the Effect of Artificial Intelligence Literacy on Artificial Intelligence Anxiety: the Mediating Role of Perceived Cognitive Load
Subject Areas :
1 -
Keywords: A Literacy, A I Anxiety, Cognitive Load,
Abstract :
The purpose of this research was to Analysis of Artificial Intelligence Literacy on Artificial Intelligence Anxiety by mediating Cognitive Load On. The method of the current research was descriptive-correlation with emphasis on structural equation modelling. The statistical population of the research was all postgraduate students (320) (master's and doctoral) of the Islamic Azad University, Kazerun Branch. Based on the Krejci and Morgan table, 175 people were selected as the statistical sample of the research using simple random sampling. Data was collected using AI Anxiety scale (Wang and Wang, 2022), AI literacy scale (wang 2023) and cognitive load questionnaire (Oktaviyanthi2024). The reliability of the questionnaires was assessed using the Cronbach's Alpha Coefficient, which was equal to .89, .91, and .87 respectively. Data analysis was done with SPSS 24 and SPLS 3 software. The results showed that AI literacy has an effect on cognitive load, cognitive load has an effect on AI anxiety, and the effect of AI literacy on AI anxiety is significant (P<0.01). Also, examining the indirect effect showed that cognitive load plays a mediating role in the relationship between AI literacy and AI anxiety.
References
Allen L. K., Kendeou P. ED-AI Lit: An interdisciplinary framework for AI literacy in education. Policy Insights from the Behavioral and Brain Sciences. 2024;11(1):3–10. doi: 10.1177/23727322231220339. [DOI] [Google Scholar]
Ayduğ, D., & Altınpulluk, H. (2025). Are Turkish pre-service teachers worried about AI? A study on AI anxiety and digital literacy. AI & SOCIETY, 1-12.
Belda-Medina J., Calvo-Ferrer J. R. Using chatbots as AI conversational partners in language learning. Applied Sciences. 2022;12(17):8427. doi: 10.3390/app12178427. [DOI] [Google Scholar]
Brougham, D., & Haar, J. (2023). Smart technology, artificial intelligence, robotics, and algorithms (STARA): Employees’ perceptions of our future workplace. Journal of Management & Organization, 29(1), 1-18. [Recent workplace AI anxiety study]
Cengiz, S., & Peker, A. (2025). Generative artificial intelligence acceptance and artificial intelligence anxiety among university students: the sequential mediating role of attitudes toward artificial intelligence and literacy. Current Psychology, 44(9), 7991-8000.
Chen, X., Xie, H., Zou, D., & Hwang, G. J. (2022). Application and theory development of artificial intelligence in education: A systematic review. Educational Technology & Society, 25(1), 130–146. [https://doi.org/10.30191/ETS.202201.12](https://doi.org/10.30191/ETS.202201.12)
Cheng, X., Zhang, X., & Cohen, J. (2023). Human vs. algorithm: How cognitive load influences trust in AI decision-making. Computers in Human Behavior, 142, 107632. [Cognitive load & AI trust]
Feng, L. (2024). Investigating the Effects of Artificial Intelligence-Assisted Language Learning Strategies on Cognitive Load and Learning Outcomes: A Comparative Study. Journal of Educational Computing Research, 62(8), 1741-1774. https://doi.org/10.1177/07356331241268349 (Original work published 2025)
Herm, L. V. (2023). Impact of explainable ai on cognitive load: Insights from an empirical study. arXiv preprint arXiv:2304.08861.
Howard, S. K., Tondeur, J., Ma, J., & Yang, J. (2021). What to teach? Strategies for developing digital literacy in pre-service teacher education. Computers & Education, 169, 104212. [https://doi.org/10.1016/j.compedu.2021.104212](https://doi.org/10.1016/j.compedu.2021.104212)
Johnson DG, Verdicchio M (2017) AI anxiety. J Am Soc Inf Sci 68(9):2267–2270. https://doi.org/10.1002/asi.23867
Kalyuga, S. (2011). Cognitive load theory: How many types of load does it really need? *Educational Psychology Review, 23*(1), 1–19. [https://doi.org/10.1007/s10648-010-9150-7](https://doi.org/10.1007/s10648-010-9150-7)
Kaya F, Aydin F, Schepman A, Rodway P, Yetişensoy O, Demir Kaya M (2022) The roles of personality traits, AI anxiety, and demographic factors in attitudes toward artificial intelligence. Int J Human-Comput Interaction. https://doi.org/10.1080/10447318.2022.2151730
Long, D., & Magerko, B. (2020). What is AI literacy? Competencies and design considerations. Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (pp. 1–16). [https://doi.org/10.1145/3313831.3376727](https://doi.org/10.1145/3313831.3376727)
Makransky, G., Terkildsen, T. S., & Mayer, R. E. (2019). Adding immersive virtual reality to a science lab simulation causes more presence but less learning. *Learning and Instruction, 60,* 225–236. [https://doi.org/10.1016/j.learninstruc.2017.12.007](https://doi.org/10.1016/j.learninstruc.2017.12.007)
Mayer, R. E., & Moreno, R. (2003). Nine ways to reduce cognitive load in multimedia learning. Educational Psychologist, 38 (1), 43–52. [https://doi.org/10.1207/S15326985EP3801\_6](https://doi.org/10.1207/S15326985EP3801_6)
Ng, W. (2021). Artificial intelligence in education: A review. *Computers & Education: Artificial Intelligence, 2,* 100009. [https://doi.org/10.1016/j.caeai.2021.100009](https://doi.org/10.1016/j.caeai.2021.100009)
Paas, F., & van Merriënboer, J. J. G. (1994). Variability of worked examples and transfer of geometrical problem-solving skills: A cognitive-load approach. *Journal of Educational Psychology, 86*(1), 122–133. [https://doi.org/10.1037/0022-0663.86.1.122](https://doi.org/10.1037/0022-0663.86.1.122)
Plass, J. L., & Kalyuga, S. (2019). Four ways of considering instructional design to support cognitive load. *Educational Psychology Review, 31*(2), 339–347. [https://doi.org/10.1007/s10648-019-09466-1](https://doi.org/10.1007/s10648-019-09466-1)
Schunk, D. H., Pintrich, P. R., & Meece, J. L. (2022). *Motivation in education: Theory, research, and practice* (5th ed.). Pearson.
Su, J., & Yang, W. (2023). AI literacy and its impact on digital readiness: A meta-analysis. Educational Research Review, 39, 100512. [Meta-analysis on AI training]
Sweller, J. (1988). Cognitive load during problem solving: Effects on learning. *Cognitive Science, 12*(2), 257–285. [https://doi.org/10.1207/s15516709cog1202\_4](https://doi.org/10.1207/s15516709cog1202_4)
Xie, H., Chu, H. C., Hwang, G. J., & Wang, C. C. (2023). AI-based learning analytics in educational technology research: Theoretical perspectives and research trends. *Computers & Education, 182,* 104463. [https://doi.org/10.1016/j.compedu.2022.104463](https://doi.org/10.1016/j.compedu.2022.104463)
Yuen, A. H. K., Park, J., Chen, L., & Cheng, M. (2021). Knowledge, attitudes, and anxiety of university students toward artificial intelligence. Computers & Education: Artificial Intelligence, https://doi.org/10.1016/j.caeai.2021.100014)
Zhang, B., & Dafoe, A. (2019). Artificial intelligence: American attitudes and trends. Center for the Governance of AI, Future of Humanity Institute, University of Oxford. [https://governanceai.eu](https://governanceai.eu)
Zhang, Q., Nie, H., Fan, J., & Liu, H. (2025). Exploring the dynamics of artificial intelligence literacy on english as a foreign language learners’ willingness to communicate: The critical mediating roles of artificial intelligence learning self-efficacy and classroom anxiety. Behavioral Sciences, 15(4), 523.
Zhang, X., & Zhu, F. (2022). How AI literacy reduces anxiety and increases adoption intention. Journal of Business Research, 145, 180–191. [AI literacy & anxiety
Mashayekh: the Effect of Artificial Intelligence Literacy on Artificial Intelligence Anxiety…
ISSN (print): 2822-8771 E-ISSN: 7060-6878
The Mediating Role of Perceived Cognitive Load in the relationship between Artificial Intelligence Literacy and Artificial Intelligence Anxiety
Pari Mashayekh *1
Received Date:24/10/2024 Accepted Date:16/12/2024
Pp: 185-196
Abstract
The expansion of AI applications in higher education increases the need for students to be AI literate in order to understand and effectively use this technology. Lack of sufficient knowledge in this area can lead to anxiety, worry, and feelings of helplessness. In this regard, this study aims to examine the mediating role of Cognitive Load in the relationship between Artificial Intelligence Literacy and Artificial Intelligence Anxiety. The method of the current research was descriptive-correlation with emphasis on structural equation modelling. The statistical population of the research was all postgraduate students (320) (master's and doctoral) of the Islamic Azad University, Kazerun Branch. Based on the Krejci and Morgan table, 175 people were selected as the statistical sample of the research using simple random sampling. Data was collected using AI Anxiety scale (Wang and Wang, 2023), AI literacy scale (wang,Ru and Uan, 2023) and cognitive load questionnaire (Oktaviyanthi et al, 2024). The reliability of the questionnaires was evaluated using Cronbach's alpha coefficients, which were .89, .91, and .87, respectively—indicating high internal consistency. Additionally, content validity was assessed through the Content Validity Ratio (CVR), with all items scoring above 0.70, thereby confirming their acceptable level of validity. Data analysis was done with SPSS 24 and SPLS 3 software. The results showed that AI literacy has an effect on cognitive load, cognitive load has an effect on AI anxiety, and the effect of AI literacy on AI anxiety is significant (P<0.01). Also, examining the indirect effect showed that cognitive load plays a mediating role in the relationship between AI literacy and AI anxiety. Overall, the results of the study indicate that perceived cognitive load can be an important psychological factor that shapes the path of AI literacy’s impact on AI-related anxiety. Understanding and managing this cognitive load plays a key role in reducing students’ anxiety and enhancing their technological learning experience. These results emphasize the importance of designing learning environments with controlled cognitive load and targeted training in enhancing students’ psychological experience when encountering smart technologies.
Keywords: Artificial Intelligence Literacy - Artificial Intelligence Anxiety- Cognitive Load.
[1] - Assistant professor, Department of Educational Administration, Kazerun Branch, Islamic Azad University, Kazerun, Iran
Introduction
Artificial intelligence (AI) in education has emerged as a rapidly evolving multidisciplinary field that leverages AI to enhance teaching, learning, instructional design, and assessment (Ouyang & Jiao, 2021). AI applications are now widely adopted in universities and industries, ranging from data analytics platforms to writing assistants and intelligent tutoring systems. Students, particularly at the graduate level, are increasingly expected to use these technologies for research, communication, and problem-solving (Luckin et al, 2016). Universities now encourage all students—not only those in computer science or AI-related disciplines—to develop the skills necessary for engaging with AI in their future careers (Southworth et al, 2023).
As AI becomes embedded in educational, professional, and everyday life, understanding how individuals interact with and emotionally respond to AI is essential (Long & Magerko, 2020).
Although AI is increasingly integrated into higher education, many graduate students report experiencing AI anxiety. It defined as stress or apprehension related to interacting with AI technologies (Shen et al, 2023).
AI anxiety encompasses fears that AI is uncontrollable or may negatively impact personal and social life (Kaya et al., 2022). Similar to earlier notions of computer anxiety (Nomura et al, 2008), AI anxiety refers to an overall affective response of anxiety or fear and feelings of agitation about out-of-control AI that inhibits an individual from interacting with AI (Johnson & Verdicchio, 2017). Although AI anxiety is very similar to computer anxiety, there are some key differences. First, AI makes autonomous decisions; thus, it operates without human interference, leading to problems of artificial consciousness. Its decisions lack transparency, generating unpredictable risks. Second, when AI programs are making decisions, they use a large amount of data analysis to determine the strengths and weaknesses of action options, but human values and ethics are not considered, resulting in ethical violation issues (Wang et al,2022). Such anxiety can undermine motivation, lead to avoidance behaviors, and negatively affect academic performance and professional readiness (Shen et al., 2023).
Research consistently shows that AI literacy plays a key role in reducing AI anxiety (Zhang & Zhu, 2022). Higher AI literacy is associated with greater confidence and willingness to use AI tools, while lower literacy is linked to greater apprehension (Lee et al , 2024). AI literacy encompasses conceptual knowledge of AI, critical awareness of its societal impact, and practical skills to engage responsibly with AI systems without necessarily developing them (Wang, Cui, & Yuan, 2025).
Another critical factor linked to AI anxiety is cognitive load, which refers to the mental effort required to process information and complete tasks. Cognitive load theory (CLT) explains that learning is constrained by limited working memory, and excessive cognitive demands can increase stress and reduce performance(Sweller, 2011). Educational technology research shows that high cognitive load - often caused by new or complex systems- can result in frustration, disengagement, and technology-related anxiety (Su & Yang, 2023).
In the context of AI, students with lower AI literacy may experience higher cognitive load when interacting with AI systems, which in turn may exacerbate AI anxiety (Lee et al., 2024). Conversely, better AI literacy reduces cognitive load and supports more effective, less stressful engagement with AI technologies (Chen et al., 2024). Various researches have been conducted in this field, including: Zhang (2025) demonstrated that AI literacy improved teachers’ AI self-efficacy and reduced job-related anxiety. Chen et al. (2024) found that AI learning self-efficacy mediated the relationship between AI literacy and classroom anxiety, while
Similarly, Cengiz & Peker (2025) showed that AI literacy and attitudes toward AI sequentially mediated the relationship between AI acceptance and AI anxiety among university students. Ayduğ and Altınpulluk (2025) also observed that AI anxiety was negatively associated with digital literacy among preservice teachers, emphasizing the importance of AI literacy in professional training. Moazami and Alimoradi (2025) also in a descriptive correlational study on students at the University of Tehran found that artificial intelligence tools have a positive and significant effect on students' cognitive load.
Omidi and Jame Bozorgy (2025) evaluated AI literacy and analyzed its conceptual structure among students of Allameh Tabatabaei University, focusing on three components of AI literacy, AI self-efficacy, and AI self-management using a descriptive-analytical method, and showed that the average scores of students in all three components were significantly higher than the theoretical average level. The students studied had a relatively favorable level of AI literacy.
Despite growing evidence linking AI literacy to reduced anxiety, few studies have examined whether cognitive load mediates this relationship, particularly among graduate students. Graduate students represent a critical population because they frequently engage with advanced AI-driven research tools and are preparing for careers in increasingly AI-augmented industries. This study seeks to address this gap by investigating whether perceived cognitive load serves as a mediating mechanism between AI literacy and AI anxiety among graduate students. Therefore, the research hypotheses are examined as:
1. AI literacy has a significant effect on AI anxiety.
2. AI literacy has a significant effect on perceived cognitive load.
3. Perceived cognitive load has a significant effect on AI anxiety.
4. Perceived cognitive load plays a significant mediating role in the relationship between AI literacy and AI anxiety.
Figure 1: Research concept model
Methodology
A quantitative approach correlational research method was applied to achieve the research objectives. Partial least squares structural equation modeling (PLS-SEM) was employed to test both the direct and moderating effects among the study variables. The statistical population of the research was all postgraduate students (320) (master's and doctoral) of the Islamic Azad University, Kazerun Branch. Based on the Krejci and Morgan table, 175 people were selected as the statistical sample of the research using simple random sampling.
Data were collected using a structured questionnaire comprising two sections. The first section collected demographic information, including age, gender, education level, and field of study.
The second section assessed the three main variables of interest: AI literacy, AI anxiety, and cognitive load. All scales employed a 5-point Likert response format, ranging from 1 (strongly disagree) to 5 (strongly agree).
AI anxiety was measured using the AI Anxiety Scale developed by Wang and Wang (2022), which includes 21 items across four dimensions: AI Learning Anxiety, Job Replacement Anxiety, Sociotechnical Blindness, and AI Configuration Anxiety. Reported reliability coefficients for these subscales are α = .97 (Wang & Wang, 2022). AI literacy was measured using an adapted 12-item scale developed by Wang et al. (2023). This scale evaluates students’ competence in four domains: AI awareness, practical usage, evaluation of AI technologies, and ethical considerations. Cognitive load was assessed using an adapted version of the cognitive load measurement framework proposed by Sweller et al. (2011). This instrument measures three components of cognitive load: intrinsic, extraneous, and germane cognitive load.
the reliability of the questionnaires was assessed using Cronbach's alpha coefficient, which exceeded 0.80, indicating high internal consistency. Additionally, the content validity of the questionnaires was evaluated using the Content Validity Ratio (CVR), and all items scored above 0.70, confirming their validity.
A total of 185 students with a mean age of 32.8 and a standard deviation of 6.4 participated in this study, 84.8% (157 at the master's level) and 28.15% were studying at the doctoral level. 58.91 percent (109 people) were women and 14.08 percent (76 people) were men.
Research Findings
The partial least-squares structural equation modeling (PLS-SEM) approach was used to test the proposed research model. because It places minimal demands on sample size and residual distributions for estimating the model parameters. The normality of the data distribution was checked using the Kolmogorov-Smirnov test. As can be seen in Table 1, all of the research variables were not normal and it was better to use the partial least squares method (PLS).
Table 1- Kolmogorov-Smirnov for normality
| Kolmogorov-Smirnov -z | Sig |
AI anxiety | 0.49 | 0.23 |
AI literacy | 0.51 | 0.21 |
cognitive load | 0.55 | 0.22 |
The initial step in evaluating the measurement model in PLS was to assess indicator reliability through factor loadings. According to Heer and Ringel (2011), factor loadings should ideally exceed 0.70 to demonstrate adequate item reliability. However, items with loadings slightly below 0.70 can be retained if they do not negatively influence the overall model fit and are theoretically justified by their association with other indicators. Figure 1 presents the initial reflective measurement model, displaying standardized coefficients for all indicators. As shown, all factor loadings exceeded the recommended threshold of 0.70, indicating satisfactory indicator reliability.
Figure 1 : standard coefficients
Table 1: Evaluation of Measurement Model
| AVE (≥0.50) | CA (≥0.70) | RHO_A (≥0.70) | CR (≥0.70) |
AI anxiety | 0.52 | 0.93 | 0.93 | 0.94 |
AI literacy | 0.55 | 0.91 | 0.91 | 0.92 |
cognitive load | 0.63 | 0.92 | 0.91 | 0.95 |
As shown in Table 1, all reflective constructs demonstrated satisfactory reliability, as indicated by Composite Reliability (CR) values exceeding the recommended threshold of 0.70 for all variables. Convergent validity was also established through the Average Variance Extracted (AVE), which measures the amount of variance captured by a construct relative to the variance attributed to measurement error. All constructs reported AVE values above 0.50, indicating that each construct explains more than 50% of the variance in its indicators (Hair et al., 2014). These results confirm that the measurement model meets the standard criteria for internal consistency reliability and convergent validity.
Table 1: Evaluation of Measurement Model
| AVE (≥0.50) | CA (≥0.70) | RHO_A (≥0.70) | CR (≥0.70) |
AI anxiety | 0.52 | 0.93 | 0.93 | 0.94 |
AI literacy | 0.55 | 0.91 | 0.91 | 0.92 |
cognitive load | 0.63 | 0.92 | 0.91 | 0.95 |
To further establish the discriminant validity of the reflective constructs, the Fornell–Larcker criterion was applied. According to this criterion, the square root of the Average Variance Extracted (AVE) for each construct should exceed its correlations with any other construct (Fornell & Larcker, 1981). As shown in Table 2, the bolded diagonal values represent the square roots of the AVE for each construct, all of which are greater than their respective inter-construct correlations. These results confirm that each construct is empirically distinct from the others, thereby supporting discriminant validity
Table 2. Discriminant Validity: The Fornell Larcker
| AI anxiety | AI literacy | cognitive load |
AI anxiety | 0.96 | - | - |
AI literacy | 0.95 | O.93 | - |
cognitive load | 0.94 | 0.91 | 0.79 |
Another approach to assessing discriminant validity is through the examination of cross-loadings. According to Hair et al. (2016), an item should exhibit a higher loading on its associated latent construct than on any other construct in the model. The results of this analysis confirmed adequate discriminant validity, as all items loaded more strongly on their respective constructs than on alternative constructs. Table 3 presents the detailed cross-loading results supporting these findings.
Table 3. Discriminant Validity: The HTMT
| AI anxiety | AI literacy | cognitive load |
AI anxiety | - | - | - |
AI literacy | 0.89 | - | - |
cognitive load | 0.86 | 0.88 | - |
To test the predetermined hypotheses, structural equation modeling (SEM) was conducted using the bootstrap method to assess indirect effects (Hair et al., 2014). As shown in Table 4, the results indicate that AI literacy is significantly related to cognitive load (β = 0.58, p < 0.05), supporting Hypothesis 1. Likewise, cognitive load was found to be significantly associated with AI anxiety (β = 0.42, p < 0.05), providing empirical support for Hypothesis 2. Furthermore, AI literacy was also significantly related to AI anxiety (β = 0.92, p < 0.05), confirming Hypothesis 3.
Table4: Results of Path Coefficient: Direct Effects
p.value | t | B | Path |
.05 | 71.18 | 0.58 | AI literacy - cognitive load |
.05 | 10.19 | 0,92 | AI literacy - AI anxiety |
.05 | 7.53 | 0.42 | cognitive load- AI anxiety |
To assess the indirect effect of cognitive load, the bootstrap method was employed due to its robustness and accuracy in estimating mediation effects. The significance of the indirect relationship was evaluated using two criteria: (1) the significance level (p-value), and (2) the 95% confidence interval (CI) of the mediation effect. Specifically, if the lower and upper bounds of the 95% CI are both either positive or negative, and the interval does not include zero,the mediation effect is considered statistically significant(Preacher & Hayes, 2008).
Table 5: Results of Path Coefficient: Indirect Effects
Bootstrap limits | p.value | T | Path | |
Upper bound | Lower bound | |||
0.49 | 0.26 | 0.05 | 6.44 | AI literacy - cognitive load - AI anxiety |
As shown in Table 5, the bootstrap results indicate that cognitive load significantly mediates the relationship between AI literacy and AI anxiety. This finding confirms the hypothesized mediating role of cognitive load in linking AI literacy to AI-related anxiety
Fi
Figure2: t-value
Discussion and conclusion
The present study investigated the relationship between AI literacy, cognitive load, and AI anxiety among university students, focusing specifically on the mediating role of cognitive load. The results indicate that cognitive load significantly mediates the association between AI literacy and AI anxiety. In particular, students with higher AI literacy experienced reduced cognitive load when engaging with AI-related concepts and tasks, which, in turn, was associated with lower levels of AI-related anxiety. Conversely, limited AI literacy was linked to heightened cognitive load and increased anxiety toward AI technologies
The negative relationship observed between AI literacy and AI anxiety is consistent with prior research demonstrating that technological literacy can alleviate fear and resistance to emerging technologies (Long & Magerko, 2020; Zhang & Dafoe, 2019). Increased literacy reduces uncertainty and enhances individuals’ perceptions of control, thereby mitigating negative emotional responses (Ng, 2021). In the specific context of AI, literacy equips individuals with an understanding of the functionality, limitations, and implications of AI, reducing misconceptions and unfounded fears often rooted in a lack of exposure or knowledge (Yuen et al.,2021). The current findings align with these observations while extending existing knowledge by identifying cognitive processing as an underlying mechanism through which literacy influences emotional outcomes.
A key contribution of this study lies in the identification of cognitive load as a mediator. According to cognitive load theory, working memory has limited capacity, and excessive task complexity or unfamiliarity increases cognitive demands, which can result in negative affective states such as anxiety and frustration (Plass & Kalyuga, 2019). While cognitive load has been extensively studied in educational technology contexts, including virtual reality learning (Makransky et al., 2019) and intelligent tutoring systems (Kalyuga, 2011), its mediating role in AI-related emotional responses has received little attention.
The findings of this study contribute to filling this gap by illustrating that limited AI literacy may impose greater cognitive demands on learners, thereby amplifying anxiety. This aligns with emerging evidence from technology-enhanced learning, where cognitive load is recognized as a critical determinant of learner engagement, performance, and emotional well-being (Chen et al., 2022). The mediating effect observed here supports broader cognitive–affective frameworks that posit that higher mental effort, particularly when coupled with novelty and complexity, triggers avoidance behaviors and negative emotional reactions (Schunk et al., 2022).
References
Ayduğ, D., Altınpulluk, H. (2025). Are Turkish pre-service teachers worried about AI? study on AI anxiety and digital literacy. AI & society, 1-12.
Cengiz, S., & Peker, A. (2025). Generative artificial intelligence acceptance and artificial intelligence anxiety among university students: the sequential mediating role of attitudes toward artificial intelligence and literacy. Current Psychology, 44(9), 7991-8000.
Chen, S.Y., Su, Y.S., Ku, Y.Y., Lai, C.F., Hsiao, K.L.(2024). Exploring the factors of students’ intention to participate in AI software development. Libr. hi Tech. 42 (2),392–408.
Chen, X., Xie, H., Zou, D., & Hwang, G. J. (2022). Application and theory development of artificial intelligence in education: A systematic review. Educational Technology & Society, 25(1), 130–146.
Cheng, X., Zhang, X., & Cohen, J. (2023). Human vs. algorithm: How cognitive load influences trust in AI decision-making. Computers in Human Behavior, 142, 107632
Hair, J. F., Hollingsworth, C. L., Randolph, A. B., & Chong, A. Y. L. (2017b). An updated and expanded assessment of PLS-SEM in information systems research. Industrial Management & Data Systems, 117, 442–458.
Johnson, D. G., & Verdicchio, M. (2017). AI anxiety. Journal of the Association for Information Science and Technology, 68(9), 2267–2270.
Kalyuga, S. (2011). Cognitive load theory: How many types of load does it really need? Educational Psychology Review, 23 (1), 1–19.
Kaya F, Aydin F, Schepman A, Rodway P, Yetişensoy O, Demir Kaya M (2022) The roles of personality traits, AI anxiety, and demographic factors in attitudes toward artificial intelligence. Int J Human-Comput Interaction.
Lee, J., Hong, S., Lee, J., & Kim, H. (2024). The effect of AI literacy education on college students’ AI anxiety and acceptance: Focusing on moderated mediation effect of social influence. Technology in Society, 76, 102556.
Long, D., & Magerko, B. (2020). What is AI literacy? Competencies and design considerations. Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (pp. 1–16).
Luckin, R., Holmes, W., Griffiths, M., & Forcier, L. B. (2016). Intelligence Unleashed: An Argument for AI in Education..
Makransky, G., Terkildsen, T. S., & Mayer, R. E. (2019). Adding immersive virtual reality to a science lab simulation causes more presence but less learning. Learning and Instruction, 60, 225–236.
Moazami Mojtaba and Ali Moradi Zeinab (2025). Investigating the effect of using artificial intelligence tools on cognitive load and future thinking of educational management students at the University of Tehran, New Approaches to Islamic Studies 7 (23), 1-21 (in Persian)
Mostafa Omidi, & Zahra Jamehbozorg. (2025). Assessment of Artificial Intelligence Literacy and Analysis of Its Conceptual Structure Among Students of Allameh Tabatabaei University. Management, Education and Development in the Digital Age, 2(2). (in Persian)
Ng, W. Y., Cheung, C. Y., Milea, D., & Ting, D. S. W. (2021). Artificial intelligence and machine learning for Alzheimer’s disease: let’s not forget about the retina. British Journal of Ophthalmology, 105(5), 593-594.
Nomura, T., Kanda, T., Suzuki, T., & Kato, K. (2008). Prediction of human behavior in human-robot interaction using psychological scales for anxiety and negative attitudes toward robots. Transactions on Robotics, 24(2), 442–451.
Oktaviyanthi, R., Agus, R. N., Garcia, M. L. B., & Lertdechapat, K. (2024). Cognitive load scale in learning formal definition of limit: A rasch model approach. Infinity, 13(1), 99-118.
Ouyang, F., & Jiao, P. (2021). Artificial intelligence in education: The three paradigms. Computers and Education: Artificial Intelligence, 2, 100020.
Plass, J. L., & Kalyuga, S. (2019). Four ways of considering instructional design to support cognitive load. Educational Psychology Review, 31 (2), 339–347.
Schunk, D. H., Pintrich, P. R., & Meece, J. L. (2022). Motivation in education: Theory, research, and practice (5th ed.).
Shen, Y., Song, K., Tan, X., Li, D., Lu, W., & Zhuang, Y. (2023). Hugginggpt: Solving ai tasks with chatgpt and its friends in hugging face. Advances in Neural Information Processing Systems, 36, 38154-38180.
Southworth, J., Migliaccio, K., Glover, J., Glover, J., Reed, D., McCarty, C. et al. (2023). Developing a Model for AI across the Curriculum: Transforming the Higher Education Landscape via Innovation in AI Literacy Computer and Education: Artificial Intelligence
Su, J., & Yang, W. (2023). AI literacy and its impact on digital readiness: A meta-analysis. Educational Research Review, 39, 10051
Sweller, J. (2011). Cognitive load theory. Psychology of Learning and Motivation, 55, 37–76. [Foundational CLT]
Wang , B., Rau, P. L. P., & Yuan, T. (2022). Measuring user competence in using artificial intelligence: validity and reliability of artificial intelligence literacy scale. Behaviour & Information Technology, 42(9), 1324–1337
Wang, K., Cui, W., & Yuan,X. (2025). Artificial Intelligence in Higher Education: The Impact of Need Satisfaction on Artificial Intelligence Literacy Mediated by Self-Regulated Learning Strategies. Behavioral Sciences, 15(2), 165.
Wang, Y. M., Wei, C. L., Lin, H. H., Wang, S. C., & Wang, Y. S. (2022). What drives students’ AI learning behavior: a perspective of AI anxiety. Interactive Learning Environments, 32(6), 2584–2600.
Wang, Y. Y., & Wang, Y. S. (2022). Development and validation of an artificial intelligence anxiety scale: An initial application in predicting motivated learning behavior. Interactive Learning Environments, 30(4), 619-634.
Xie, H., Chu, H. C., Hwang, G. J., & Wang, C. C. (2023). AI-based learning analytics in educational technology research: Theoretical perspectives and research trends. Computers & Education, 182, 104463.
Yuen, A. H. K., Park, J., Chen, L., & Cheng, M. (2021). Knowledge, attitudes, and anxiety of university students toward artificial intelligence. Computers & Education: Artificial Intelligence,
Zhang, B., & Dafoe, A. (2019). Artificial intelligence: American attitudes and trends. Center for the Governance of AI, Future of Humanity Institute, University of Oxford.
Zhang, Q., Nie, H., Fan, J., & Liu, H. (2025). Exploring the dynamics of artificial intelligence literacy on english as a foreign language learners’ willingness to communicate: The critical mediating roles of artificial intelligence learning self-efficacy and classroom anxiety. Behavioral Sciences, 15(4), 523.
Zhang, X., & Zhu, F. (2022). How AI literacy reduces anxiety and increases adoption intention. Journal of Business Research, 145, 180–191. [AI literacy & anxiety
