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MORE THAN SKILLS: HOW AI LITERACY SHAPES STUDENT MOTIVATION IN THE AGE OF EDUCATION 4.0
Quirino State University (Philippines)
Received July 2025
Accepted November 2025
Abstract
Grounded in Expectancy-Value Theory (EVT), this study examined the relationship between students’ AI literacy and their motivation to use AI in academic contexts. Specifically, it explored how four AI literacy domains, namely awareness, usage, evaluation, and ethics, relate to expectancy beliefs and task value components (attainment, utility, intrinsic/interest, and cost). Data were collected from Filipino college students enrolled in a general education science course and analyzed using descriptive statistics, t-tests, ANOVA, correlation, and multiple regression. Results showed that students possessed high AI literacy across all domains, with the highest rating in awareness. Motivation was generally high across task value components but moderate for expectancy beliefs and cost. Female students demonstrated slightly higher intrinsic motivation, evaluative skills, and ethical awareness than males, while no significant differences were observed across academic programs. Correlation analyses revealed small-to-moderate relationships between AI literacy and motivational beliefs, except for the non-significant link between ethics and utility values. Among the literacy domains, usage exhibited the strongest associations and emerged as the only significant predictor of task value components, highlighting the motivational benefits of hands-on AI engagement. Although AI literacy accounted for a small proportion of variance in motivation, the findings emphasize its meaningful role in fostering students’ confidence, value perceptions, and ethical reflection. The study underscores the importance of experiential, reflective, and inclusive AI learning approaches that cultivate both competence and sustained motivation in higher education.
Keywords – AI literacy, Motivational beliefs, Expectancy-value theory, Higher education.
To cite this article:
|
Ocado, M.K.G. (2025). More than skills: How AI literacy shapes student motivation in the age of Education 4.0. Journal of Technology and Science Education, 15(3), 679-698. https://doi.org/10.3926/jotse.3748 |
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1. Introduction
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Artificial Intelligence (AI) is continuously shaping various sectors, including education. With the emergence of Education 4.0, AI plays a key role in transforming the learning process by supporting adaptive, self-directed, and immersive learning (Rane, 2024; Lata & Kumari, 2025). Education 4.0 aims to equip learners with the required skills to successfully adapt in an increasingly digitalized and globalized world through personalized, flexible, and technology-integrated learning environments (Portilla, 2025). However, effective engagement with AI requires not only technical skills but also the motivation to willingly use them meaningfully, responsibly, and independently. In this case, motivation is the students’ belief in their competence and the value they see in using AI (Wigfield & Eccles, 2000). Students who can confidently use AI tools (expectancy) and believe they are important and enjoyable (task value) are more likely to embrace AI positively (Yin & Goh, 2024). These motivational beliefs are often shaped by students’ knowledge and skills, notably their AI literacy (Zhao, 2025).
While commonly viewed as a set of technical skills, AI literacy is a multidimensional concept. It encompasses four domains, which include (1) awareness of AI capabilities, (2) actual usage of AI tools, (3) ability to assess AI-generated content critically, and (4) understanding of the ethical implications of AI in both academic and social settings (Ng, Leung, Chu & Qiao, 2021; Ma & Chen, 2024). These domains are aligned to the broader objective of Education 4.0, which is to develop digital citizens who are reflective, adaptive, and ethically informed (Costa, Cangussu, Gonçalo, Reinoso, Lemos, Azevedo et al., 2025). As such, AI literacy may play a critical role in shaping students’ motivation to use AI by boosting their confidence, clarifying its relevance to academic success, and reducing apprehensions or perceived barriers (Chiu, Ahmad, Ismailov & Sanusi, 2024).
Despite the growing focus of AI research in education, there are still few studies on AI literacy. Many previous works have focused on digital literacy, which appears to be similar to AI literacy (Siddiq, Gochyyev & Wilson, 2017; Ghomi & Redecker, 2019; Li & Hu, 2020). Wang, Rau and Yuan (2022) pointed out, however, that digital literacy is not a replacement for AI literacy, since this is an interdisciplinary field. This distinction underscores the need for independent research on AI literacy (Ma & Chen, 2024). Moreover, existing AI literacy frameworks (Wang et al., 2022; Laupichler, Aster, Haverkamp & Raupach, 2023) also failed to survey college students in developing countries. Furthermore, AI research in developing countries is more focused on technical proficiency, knowledge, and frequency of use, rather than on psychological and emotional factors that stimulate AI use (Wulyani, Widiati, Muniroh, Rachmadhany, Nurlaila, Hanifiyah et al., 2024; Saihi, Ben-Daya & Hariga, 2024; Rahman, Hossain, Ismail, Hossen & Sultana, 2025; Rosa, Dacuma, Ang, Nudalo, Cruz & Vallespin, 2024).
Hence, the current study aims to investigate the relationship between students’ AI literacy and their motivation to utilize AI. Understanding their relationship is crucial for creating learning experiences and institutional supports that foster both digital competencies and meaningful engagement with AI in the Education 4.0 era. This study primarily examines the influence of students’ four AI literacy domains — awareness, usage, evaluation, and ethics — on their five motivational constructs, as conceptualized within the Expectancy-Value Theory (EVT) (Yurt & Kasarci, 2024). Expectancy pertains to individuals’ beliefs in their ability to effectively execute a specific work, while value reflects the perceived significance and benefits of that task to the individuals (Yurt & Kasarci, 2024). There are four value subcomponents: attainment, utility, intrinsic or interest, and cost values. Attainment values refer to the personal significance of a task, particularly when it aligns with one’s identity (Eccles & Wigfield, 2002). Utility value is described as the task’s relevance to future goals (Barron & Hulleman, 2015), whereas intrinsic value reflects the satisfaction or interest gained from a task (Ryan & Deci, 2000). The cost includes the perceived effort, the danger of failure, and the trade-offs associated with task completion (Barron & Hulleman, 2015). Understanding these motivating factors helps explain why people engage with AI tools and technologies.
In addition to EVT as the main theoretical framework of the study, the theories Social Cognitive Theory (SCT), Self-Determination Theory (SDT), and Technology Acceptance Model (TAM) were also applied to provide a more comprehensive understanding of the interplay between AI literacy and motives for using AI. The SCT emphasizes the influence of personal factors, behavioral patterns, and environmental influences on learning and motivation (Bandura, 1997). Self-efficacy, which is a component of SCT, refers to an individual’s confidence in their ability to accomplish a task. This is an essential factor in their motivation, decision-making skills, and persistence to learn new technologies. SDT emphasizes the importance of addressing fundamental psychological needs, such as competence, autonomy, and relatedness, to foster growth (Ryan & Deci, 2000). Finally, the TAM shows that perceived usefulness and ease of use are essential factors in shaping technology adoption (Davis, 1989). Based on this multi‑theoretical perspective and multiple regression analysis, the study aims to advance understanding of the psychological predictors of students’ motivation to use AI in higher education. The results aim to inform educators, curriculum developers, and instructional leaders in guiding interventions that foster not only the technical form, but also the motivational readiness, ethical sensitivity, and self-directed engagement – critical tenets of Education 4.0.
Generally, this study aims to investigate the relationship between students’ AI literacy and their motivations for using AI tools. Specifically, this study has the following objectives:
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1.To describe the levels of AI literacy and motivational beliefs (expectancy, attainment value, utility value, intrinsic/interest value, and cost) among college students;
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2.To determine whether significant differences exist in AI literacy and motivational beliefs when grouped by sex and academic program;
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3.To assess the relationships between the four dimensions of AI literacy (awareness, usage, evaluation, and ethics) and students’ motivational beliefs; and
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4.To evaluate the predictive power of AI literacy dimensions in determining students’ motivational beliefs using multiple regression analysis.
2. Methodology
2.1. Research Design
A quantitative correlational research design with inferential statistics was utilized to investigate the predictive correlations between students’ AI literacy and motivational beliefs. The research was conducted in a higher education setting, specifically among college students enrolled in the general education course Science, Technology, and Society.
2.2. Participants of the Study
The participants in this study were selected using stratified random sampling to ensure academic representation among three programs that offered the General Education course Science, Technology, and Society (GE 7) during the second semester of the academic year, 2024-2025. A priori power analysis was performed using G*Power 3.1.9.7 to calculate the required sample size for multiple linear regression with four predictors. Based on a medium effect size (f² = 0.15), an alpha level of 0.05, and a desired power of 0.90, a minimum of 108 participants is required to detect a statistically significant effect. The sample size of 150 participants exceeded this requirement, indicating that the study was sufficiently powered to detect meaningful effects and reduce the likelihood of Type II errors (Erdfelder, Faul & Buchner, 1996).
2.3. Research Instrument
The online survey instrument consisted of four parts. Part I outlined the purpose of the study and the informed consent obtained in accordance with the Data Privacy Act. Part II collected demographic information, including respondents’ sex and academic program. Part III contained items from the AI Literacy Scale (ALS) (Ma & Chen, 2024) which included four constructs: Awareness, Usage, Evaluation and Ethics; and Part IV featured the Questionnaire of AI Use Motives (QAIUM) by Yurt and Kasarci (2024) grounded in the Expectancy-Value Theory (Wigfield & Eccles, 2000) covering five constructs: Expectancy and Task Value encompassing Attainment;, Utility, Intrinsic/interest value and Cost. Items were rated using a five-point Likert scale, ranging from 1 (strongly disagree) to 5 (strongly agree) for the ALS, and from 1 (completely false) to 5 (completely true) for the QAIUM.
Both adopted instruments underwent pilot testing to verify their reliability and suitability within the study context and Confirmatory Factor Analyses (CFA) to establish construct validity. Cronbach’s alpha values showed excellent internal consistencies (ALS = 0.945; QAIUM = 0.974). The CFA, on the other hand, yielded acceptable model fit indices for the ALS: χ² (df = 84) = 211, χ²/df = 2.51, p < .001; SRMR = 0.045; CFI = 0.928; IFI = 0.929; GFI = 0.962; AGFI = 0.940, and for the QAIUM: χ² (df = 160) = 413, χ²/df = 2.58, p < .001; SRMR = 0.048; CFI = 0.906; IFI = 0.907; GFI = 0.987; AGFI = 0.982. These indices met the recommended standards (χ²/df = 1–3; CFI, IFI, GFI, AGFI ≥ 0.90; SRMR ≤ 0.08) (Hu & Bentler, 1999), which showed that the measurement models were good enough. The results aligned with those presented by the original developers, who similarly identified acceptable construct validity and model fit indices (e.g., RMSEA < 0.08, CFI > 0.90) (Ma & Chen, 2024; Yurt & Kasarci, 2024).
2.4. Data Gathering Procedure
A web-based survey was developed using Google Forms and distributed through the official group chats of each class. Data were collected during the second semester of the academic year 2024-2025. The study adhered to the institution’s ethical standards and formal permission was obtained from the relevant offices before data gathering. Before answering the questionnaire, participants were provided with an electronic informed consent form that explained the purpose of the study. Participation was voluntary and confidentiality was strictly maintained.
2.5. Data Analysis
Data were analyzed using Jamovi software. Descriptive statistics (frequency, percentage, mean, and standard deviation) were used to summarize students’ levels of AI literacy and motivation. Group differences based on sex and academic program were examined using independent samples t-tests and one-way ANOVA. Pearson’s r correlation was employed to assess the relationships between AI literacy and students’ motivational beliefs. To assess the predictive power of the four AI literacy dimensions ) on expectancy and task value components, multiple linear regression analyses were conducted. All statistical tests were performed at a significance level of α = 0.05, with relevant assumptions checked before analysis.
3. Findings
This section presents the results of the analyses conducted to address the research objectives. The respondents of the study were students enrolled in the General Education course Science, Technology, and Society (GE 7) during the second semester of the academic year 2024–2025. They came from three academic programs: Bachelor of Elementary Education (BEED), Bachelor of Science in Tourism Management (BSTM), and Bachelor of Science in Hospitality Management (BSHM). The findings highlight the respondents’ levels of AI literacy and motivational beliefs, as well as the relationships and predictive paths among the study variables.
Table 1 presents the demographic profile of the respondents. As shown in the table, the majority of the participants were female (66.67%). In terms of academic program, most of the respondents were enrolled in the Bachelor of Science in Hospitality Management (BSHM), comprising 45.33% of the total sample. The remaining participants were equally distributed between the Bachelor of Elementary Education (BEEd) and the Bachelor of Science in Tourism Management (BSTM), each representing 27.33% of the respondents.
|
Variables |
f |
% |
|
Gender |
||
|
Male |
50 |
33.33 |
|
Female |
100 |
66.67 |
|
Program |
||
|
Bachelor of Elementary Education (BEEd) |
41 |
27.33 |
|
Bachelor of Science in Tourism Management (BSTM) |
41 |
27.33 |
|
Bachelor of Science in Hospitality Management (BSHM) |
68 |
45.33 |
|
Total |
150 |
100.0 |
Table 1. Demographic profile of respondents
3.1. Descriptive Statistics
3.1.1. Students’ Level of AI Literacy
Table 2 reveals that the students demonstrated a high overall level of AI literacy (M = 3.56, SD = 0.75) with the highest mean score in Awareness (M = 3.61, SD = 0.87). This reflects strong familiarity with general AI ideas, likely due to frequent exposure to AI-powered technologies and the increasing presence of AI in media and education (Sova, Tudor, Tartavulea & Dieaconescu, 2024; Farrokhnia, Banihashem, Noroozi & Wals, 2023; Hasanein & Sobaih, 2023). Similarly, high literacies were observed under Usage (M = 3.54, SD = 0.84), Evaluation (M = 3.58, SD = 0.80), and Ethics (M = 3.50, SD = 0.78).
Their high AI usage suggests their effectiveness in integrating AI into learning tasks such as research writing, problem-solving, and other academic-related tasks. This meaningful use of AI tools can be attributed to students’ positive perceptions of their usefulness and ease of use (Davis, 1989), as well as their increasing incorporation into educational settings (Nikoulina & Caroni, 2024; Hermosura, 2025; Zaidy, 2024; Zhou, Zhang & Chan, 2024; Sova et al., 2024). Frequent interactions with AI can also promote ethical reflection and critical comprehension (Wang, Chai, Li & Lee, 2025). This explains the observed high scores of the students in the Evaluation and Ethics domains.
|
Items on AI Literacy |
Mean |
SD |
Description |
|
|
Awareness |
Understands AI definition |
3.91 |
1.07 |
High |
|
Knows AI principles (e.g., ML) |
3.36 |
0.97 |
Moderate |
|
|
Understands AI perception (seeing, hearing) |
3.61 |
1.02 |
High |
|
|
Compares AI concepts (deep learning vs ML) |
3.57 |
1.01 |
High |
|
|
Mean |
3.61 |
0.87 |
High |
|
|
Usage |
Proficient in AI tools |
3.43 |
0.89 |
High |
|
Uses AI to solve daily problems |
3.45 |
1.00 |
High |
|
|
Uses AI for learning |
3.74 |
0.92 |
High |
|
|
Mean |
3.54 |
0.84 |
High |
|
|
Evaluation |
Selects appropriate AI solutions |
3.55 |
0.91 |
High |
|
Evaluates AI limitations |
3.54 |
0.97 |
High |
|
|
Identifies AI-generated bias |
3.53 |
0.94 |
High |
|
|
Skeptical about AI content |
3.69 |
1.00 |
High |
|
|
Mean |
3.58 |
0.80 |
High |
|
|
Ethics |
Adheres to ethical AI use |
3.59 |
0.98 |
High |
|
Vigilant about privacy/security |
3.70 |
1.05 |
High |
|
|
Reflects on AI’s societal impact |
2.94 |
1.21 |
Moderate |
|
|
Alert to AI misuse |
3.76 |
1.03 |
High |
|
|
Mean |
3.50 |
0.78 |
High |
|
|
Overall AI Literacy |
3.56 |
0.75 |
High |
|
Table 2. Mean levels of AI literacy among the respondents
3.1.2. Students’ Level of Motivation in Using AI
Table 3 presents the mean levels of students’ motivation to use AI tools. The data showed moderate to strong motivation, with the highest ratings observed in the Intrinsic/Interest Value (M = 3.46, SD = 0.82) and Utility Value (M = 3.46, SD = 0.83) subcomponents of task value. This high recognition of the practical benefits of AI in both academic and everyday settings and the strong willingness to engage with it can be the result of their extensive use of AI in everyday applications (Ombale & Jajoo, 2024) and its role in improving academic efficiency (Zhou et al., 2024). Moreover, their enthusiasm is presumably motivated by digital literacy, curiosity, and the perceived importance of AI in future employment (Vázquez-Parra, Henao-Rodríguez, Lis-Gutiérrez & Palomino-Gámez, 2024).
In contrast, their moderate Expectancy Beliefs (M = 3.33, SD = 0.79) show their lack of full confidence in their ability to use AI technologies for academic purposes effectively, despite their strong AI awareness, usage, evaluation, and ethical understanding (Table 2). This suggests that high AI literacy cannot be equated with higher self-efficacy. According to the findings of Osman, Mohamad and Kasbun (2024) and Ruiz-Rojas, Salvador-Ullauri and Acosta-Vargas (2024), this gap can be explained by insufficient systematic technical training and institutional support. Similarly, the students’ moderate perceived Cost (M = 2.72, SD = 0.87) reflects existing concerns about factors like time, cognitive effort, and opportunity costs associated with AI use.
|
Items on Motivational Beliefs toward AI Use |
Mean |
SD |
Description |
|
|
Expectancy |
Confidence in learning AI |
3.68 |
0.85 |
High |
|
Self-assessed AI knowledge |
3.29 |
0.93 |
Moderate |
|
|
Comparative self-confidence |
3.16 |
0.96 |
Moderate |
|
|
High self-expectation for AI use |
3.20 |
0.98 |
Moderate |
|
|
Mean |
3.33 |
0.79 |
Moderate |
|
|
Attainment Values |
Importance of AI competence |
3.63 |
0.85 |
High |
|
Priority on AI development |
3.35 |
0.87 |
Moderate |
|
|
Need for AI awareness |
3.37 |
0.97 |
Moderate |
|
|
Skill development focus |
3.41 |
0.94 |
High |
|
|
Mean |
3.44 |
0.77 |
High |
|
|
Utility Values |
AI as a career tool |
3.23 |
1.01 |
Moderate |
|
Boost in productivity |
3.49 |
0.97 |
High |
|
|
Task simplification |
3.44 |
0.97 |
High |
|
|
Academic benefits of AI |
3.69 |
0.90 |
High |
|
|
Mean |
3.46 |
0.83 |
High |
|
|
Intrinsic/Interest Values |
Enjoyment in AI use |
3.44 |
0.94 |
High |
|
Positive experiences with AI |
3.52 |
0.91 |
High |
|
|
Interest in AI trends |
3.42 |
0.96 |
High |
|
|
Fun in learning AI |
3.46 |
0.88 |
High |
|
|
Mean |
3.46 |
0.82 |
High |
|
|
Cost |
Time-value justification |
2.64 |
0.93 |
Moderate |
|
Perceived ease of learning |
2.55 |
0.90 |
Moderate |
|
|
Time investment trade-off |
2.90 |
1.05 |
Moderate |
|
|
Willingness to invest effort |
2.77 |
1.05 |
Moderate |
|
|
Mean |
2.72 |
0.87 |
Moderate |
|
|
Overall Motives in Using AI |
3.28 |
0.46 |
Moderate |
|
Table 3. Mean levels of respondents’ level of motivational beliefs in using AI
3.2. Comparative Statistics
3.2.1. Comparison of the Constructs of AI Literacy when Grouped by Sex and Academic Program
The independent samples t-test results for students’ AI literacy, grouped by sex, are illustrated in Table 4. Statistically significant differences were reported in Evaluation (t(148) = 2.525, p-value = 0.013, d = 0.437), Ethics (t(148) = 3.277, p-value = 0.001, d = 0.568), and Overall AI Literacy (t(148) = 2.198, p-value = 0.029, d = 0.381), with female students exhibiting slightly to moderately higher AI literacy levels in these domains (Cohen, 1988; Brydges, 2019). This finding aligns with the results of Jang, Choi and Kim (2022) and Wang et al. (2025), who observed that female students often display greater ethical awareness, presumably because of their stronger moral reasoning and higher sensitivity to risks, fairness, and social impacts. Female students also exhibited slightly higher degrees of Awareness (t(148) = 1.553, p-value = 0.122, d = 0.269) and Usage (t(148) = 0.805, p-value = 0.422, d = 0.140) (Cohen, 1988; Brydges, 2019) of AI compared to male students; however, these differences were not statistically significant.
|
Constructs |
Sex |
Mean |
SD |
df |
t |
p-value |
Cohen’s d |
Decision |
|
Awareness |
Male |
3.46 |
0.91 |
148 |
1.553 |
0.122 |
0.269 |
Failed to Reject Ho |
|
Female |
3.69 |
0.84 |
||||||
|
Usage |
Male |
3.46 |
0.92 |
148 |
0.805 |
0.422 |
0.140 |
Failed to Reject Ho |
|
Female |
3.58 |
0.79 |
||||||
|
Evaluation |
Male |
3.35 |
0.88 |
148 |
2.525 |
0.013 |
0.437 |
Reject Ho |
|
Female |
3.69 |
0.74 |
||||||
|
Ethics |
Male |
3.21 |
0.79 |
148 |
3.277 |
0.001 |
0.568 |
Reject Ho |
|
Female |
3.64 |
0.74 |
||||||
|
Overall Literacy |
Male |
3.37 |
0.81 |
148 |
2.198 |
0.029 |
0.381 |
Reject Ho |
|
Female |
3.65 |
0.70 |
Table 4. Independent sample T-Test result on students’ level of AI literacy based on sex
Table 5 presents the ANOVA results on students’ AI literacy across academic programs. The analysis indicated no statistically significant differences among BEED, BSTM, and BSHM students across all dimensions (all p-values > 0.05). These results imply that the students have comparable levels of AI literacy, as supported by the obtained minimal effect sizes (ω² = 0.003 to 0.033) (Cohen, 1988; Brydges, 2019). However, BSTM and BSHM students consistently displayed slightly greater levels of AI literacy across all domains than the BEED students. Although these differences were not significant, these trends may reflect the influence of other contextual factors, such as better digital literacy (Saklaki & Gardikiotis, 2024) and higher exposure to digital technologies in their curriculum (Lee, Oh & Hong, 2024).
|
Constructs |
Program |
Mean |
SD |
df |
F |
p-value |
ω² |
Decision |
|
Awareness |
BEED |
3.43 |
1.00 |
(2,86.2) |
1.084 |
0.343 |
0.003 |
Failed to Reject Ho |
|
BSTM |
3.71 |
0.73 |
||||||
|
BSHM |
3.65 |
0.86 |
||||||
|
Usage |
BEED |
3.32 |
1.00 |
(2,79.0) |
1.551 |
0.218 |
0.013 |
Failed to Reject Ho |
|
BSTM |
3.63 |
0.83 |
||||||
|
BSHM |
3.62 |
0.71 |
||||||
|
Evaluation |
BEED |
3.41 |
0.96 |
(2,81.3) |
1.023 |
0.364 |
0.004 |
Failed to Reject Ho |
|
BSTM |
3.63 |
0.76 |
||||||
|
BSHM |
3.65 |
0.72 |
||||||
|
Ethics |
BEED |
3.23 |
0.90 |
(2,85.6) |
2.862 |
0.063 |
0.033 |
Failed to Reject Ho |
|
BSTM |
3.57 |
0.65 |
||||||
|
BSHM |
3.62 |
0.75 |
||||||
|
Overall AI Literacy |
BEED |
3.35 |
0.92 |
(2,81.8) |
1.679 |
0.193 |
0.017 |
Failed to Reject Ho |
|
BSTM |
3.64 |
0.66 |
||||||
|
BSHM |
3.64 |
0.66 |
Table 5. One-way ANOVA on students’ level of AI literacy based on academic program
3.2.2. Comparison of the Constructs of Motivational Beliefs in Using AI when Grouped by Sex and Academic Program
Table 6 reveals the results of the independent samples t-test comparing students’ motivational beliefs about employing AI by sex. No significant differences were identified across most domains (p-values > 0.05). This indicates a similar level of motivation among male and female students. Nonetheless, a significant difference emerged in Intrinsic/Interest Value, t(148) = 2.041, p-value = .043, d = 0.354, with females (M = 3.56, SD = 0.75) achieving slightly higher scores than males (M = 3.27, SD = 0.92) (Cohen, 1988; Brydges, 2019). This suggests that female students show a greater appreciation for the collaborative and creative uses of digital technologies than their male counterparts. They also tend to enjoy design-oriented tasks (Wajcman, 2000) and are more drawn to technologies that can generate creative outputs like multimedia presentations. In addition, they may approach AI with greater interest and reflective thinking, resulting in more significant intrinsic value (Yin & Goh, 2024).
|
Constructs |
Sex |
Mean |
SD |
df |
t |
p-value |
Cohen’s d |
Decision |
|
Expectancy |
Male |
3.29 |
0.91 |
81.0 |
0.442 |
0.659 |
0.079 |
Failed to Reject Ho |
|
Female |
3.35 |
0.72 |
||||||
|
Attainment Value |
Male |
3.33 |
0.91 |
77.6 |
1.184 |
0.240 |
0.214 |
Failed to Reject Ho |
|
Female |
3.50 |
0.68 |
||||||
|
Utility Value |
Male |
3.35 |
0.94 |
148 |
1.251 |
0.213 |
0.217 |
Failed to Reject Ho |
|
Female |
3.52 |
0.77 |
||||||
|
Intrinsic/ Interest Value |
Male |
3.27 |
0.91 |
148 |
2.041 |
0.043 |
0.354 |
Reject Ho |
|
Female |
3.56 |
0.75 |
||||||
|
Cost |
Male |
2.75 |
1.02 |
79.0 |
-0.351 |
0.726 |
-0.063 |
Failed to Reject Ho |
|
Female |
2.70 |
0.78 |
||||||
|
Overall Motivation |
Male |
3.20 |
0.52 |
148 |
1.625 |
0.106 |
0.281 |
Failed to Reject Ho |
|
Female |
3.33 |
0.42 |
Table 6. Independent sample T-test result on students’ level of motivational beliefs in using AI
Table 7 shows the ANOVA results comparing students’ motivational beliefs toward using AI across academic programs. No statistically significant differences were found among BEED, BSTM, and BSHM students across all constructs (p-values > .05). Effect sizes (η² = –0.000 to 0.005) were negligible, confirming that program affiliation had no to little influence on motivational beliefs (Cohen, 1988; Brydges, 2019)This suggests the greater role of digital culture and personal experiences in influencing motivation for AI use than disciplinary instruction alone (Yurt & Kasarci, 2024).
|
Constructs |
Program |
Mean |
SD |
df |
F |
p-value |
η² |
Decision |
|
Expectancy |
BEED |
3.16 |
0.921 |
(2,87.0) |
1.184 |
0.311 |
0.005 |
Failed to Reject Ho |
|
BSTM |
3.43 |
0.603 |
||||||
|
BSHM |
3.38 |
0.789 |
||||||
|
Attainment Value |
BEED |
3.31 |
0.875 |
(2,83.7) |
0.688 |
0.505 |
-0.002 |
Failed to Reject Ho |
|
BSTM |
3.47 |
0.725 |
||||||
|
BSHM |
3.50 |
0.725 |
||||||
|
Utility Value |
BEED |
3.31 |
0.987 |
(2,82.5) |
0.781 |
0.461 |
-0.000 |
Failed to Reject Ho |
|
BSTM |
3.51 |
0.774 |
||||||
|
BSHM |
3.53 |
0.763 |
||||||
|
Intrinsic/Interest Value |
BEED |
3.30 |
0.962 |
(2,83.8) |
0.889 |
0.415 |
0.001 |
Failed to Reject Ho |
|
BSTM |
3.53 |
0.727 |
||||||
|
BSHM |
3.51 |
0.765 |
||||||
|
Cost |
BEED |
2.85 |
0.957 |
(2,86.8) |
0.960 |
0.387 |
0.001 |
Failed to Reject Ho |
|
BSTM |
2.76 |
0.752 |
||||||
|
BSHM |
2.61 |
0.870 |
||||||
|
Overall Motivation |
BEED |
3.19 |
0.545 |
(2,84.0) |
1.071 |
0.347 |
0.004 |
Failed to Reject Ho |
|
BSTM |
3.34 |
0.407 |
||||||
|
BSHM |
3.31 |
0.434 |
Table 7. One-way ANOVA on students’ level of motivational beliefs in using AI based on academic program
3.3. The Significant Relationship between the Level of AI Literacy and Motivational Beliefs toward AI Use
Table 8 presents the Pearson correlations between students’ AI literacy dimensions and their motives in using AI. Results show that all AI literacy dimensions were significantly and positively correlated with Expectancy, Attainment, Utility and Intrinsic/Interest Values (p-values < 0.05), except for Ethics and Utility Value (p-value = 0.052). However, these correlations were only small to moderate (r = 0.20 to 0.35) (Cohen, 1988) And among the four AI literacy domains, only Usage consistently demonstrated the strongest positive relationships across these motivational constructs: Expectancy Beliefs (r = 0.29, p-value <.001); Attainment Value (r = 0.34, p- value <.001), Utility Value (r = 0.31, p-value <.001), and Intrinsic/Interest Value (r = 0.35, p-value <.001), indicating that students who are more proficient in using AI tools are more likely to find them useful, personally valuable, and enjoyable. From a theoretical standpoint, these links are best explained by the function of actual AI use in developing one’s self-efficacy (Bandura, 1997), boosting value internalization (Wigfield & Eccles, 2000), and promoting autonomy and interest (Ryan & Deci, 2000). Also, according to previous studies, students begin to regard AI as critical to their future, supporting its importance to them (Luckin & Holmes, 2016; Van de Oudeweetering & Voogt, 2017).
|
Motivational Beliefs |
Level of AI Literacy |
Pearson’s r |
p-value |
Decision |
|
Expectancy |
Awareness |
0.24 |
0.011 |
Reject Ho |
|
Usage |
0.29 |
<.001 |
Reject Ho |
|
|
Evaluation |
0.27 |
0.001 |
Reject Ho |
|
|
Ethics |
0.20 |
0.028 |
Reject Ho |
|
|
Attainment Value |
Awareness |
0.25 |
0.013 |
Reject Ho |
|
Usage |
0.34 |
<.001 |
Reject Ho |
|
|
Evaluation |
0.31 |
0.005 |
Reject Ho |
|
|
Ethics |
0.27 |
0.001 |
Reject Ho |
|
|
Utility Value |
Awareness |
0.21 |
0.023 |
Reject Ho |
|
Usage |
0.31 |
<.001 |
Reject Ho |
|
|
Evaluation |
0.26 |
0.001 |
Reject Ho |
|
|
Ethics |
0.18 |
0.052 |
Failed to RejectHo |
|
|
Intrinsic/ Interest Value |
Awareness |
0.20 |
0.011 |
Reject Ho |
|
Usage |
0.35 |
<.001 |
Reject Ho |
|
|
Evaluation |
0.29 |
0.001 |
Reject Ho |
|
|
Ethics |
0.23 |
0.028 |
Reject Ho |
|
|
Cost |
Awareness |
-0.19 |
0.013 |
Reject Ho |
|
Usage |
-0.29 |
<.001 |
Reject Ho |
|
|
Evaluation |
-0.26 |
0.005 |
Reject Ho |
|
|
Ethics |
-0.16 |
<.001 |
Reject Ho |
Table 8. Pearson’s correlation between AI literacy dimensions and students’
motivational beliefs toward AI use (df = 148)
In contrast, all AI literacy dimensions were significantly and negatively correlated with perceived Cost (r = −0.16 to −0.29; p-values < .05), but all were described as weak correlations (Cohen, 1988). Notably, usage (r = -0.29, p-value <.001) showed the strongest negative correlation. This means that the more effective students are in using AI, the less effort and risks they experience. These findings underscore the need to enhance AI usage skills in reducing psychological barriers to AI adoption in learning environments.
3.4. The Significance of the Regression Model on Determining the Linear Relationship on Students’ Level of AI Literacy and Motivational Beliefs toward AI Use
Table 9 shows the results of the multiple regression analysis examining the predictive influence of AI literacy dimensions on Expectancy Beliefs. The model was significant, F(4, 145) = 3.61, R² = 0.091; adjusted R² = 0.065, p-value = 0.008, indicating that the predictors collectively explained 9.1% of the variance in Expectancy Beliefs. However, none of the predictors: Awareness (B = -0.011, t = -0.082, p‑value = 0.935), Usage (B = 0.208, t = 1.523, p-value = 0.130), Evaluation (B = 0.140, t = 0.795, p-value = 0.428), and Ethics (B = -0.051, t = -0.362, p-value = 0.718) were significant. This indicates that Expectancy Beliefs are influenced by students’ overall AI literacy rather than any single domain. The relatively low explained variance further implies that Expectancy Beliefs may be shaped by numerous unmeasured variables like social interaction (Huang, Zheng, Wang, Yin, Wang, Ding et al., 2023), frequency and satisfaction with AI interactions (Parsakia, 2023), and perceived ease and attitude towards AI tools (Tiwari, Bhat, Khan, Subramaniam & Khan, 2023).
|
Independent Variable (AI Literacy) |
Model Summary |
ANOVA |
Coefficients |
|||||||
|
R |
R2 |
Adj. R2 |
F |
Sig. |
B |
β |
SE |
t |
p |
|
|
Constant |
0.301 |
0.091 |
0.065 |
3.61 |
0.008 |
2.315 |
|
0.306 |
7.560 |
< .001 |
|
Awareness |
-0.011 |
-0.013 |
0.139 |
-0.082 |
0.935 |
|||||
|
Usage |
0.208 |
0.221 |
0.137 |
1.523 |
0.130 |
|||||
|
Evaluation |
0.140 |
0.143 |
0.176 |
0.795 |
0.428 |
|||||
|
Ethics |
-0.051 |
-0.050 |
0.139 |
-0.362 |
0.718 |
|||||
Table 9. Regression analysis of AI literacy dimensions and expectancy beliefs of AI use
Table 10 shows the multiple regression results for AI literacy dimensions as predictors of Attainment Values in using AI. The results show that the utility of the predictive model was significant,
F(4, 145) = 5.23, p-value < 0.001, R² = 0.126; adjusted R² = 0.102. This indicates that all of the predictors explained only a small amount of variance in Attainment Values (12.6%), characteristic of educational and psychological research, wherein motivational constructs are influenced by various interacting factors (Huang et al., 2023; Tiwari et al., 2023). Usage (B = 0.289, t = 2.211, p-value = 0.029) emerged as a significant positive predictor of Attainment Values. This means that for every one unit increase in Usage, Attainment Values are predicted to increase by 0.289 units, given that Awareness, Evaluation, and Ethics remain unchanged.
|
Independent Variable (AI Literacy) |
Model Summary |
ANOVA |
Coefficients |
|||||||
|
R |
R2 |
Adj. R2 |
F |
Sig. |
B |
β |
SE |
t |
p |
|
|
Constant |
0.355 |
0.126 |
0.102 |
5.23 |
<.001 |
2.226 |
|
0.294 |
7.585 |
< .001 |
|
Awareness |
-0.141 |
-0.159 |
0.134 |
-1.054 |
0.294 |
|||||
|
Usage |
0.289 |
0.314 |
0.131 |
2.211 |
0.029 |
|||||
|
Evaluation |
0.104 |
0.109 |
0.169 |
0.615 |
0.539 |
|||||
|
Ethics |
0.093 |
0.095 |
0.134 |
0.699 |
0.486 |
|||||
Table 10. Regression analysis of AI literacy dimensions and attainment values of AI use
The results further suggest that students who are better at using AI tools are more likely to find AI engagement personally meaningful. This may be attributed to the idea that efficient use of AI tools fosters familiarity and a sense of mastery, which, in turn, strengthens their belief in the value and relevance of AI to their academic goals and self-concept. This interpretation is consistent with the core principle of Expectancy-Value Theory (Wigfield & Eccles, 2000). In contrast, Awareness (B = -0.141, t = -1.054, p‑value = 0.294), Evaluation (B = 0.104, t = 0.615, p-value = 0.539), and Ethics (B = 0.093, t = 0.699, p‑value = 0.486) were not significant predictors of Attainment Values.
Table 11 presents the multiple regression analysis examining the influence of AI literacy dimensions on students’ Utility Values in using AI. The analysis yielded a significant model F(4, 145) = 4.25, p‑value = 0.003, accounting for a small proportion (10.5%) of the variance in Utility Values (R2 = 0.105, Adj. R2 = 0.080). Among the predictors, only Usage (B = 0.349, t = 2.429, p‑value = 0.016) significantly and positively predicted Utility Values. Specifically, an increase by one unit in Usage corresponds to a 0.349 unit increase in Utility Values, assuming other domains remain constant. These findings show that the more students use AI tools effectively, the more useful they perceive them to be in accomplishing academic tasks and learning goals. This positive link likely stems from students personally experiencing the benefits of AI in academic tasks, which increases their impression of its utility. This supports the idea that hands-on use is more effective than passive awareness in shaping utility values, highlighting the need for AI-integrated instructional practices (Baca & Zhushi, 2024). The other predictors—Awareness (B = -0.134, t = -0.911, p-value = 0.364), Evaluation (B = 0.135, t = 0.727, p-value = 0.469), and Ethics (B = -0.053, t = -0.364, p-value = 0.716) did not significantly predict Utility Values.
|
Independent Variable (AI Literacy) |
Model Summary |
ANOVA |
Coefficients |
|||||||
|
R |
R2 |
Adj. R2 |
F |
Sig. |
B |
β |
SE |
t |
p |
|
|
Constant |
0.324 |
0.105 |
0.080 |
4.25 |
0.003 |
2.418 |
|
0.322 |
7.507 |
< .001 |
|
Awareness |
-0.134 |
-0.139 |
0.147 |
-0.911 |
0.364 |
|||||
|
Usage |
0.349 |
0.350 |
0.144 |
2.429 |
0.016 |
|||||
|
Evaluation |
0.135 |
0.130 |
0.185 |
0.727 |
0.469 |
|||||
|
Ethics |
-0.053 |
-0.050 |
0.147 |
-0.364 |
0.716 |
|||||
Table 11. Regression analysis of AI literacy dimensions and utility values of AI use
Table 12 presents the regression analysis for AI literacy dimensions predicting students’ Intrinsic/Interest Values in using AI. The overall model was significant, F(4, 145) = 6.07, p-value <.001, R² = 0.143, Adj.R² = 0.120. This shows that only a small proportion (14.3%) of the variance in Intrinsic/Interest Values can be explained by all predictors. The results also show that among these predictors, Awareness (B = -0.267, t = -1.903, p-value = 0.059), Evaluation (B = 0.125, t = 0.708, p-value = 0.480), and Ethics (B = 0.051, t = 0.362, p-value = 0.718) had no significant effects except Usage (B = 0.431, t = 3.137, p-value =0.002) which has a positive relationship with Intrinsic/Interest. This indicates that, if the other predictors are kept constant, Intrinsic/Interest Value is expected to increase by 0.431 units for every one-unit increase in Usage.
|
Independent Variable (AI Literacy) |
Model Summary |
ANOVA |
Coefficients |
|||||||
|
R |
R2 |
Adj. R2 |
F |
Sig. |
B |
β |
SE |
t |
P |
|
|
Constant |
0.379 |
0.143 |
0.120 |
6.07 |
<.001 |
2.271 |
|
0.308 |
7.364 |
< .001 |
|
Awareness |
-0.267 |
-0.284 |
0.140 |
-1.903 |
0.059 |
|||||
|
Usage |
0.431 |
0.442 |
0.138 |
3.137 |
0.002 |
|||||
|
Evaluation |
0.125 |
0.124 |
0.177 |
0.708 |
0.480 |
|||||
|
Ethics |
0.051 |
0.049 |
0.140 |
0.362 |
0.718 |
|||||
Table 12. Regression analysis of AI literacy dimensions and intrinsic/interest values of AI use
The findings also implies that students who are more skilled in using AI tools are more intrinsically motivated to use them. As students use and explore AI, they likely develop novelty, autonomy, and competence, leading to higher intrinsic motivation (Nguyen, Agarwal, Malik & Pandey, 2025). Moreover, students’ engagement becomes more pleasurable and self-rewarding as they gain confidence and proficiency with AI. This underscores the motivational value of self-directed, meaningful experiences (Cui, Jiang & Li, 2023).
Table 13 presents the multiple regression analysis predicting students’ perceived Cost of using AI based on the four dimensions of AI literacy. The overall model was statistically significant, F(4, 145) = 4.18, p‑value = 0.003, explaining 10.3% of the variance in perceived Cost (R2 =0.103; Adj. R2 = 0.079). This small but significant effect size indicates that students’ perceptions of the effort, challenge, and emotional stress they associate with using AI are affected by their overall level of AI literacy. But among the predictors, only Usage was a significant negative predictor of Cost (B = -0.296, t = -1.984, p‑value = 0.049). It implies that perceived Cost is expected to decrease by 0.296 units if Usage increases by one unit, assuming other predictors remain constant.
|
Independent Variable (AI Literacy) |
Model Summary |
ANOVA |
Coefficients |
|||||||
|
R |
R2 |
Adj. R2 |
F |
Sig. |
B |
β |
SE |
t |
P |
|
|
Constant |
0.321 |
0.103 |
0.079 |
4.18 |
0.003 |
3.728 |
|
0.335 |
11.132 |
< .001 |
|
Awareness |
0.175 |
0.175 |
0.152 |
1.146 |
0.254 |
|||||
|
Usage |
-0.296 |
-0.286 |
0.149 |
-1.984 |
0.049 |
|||||
|
Evaluation |
-0.292 |
-0.271 |
0.192 |
-1.519 |
0.131 |
|||||
|
Ethics |
0.129 |
0.175 |
0.152 |
0.848 |
0.398 |
|||||
Table 13. Regression analysis of AI literacy dimensions and perceived cost of AI use
Additionally, the results indicate that students with greater proficiency in using AI tools tend to experience lower psychological and effort-related barriers. This underscores the importance of practical and hands-on experiences with AI in increasing one’s efficiency, familiarity, and confidence while also decreasing their anxiety or difficulty perceptions about AI. According to Bandura (1997), Wigfield and Eccles (2000), Sova et al.(2024) and Chan and Zhou (2023), students are more likely to perceive AI tools as beneficial and manageable as they gain direct experience and mastery, thereby reducing the cognitive and emotional burden attributed to new technologies.
4. Discussion
The descriptive statistics indicate that the students generally demonstrated a high level of AI literacy, reflected in their strong awareness, effective use of AI tools, sound evaluation of AI outputs, and a clear sense of ethical responsibility. This high literacy may be attributed to their regular exposure to AI-powered learning platforms and the growing incorporation of AI-related content in academic settings (Singh, Vasishta & Singla, 2024; Nikoulina & Caroni, 2024; Hermosura, 2025). Their ability to think critically about technology also appears to be reinforced by their positive views of AI’s usefulness and ease of use (Davis, 1989; Vázquez-Parra et al., 2024). Nonetheless, concerns about credibility, data privacy, and institutional monitoring exist, affirming the observations of Hermosura (2025).
The students’ high level of AI literacy appears to be accompanied by strong motivational orientations toward AI use. Descriptive data show that they generally demonstrated high motivation across the Task Value subdomains—Attainment, Utility, and Intrinsic/Interest Values. This implies that AI is strongly valued by the students, especially for its importance to their academic and personal lives. This validates the role of enjoyment and relevance as key drivers of motivation as described in the Expectancy-Value Theory of Wigfield and Eccles (2000). However, some students expressed limited confidence in their ability to use AI effectively, as reflected by their moderate Expectancy Beliefs scores. This finding is often linked to limited training, ethical uncertainties, or insufficient institutional support (Osman et al., 2024; Ruiz-Rojas et al., 2024; Zhou et al., 2024; Ambika & Priya, 2025). Likewise, their moderate Cost scores indicate ongoing concerns about the time, effort, and trade-offs associated with AI engagement.
Comparative analyses based on sex and academic program provided additional insights into students’ AI literacy and motivational beliefs in using AI. Based on the t-test results, female students exhibited slightly to moderately higher evaluative skills and ethical reasoning toward AI than male students. This may reflect females’ greater sensitivities to the benefits, risks, and moral implications of AI use (Jang et al., 2022). These findings underscore the relevance of gender-responsive approaches that can better recognize and cultivate diverse evaluative and ethical perspectives, leading to a more balanced and responsible AI use across learner groups (Wang et al., 2025). Although sex did not significantly influence students’ Awareness and Usage of AI, the small effect sizes indicate that female students were more familiar and engaged with AI than their male counterparts. When compared by academic program, AI literacy levels were comparable. However, students from BSTM and BSHM programs consistently exhibited slightly higher literacy scores across all domains than BEED students. These differences imply that they have higher digital literacy (Saklaki & Gardikiotis, 2024) and digital tools are more frequently used in their curriculum (Lee et al., 2024). While statistically nonsignificant, these patterns highlight the importance of considering disciplinary contexts when designing AI-related educational interventions.
In terms of motivational beliefs, male and female students demonstrated generally similar levels of motivation in using AI. However, a slightly higher intrinsic motivation was observed among females. This may be linked to their appreciation for collaborative and creative digital activities. As Wajcman (2000) noted, females are more attracted to design-focused activities, while Weber and Custer (2005) found that they are more motivated when using technology in creating visually attractive and/or personally meaningful outputs like multimedia presentations. These findings highlight the importance of designing AI learning experiences that sustain female students’ interest. Finally, comparisons across academic programs revealed no significant differences, and their negligible effect sizes confirmed these. According to Yurt and Kasarci (2024) and Mohamed, Shaaban, Bakry, Guillén-Gámez and Strzelecki (2024), factors such as digital culture and personal experiences have a greater influence on motivation for AI use than disciplinary instruction alone.
These findings collectively indicate that while sex had a statistically significant but minimal influence on students’ motivation, individual experiences and interactions with AI played a more decisive role. Consistent with this, the correlation analysis showed that nearly all domains of AI literacy showed small to moderate positive associations with students’ motivational beliefs, except for Cost, which was negatively correlated with all domains. This pattern suggests that higher levels of AI literacy correspond to greater confidence, perceived usefulness, and enjoyment in using AI. Among the literacy domains, AI Usage exhibited the strongest positive correlations, highlighting the role of practical engagement in fostering motivation. This resonates with Bandura’s Social Cognitive Theory (1997) and Ryan and Deci’s Self‑Determination Theory (2000), which both state the importance of mastery experiences, competence, and autonomy in building self-efficacy and motivation. Overall, these findings highlight the need for effective AI integration in education, given the impact of meaningful and efficient AI use in driving motivation.
Moreover, the significant associations between Evaluation and motivational beliefs suggest the need for promoting students’ critical interactions with AI. This is to provide students with opportunities to do proper assessments of the benefits, limitations, and relevance of AI to their learning. Awareness and Ethics also showed weak correlations with motivations (except for the non-significant link between Ethics and Utility Value), suggesting that foundational knowledge of AI and moral awareness contribute to students’ motivation. Notably, all AI literacy negatively correlated with perceived Cost, with Usage showing the strongest negative correlation. Although the association was weak, it shows that if students are good at using AI, the fewer barriers they will experience. This affirms the critical role of hands-on experience in reducing cognitive and emotional load (Yin & Goh, 2024). Collectively, these results further strengthen the need for integrating real-world and reflective AI tasks to boost students’ motivation and adaptability to emerging technologies.
Multiple regression analyses provided clearer evidence of the influence of AI literacy on students’ motivational beliefs. All predictive models were significant but exhibited relatively low effect sizes, indicating that while AI literacy collectively influences students’ motivation, its impact is minimal. Behavioral research suggests that such small effects remain meaningful, as motivation is shaped by numerous contextual, personal, and situational factors. Among the four dimensions, Usage was the only consistent and statistically significant predictor of all Task Value subcomponents. Specifically, it positively predicted Attainment, Utility, and Intrinsic/Interest Values and negatively predicted Cost. In contrast, Awareness, Evaluation, and Ethics were not significant predictors of motivational beliefs. This suggests that students’ motivation to use AI comes from their actual use or application of AI in their lives. This uniform, predictive role of AI use for all motivational beliefs highlights the role of practical experience in promoting students’ engagement and reducing perceived barriers to AI use.
The framework proposed in this study offers an insightful and balanced approach to the multidimensionality of AI literacy as discussed in previous studies (Ma & Chen, 2024; Chun, Ning, Chen & Wijaya, 2025; Hossain, Biswas, Khan & Khan, 2025). Similarly, prior studies on student motivation have examined the influence of expectancy and task value on technology adoption in academic settings (Yin & Goh, 2024; Wang, King, Chai & Zhou, 2023; Yurt, 2025). The novelty of this study is the integration of two research strands, namely AI literacy and motivational beliefs, through the lens of EVT. Its focus goes beyond mere assessment of skills and attitudes. But rather, this study offers a more comprehensive understanding of the relationship between students’ psychological readiness and motivation to use AI technologies. In doing so, the study offers educators and institutions a stronger foundation for cultivating responsible, confident, and motivated AI users within the framework of Education 4.0.
5. Conclusions
This study illustrates the complex impact of AI literacy on students’ motivational beliefs in using AI. Findings show that students possessed high levels of AI literacy, marked by strong awareness, effective use, evaluative skills, and ethical discernment—largely stemming from their growing exposure to AI tools that are increasingly integrated into education. These findings encourage HEIs to continuously integrate AI-related content in their curriculum to provide students with more opportunities to strengthen their AI literacy. With this, HEIs can help promote responsible and meaningful use of AI that is reflective of global ethical standards in education. Students’ perceived values of AI in terms of Attainment, Utility, and Intrinsic motivation are also high. This suggests that they view it as meaningful, useful, and enjoyable. However, with moderate scores in Expectancy Beliefs and Cost, students are still uncertain of their ability to use AI effectively or feel overwhelmed by its more demanding learning requirements. Bridging these motivational gaps will require HEIs to provide students with training in AI, focused workshops, instructional modules for use at the school level, and mentoring programs to increase their confidence and lower the perceived costs of using it.
Comparative analyses yielded a gender-specific pattern at least for evaluations, ethical and intrinsic motivation, with female students showing slightly to moderately higher evaluations and ethical orientations, as well as slightly higher intrinsic motivations. These results highlight the need for effective gender-inclusive AI education that will acknowledge and support multiple motivations and perspectives. Thus, curriculum designers need to employ an inclusive pedagogy that will facilitate both analytical and generative AI learning. There are also strategies available to make AI more engaging and accessible for all students, such as promoting group work, case-based discussions of ethical issues, and using gender‑sensitive examples. Despite the lack of associations between students’ academic program to either their AI literacy or motivation, the subtle variations observed across groups revealed the role of curricular exposure and digital culture. This points out the need for discipline-specific AI-related training to make learning more meaningful and relevant. Significant but weak associations were identified between AI literacy and students’ motivational beliefs. Although correlation analysis revealed significant links in nearly all domains of AI literacy, the regression analyses showed that Usage was the only significant predictor of Task Values. This highlights the vital role of hands-on AI experience in enhancing students’ motivation toward AI technologies. It is therefore recommended for institutions to create more opportunities for students to actively use AI tools in authentic tasks, helping them better appreciate AI’s relevance, increase their interest and enjoyment, and reduce the perceived effort or cost associated with its use.
From a pedagogical standpoint, the findings offer valuable insights for curriculum design. They emphasize the impact of combining technical competencies and motivational support in bridging the gap between technical competence and meaningful engagement. To achieve this, curriculum frameworks should include inquiry-based and reflective learning tasks that promote AI use while developing students’ self-efficacy and value perceptions. By aligning AI instruction with experiential and value-based learning principles, educators can effectively boost confidence, ethical reflection, and long-term enthusiasm in interacting with AI tools among students. In conclusion, this study highlights that proficient use of AI can greatly increase students’ motivation to engage with AI technologies. By integrating AI applications in a meaningful and responsible way, HEIs could empower students with values alongside the practical expertise needed for the digital era to stimulate confidence and motivation to apply AI responsibly. Future studies might extend this work by investigating how AI literacy and motivation evolve through longitudinal studies, assessing actual usage behaviors alongside self-perceptions, and examining the impact of AI-integrated training across various institutional and disciplinary settings. Such directions might help to clarify how AI readiness needs to be cultivated in different educational settings.
Declaration of Conflicting Interests
The author declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Funding
The author received no financial support for the research, authorship, and/or publication of this article.
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