conducted through the utilization of  
Cronbach’s alpha, a widely accepted measure of  internal 
consistency for questionnaires (Mashingaidze et al., 2021). 
The obtained Cronbach’s alpha values for AI Literacy 
(0.898) and Cognitive Absorption (0.942) exceeded the 
benchmark of  0.7, indicating strong internal consistency 
and validity of  the questionnaires (Taber, 2017).
As per the recommendations established by 
Diamantopoulos et al. (2001) and Drolet and Morrison 
(2001), Cronbach’s alpha values of  0.60 to 0.70 are 
considered acceptable, while values between 0.70 and 
0.90 are deemed tolerable to good. Both factors had 

values exceeding 0.70, confirming the instrument’s 
dependability in measuring the constructs of  interest. The 
Cronbach’s alpha values did not surpass 0.95, suggesting 
that redundancy within the factors was not a problem. 
Convergent validity was evaluated by calculating the 
Average Variance Extracted (AVE). The average values 
for AI Literacy (0.586) and Cognitive Absorption (0.575) 
above the recommended threshold of  0.5, as proposed 
by Fornell and Larcker (1981) and Hair et al. (2019). 
AVE scores of  0.50 or higher suggest that the construct 
explains 50% or more of  the variability in its components, 
which supports the validity of  the measurement model. 
Discriminant validity, a critical component of  validating 
measurements, was assessed by Heterotrait-Monotrait 
(HTMT) ratios. The paired ratios varied between 0.011 and 
0.696, showing good discriminant validity as none surpassed 
the threshold of  0.85 suggested by Henseler et al. (2015). 
The results confirm that the instrument used is valid and 
trustworthy for evaluating the constructs being studied.

Table 1: Construct Validity and Reliability
Variables Cronbach's alpha Average variance extracted (AVE)
AI Literacy 0.898 0.586
Cognitive Absorption 0.942 0.575
Discriminant Validity - Heterotrait-monotrait ratio (HTMT)
AI Literacy <-> Cognitive Absorption 0.648
AI Literacy <-> Sex 0.104
AI Literacy <-> Sex x Cognitive Absorption 0.350
Sex <-> Cognitive Absorption 0.070
Sex <-> Sex x Cognitive Absorption 0.011
Cognitive Absorption <-> Sex x Cognitive Absorption 0.696

Analyzed data from 372 respondents provided significant 
insights into the degrees of  AI literacy and cognitive 
absorption among college students, as detailed in Table 
2. The average AI literacy score was 3.43, suggesting a 
high degree of  AI literacy among students from different 
universities. This discovery aligns with prior research 
conducted by Kong et al. (2022) and Kong et al. (2021), 
indicating elevated levels of  AI literacy in students 
attending universities in Hong Kong. Nevertheless, 
the results also bring to mind the concerns highlighted 
by Anderson and Anderson (2006) regarding security, 
privacy, and biases linked to advanced AI knowledge. 
AI literacy was evaluated based on four subfactors: 
Awareness, Usage, Evaluation, and Ethics. The subfactors, 
as outlined by Wang et al. (2022), explain many aspects of  
AI literacy. The average values for each subfactor were: 
Awareness (3.27), Usage (3.40), Evaluation (3.64), and 
Ethics (3.41). The results indicate a considerable level 
of  skill in all subcategories, consistent with Kong et al.’s 
(2022) research on ethics, accessibility, and expertise in AI 
literacy among university students. 
The study found that Cognitive Absorption had a mean 

value of  5.04, suggesting a moderate to high level of  
cognitive absorption in college students. The components 
of  Cognitive Absorption, such as Temporal Dissociation, 
Focused Immersion, Heightened Enjoyment, Control, 
and Curiosity, showed average values between 4.73 and 
5.52. The results support prior research conducted by 
Balakrishnan and Dwivedi (2021), emphasizing the 
connections among cognitive absorption, trust, and 
experience in interactions between humans and machines. 
College students’ proficiency in AI literacy and cognitive 
absorption varies. Research conducted by Yang et al. 
(2022) and Banele (2023) emphasizes the importance 
of  increasing awareness and implementation of  
mobile learning, along with enhancing evaluation tools 
for information literacy and individualized learning 
suggestions. Dergunova et al. (2022) highlighted the 
importance of  addressing authenticity concerns, cognitive 
anxieties, and the necessity for specific interventions 
to improve AI literacy and cognitive absorption. 
Zastudil et al. (2023) highlighted concerns regarding 
excessive dependence on AI and the possible negative 
consequences.



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Cognitive absorption, a state of  deep involvement 
with technology, has been found to be influenced by 
human-to-machine interaction (Balakrishnan & Dwivedi, 
2021). This absorption is crucial in the development 
of  AI literacy, which is considered a form of  cognitive 
intelligence (Wang & Lu, 2023). AI literacy, in turn, is 
linked to metacognition and the ability to anticipate 
the future (Yi & Park, 2021). The predictive ability 
of  cognitive skills, including working memory, is also 
important in literacy (Alloway & Gregory, 2013). Figure 
1 shows the link between Cognitive Absorption and 
AI Literacy of  College Students as Moderated by Sex. 
The path from Cognitive Absorption to AI Literacy is 

strong and positive (0.691) and highly significant (p < 
0.001). This indicates a robust direct effect of  Cognitive 
Absorption on AI Literacy, meaning that as college 
students’ cognitive absorption increases, their AI literacy 
also tends to increase.
The path from the interaction term (Sex x Cognitive 
Absorption) to AI Literacy is negative (-0.201) and 
significant (p = 0.049). This suggests that sex does 
moderate the relationship between cognitive absorption 
and AI literacy, but it has a small effect, meaning that the 
increase in AI literacy associated with cognitive absorption 
is less pronounced for female compared to male.

Table 2: Status of  college students’ AI Literacy and Cognitive Absorption
Variables Mean SD Description
Cognitive Absorption 5.04 0.94 Moderately High
Temporal Dissociation 5.52 1.24 High
Focused Immersion 4.90 1.02 Moderately High
Heightened Enjoyment 4.82 0.95 Moderately High
Control 4.73 1.09 Moderately High
Curiosity 5.22 1.37 Moderately High
AI Literacy 3.43 0.54 High
Awareness 3.27 0.62 Moderate
Usage 3.40 0.63 Moderate
Evaluation 3.64 0.77 High
Ethics 3.41 0.66 High

Figure 1: Moderation Analysis Results from SmartPLS

The results indicate that the interaction effect of  sex and 
cognitive absorption on AI literacy is significant, with a path 
coefficient of  -0.201 and a p-value of  0.049, supporting 

hypothesis 1. This suggests that sex moderates the 
relationship between cognitive absorption and AI literacy 
among college students, albeit with a small effect size (f2 = 



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0.016). AI literacy’s R-square value is 0.378, indicating that 
the model explains approximately 37.8% of  the variance in 
AI literacy, with cognitive absorption having a substantial 
effect size (f2 = 0.417) on AI literacy compared to the 

minimal effect of  sex alone (f2 = 0.011). This is a moderate 
level of  explanatory power, suggesting other factors also 
play a role in determining AI literacy levels.
The interaction plot shows the relationship between 

Table 3:  Moderating Effect, R-square values, and effect sizes
Hypothesis Path 

Coefficient 
(B)

Sample 
Mean

Standard 
deviation 
(STDEV)

T 
statistics

P 
value

Remark

Sex x Cognitive Absorption -> AI Literacy -0.201 -0.205 0.102 1.97 0.049 H1 is 
supported

R-square R-square adjusted
AI Literacy 0.378 0.373
Effect Sizes (f2)
Cognitive Absorption -> AI Literacy 0.417
Sex -> AI Literacy 0.011
Sex x Cognitive Absorption -> AI Literacy 0.016

cognitive absorption and AI literacy, differentiated by sex. 
The red line represents male respondents, and the green 
line represents female respondents. The lines are not 
parallel, which indicates that there is indeed a moderating 
effect of  sex on the relationship between cognitive 
absorption and AI literacy.
The positive slope of  both lines suggests that as cognitive 
absorption increases, so does AI literacy for both sexes. 
However, the red line is steeper, indicating a stronger 
relationship between cognitive absorption and AI literacy 
for the group represented by males. This corroborates 

the findings of  Shashaani (1997), Shashaani and Khalili 
(2001),  Dzandu et al. (2016), and Nouraldeen (2022) who 
explored the attitudes and adoption of  college students 
towards AI and other related technology revealed 
intriguing dynamics, with sex playing a significant role. 
They have found that male students exhibit a more 
positive attitude or adoption towards AI/technology, 
spending more time on computer and mobile devices 
and demonstrating greater technological experience, 
knowledge, and awareness.

Figure 2: Simple Slope Analysis (0=Male and 1=Female)



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CONCLUSION
The research results emphasize the importance of  
cognitive absorption in developing AI literacy among 
college students, influenced by gender. The notion of  
cognitive absorption suggests that intense involvement 
in an activity results in improved learning outcomes, as 
seen by the significant correlation between cognitive 
absorption and AI literacy. The association between 
cognitive absorption and AI literacy is influenced by sex, 
as shown by a substantial interaction effect, indicating 
that the impact of  cognitive absorption on AI literacy 
differs between males and females. The model explains 
around 37.8% of  the variation in AI literacy, emphasizing 
the significant impact of  cognitive absorption. It also 
suggests the existence of  additional components not 
covered in this study. The results show that educational 
tactics in AI should be customized to reflect both 
cognitive engagement and the moderating effect of  sex.
Though studying cognitive absorption and AI literacy 
among college students provides important information, 
some issues need to be addressed in future studies. 
To begin with, this research focuses mainly on sex as 
a moderator, leaving out possible influences such as 
culture, education, and social status. An intersectional 
analysis including these factors would probably give 
a deeper explanation for the complex forces causing 
AI literacy and cognitive immersion among university 
students. Furthermore, the use of  Google Forms for data 
collection may result in a digital literacy bias that excludes 
those who lack internet prowess. Further studies can be 
done on how the students see AI and look at some risks 
and morality issues that accompany this technology in 
education.

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