Üniversite Öğrencilerinde Yapay Zekâ Okuryazarlığı, Yapay Zekâya Yönelik Tutumlar ve Teknostres Arasındaki İlişkinin İncelenmesi

Abstract

Yapay zekâ (YZ) teknolojileri, öğrencilerin öğrenme süreçlerini ve dijital deneyimlerini etkileyerek eğitim ortamlarına hızla nüfuz etmiştir. Bu çalışmanın amacı, lisans öğrencilerinde yapay zekâ okuryazarlığı, yapay zekâya yönelik tutumlar ve teknostres arasındaki ilişkileri incelemektir. Araştırmada kesitsel, betimsel ve ilişkisel bir desen kullanılmıştır. Veriler, Haziran 2025’te Artvin Çoruh Üniversitesi Sağlık Bilimleri, Eğitim, Fen-Edebiyat ve Mühendislik fakültelerinde öğrenim gören 400 lisans öğrencisinden toplanmıştır. Veri toplama araçları Sosyodemografik Bilgi Formu, Yapay Zekâ Okuryazarlığı Ölçeği (AILS), Yapay Zekâya Yönelik Genel Tutumlar Ölçeği (GAAIS) ve Teknostres Ölçeği’nden oluşmaktadır. Verilerin analizinde SPSS 26.0 programı kullanılarak betimsel istatistikler, Pearson korelasyon analizi ve çoklu regresyon analizi uygulanmıştır. Elde edilen bulgular, yapay zekâya yönelik olumlu tutumların yapay zekâ okuryazarlığı ile anlamlı ve pozitif yönde ilişkili olduğunu; teknostresin ise hem olumlu tutumlar hem de yapay zekâ okuryazarlığının değerlendirme alt boyutu ile negatif yönde ilişkiler gösterdiğini ortaya koymuştur. Regresyon analizi sonuçları, akademik başarı (GNO), bilişim teknolojileri yeterliği ve olumlu tutumların yapay zekâ okuryazarlığının anlamlı yordayıcıları olduğunu; olumsuz tutumların ise yapay zekâ okuryazarlığı üzerinde azaltıcı bir etki gösterdiğini ortaya koymuştur. Buna karşın, teknostres anlamlı bir yordayıcı olarak belirlenmemiştir. Bu bulgular, yükseköğretimde yapay zekâ entegrasyonunun çok boyutlu doğasına işaret etmektedir. Çalışmanın sonuçları, yapay zekâ okuryazarlığının artırılmasının yalnızca teknik yeterliklerin gelişimiyle değil, aynı zamanda daha olumlu algıların oluşması ve teknolojiye bağlı stresin azalmasıyla da ilişkili olduğunu göstermektedir. Bu doğrultuda, eğitsel stratejilerin öğrencilerin yapay zekâ okuryazarlığını güçlendirmeye, dengeli ve bilinçli tutumlar geliştirmeye ve teknostresle başa çıkmalarını destekleyecek baş etme mekanizmaları kazandırmaya odaklanması önerilmektedir. Bu çalışma, dijital çağda öğrencilerin uyumunu ve akademik başarısını desteklemek amacıyla yapay zekâ eğitimine bilişsel, duyuşsal ve psikolojik boyutların entegre edilmesinin önemini vurgulayarak alanyazına katkı sağlamayı amaçlamaktadır.

Keywords

• yapay zekâ okuryazarlığı
• yapay zekâya yönelik tutumlar
• teknostres
• yükseköğretim
• üniversite öğrencileri

Examining the Relationship Between Artificial Intelligence Literacy, Attitudes Toward Artificial Intelligence, and Technostress Among University Students

Abstract

Artificial intelligence (AI) technologies have rapidly permeated educational contexts, influencing students’ learning processes and digital experiences. This study aimed to examine the interrelationships among AI literacy, attitudes toward AI, and technostress in undergraduate students. A cross-sectional, descriptive, and correlational design was employed. Data were collected in June 2025 from 400 undergraduate students enrolled in the Faculties of Health Sciences, Education, Arts and Sciences, and Engineering at Artvin Çoruh University. Data collection instruments included a Sociodemographic Information Form, the Artificial Intelligence Literacy Scale (AILS), the General Attitudes Toward Artificial Intelligence Scale (GAAIS), and the Technostress Scale. Descriptive statistics, Pearson’s correlation, and multiple regression analyses were conducted using SPSS 26.0. The results indicated that positive attitudes toward AI were significantly and positively correlated with AI literacy, whereas technostress showed negative associations with both positive attitudes and the evaluation subdimension of AI literacy. Regression analysis further revealed that academic achievement (GPA), IT competence, and positive attitudes were significant predictors of AI literacy, while negative attitudes exerted a diminishing effect. Technostress, however, did not emerge as a significant predictor. These findings highlight the multifaceted nature of AI integration in higher education. The findings of this study indicate that enhancing AI literacy is associated not only with improved technical competence but also with more positive perceptions and reduced technology-related stress. Educational strategies should therefore focus on strengthening students’ AI literacy, promoting balanced and informed attitudes, and equipping learners with coping mechanisms to manage technostress. This study aims to contribute to the growing body of literature by emphasizing the importance of integrating cognitive, emotional, and psychological dimensions into AI education to support students’ adaptation and academic success in the digital era.

Keywords

• artificial intelligence literacy
• attitudes toward artificial intelligence
• technostress
• higher education
• university students

References

1. Al Omari, O., Alshammari, M., Al Jabri, W., Al Yahyaei, A., Aljohani, K. A., Sanad, H. M., Al-Jubouri, M. B., Bashayreh, I., Fawaz, M., & ALBashtawy, M. (2024). Demographic factors, knowledge, attitude and perception and their association with nursing students’ intention to use artificial intelligence (AI): A multicentre survey across 10 Arab countries. BMC Medical Education, 24(1), 1456. https://doi.org/10.1186/s12909-024-06452-5
2. Ali, O., Murray, P. A., Momin, M., Dwivedi, Y. K., & Malik, T. (2024). The effects of artificial intelligence applications in educational settings: Challenges and strategies. Technological Forecasting and Social Change, 199, 123076. https://doi.org/10.1016/j.techfore.2023.123076
3. Almaiah, M. A., Alfaisal, R., Salloum, S. A., Hajjej, F., Thabit, S., El-Qirem, F. A., Lutfi, A., Alrawad, M., Al Mulhem, A., & Alkhdour, T. (2022). Examining the impact of artificial intelligence and social and computer anxiety in e-learning settings: Students’ perceptions at the university level. Electronics, 11(22), 3662.
4. Ayed, A., Ejheisheh, M. A., Al-Amer, R., Aqtam, I., Ali, A. M., Othman, E. H., Farajallah, M., Qaddumi, J., & Batran, A. (2025). Insights into the relationship between anxiety and attitudes toward artificial intelligence among nursing students. BMC Nursing, 24(1), 812. https://doi.org/10.1186/s12912-025-03490-2
5. Bagaskara, D., Hasanudin, H., Karsim, K., Kalis, M. C. I., & Kalis, I. D. I. (2024). the influence of artificial intelligence (AI) and information communications technology (ICT) on employee mental health with technostress as mediation in generation Z employees in West Kalimantan. ECo-Buss, 7(2), 1147–1162. https://doi.org/10.32877/eb.v7i2.1781
6. Baidoo-Anu, D., & Ansah, L. O. (2023). Education in the era of generative artificial intelligence (AI): Understanding the potential benefits of ChatGPT in promoting teaching and learning. Journal of AI, 7(1), 52–62. https://doi.org/10.61969/jai.1337500
7. Baş, M., Balaban, F., & Balcı, S. (2021). Üniversite öğrencilerinin teknoloji destekli öğrenme ortamında teknostres düzeyleri ölçeğinin türkçeye uyarlanması: Geçerlik-güvenirlik çalışması. EKEV Akademi Dergisi, 87, 451–470.
8. Bećirović, S., Polz, E., & Tinkel, I. (2025). Exploring students’ AI literacy and its effects on their AI output quality, self-efficacy, and academic performance. Smart Learning Environments, 12(1), 29. https://doi.org/10.1186/s40561-025-00384-3

9. Bewersdorff, A., Hornberger, M., Nerdel, C., & Schiff, D. S. (2025). AI advocates and cautious critics: How AI attitudes, AI interest, use of AI, and AI literacy build university students’ AI self-efficacy. Computers and Education: Artificial Intelligence, 8, 100340. https://doi.org/10.1016/j.caeai.2024.100340
10. Brauner, P., Glawe, F., Liehner, G. L., Vervier, L., & Ziefle, M. (2025). Mapping public perception of artificial intelligence: Expectations, risk–benefit tradeoffs, and value as determinants for societal acceptance. Technological Forecasting and Social Change, 220, 124304. https://doi.org/10.1016/j.techfore.2025.124304
11. Cave, S., Coughlan, K., & Dihal, K. (2019). “ Scary Robots” Examining Public Responses to AI. Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society, 331–337. https://doi.org/10.1145/3306618.3314232
12. Çelebi, C., Yılmaz, F., Demir, U., & Karakuş, F. (2023). Artificial intelligence literacy: An adaptation study. Instructional Technology and Lifelong Learning, 4(2), 291–306. https://doi.org/10.52911/itall.1354841
13. 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. https://doi.org/10.1007/s12144-023-04732-1
14. Chang, P.-C., Zhang, W., Cai, Q., & Guo, H. (2024). Does AI-driven technostress promote or hinder employees’ artificial intelligence adoption intention? A moderated mediation model of affective reactions and technical self-efficacy. Psychology Research and Behavior Management, 413–427. https://doi.org/10.2147/PRBM.S462052
15. Chen, F. (2025). The relationship between digital literacy and college students’ academic achievement: The chain mediating role of learning adaptation and online self-regulated learning. Frontiers in Psychology, 16, 1590649. https://doi.org/10.3389/fpsyg.2025.1590649
16. Chen, N., Zhao, X., & Wang, L. (2024). The effect of job skill demands under artificial intelligence embeddedness on employees’ job performance: A moderated double-edged sword model. Behavioral Sciences, 14(10), 974. https://doi.org/10.3390/bs14100974
17. Chukwuere, J. E., & Chukwuere, G. C. J. (2024). Technostress factors among students in the adoption of Generative Artificial Intelligence (GenAI) in higher education: A rapid review narrative. International Conference on Emerging Technology and Interdisciplinary Sciences, 37–45. https://doi.org/10.1109/ETIS59833.2024.10719758
18. Cicero, L., Russo, A., Di Stefano, G., & Zammitti, A. (2025). The General Attitudes towards Artificial Intelligence Scale (GAAIS): Validation and psychometric properties analysis in the Italian context. BMC Psychology, 13(1), 641. https://doi.org/10.1186/s40359-025-02935-2
19. Clercq, M., Galand, B., Dupont, S., & Frenay, M. (2013). Achievement among first-year university students: An integrated and contextualised approach. European Journal of Psychology of Education, 3(28), 641–662. https://doi.org/10.1007/s10212-012-0133-5
20. Cohen, J. (2013). Statistical Power Analysis for the Behavioral Sciences (2nd ed.). Lawrence Erlbaum Associates. https://doi.org/10.4324/9780203771587
21. Du, Y. (2024). The impact of artificial intelligence on people’s daily life. FSST, 6, 12–18. https://doi.org/10.11648/j.fs.20240601.12
22. Fan, J., & Zhang, Q. (2024). From literacy to learning: The sequential mediation of attitudes and enjoyment in AI-assisted EFL education. Heliyon, 10(17), e37158. https://doi.org/10.1016/j.heliyon.2024.e37158
23. Fast, E., & Horvitz, E. (2017). Long-term trends in the public perception of artificial intelligence. Proceedings of the AAAI Conference on Artificial Intelligence, 31(1). https://doi.org/10.1609/aaai.v31i1.11231
24. Feng, X., & Liu, H. (2024). I feel blue–teacher, can you help me? A study on the effect of digital literacies on language learners’ technostress, on-line engagement, autonomy, and academic success. BMC Psychology, 12(1), 143. https://doi.org/10.1186/s40359-024-02169-5
25. Francis, N. J., Jones, S., & Smith, D. P. (2025). Generative AI in higher education: Balancing innovation and integrity. British Journal of Biomedical Science, 81, 14048. https://doi.org/10.3389/bjbs.2025.14048
26. Hayat, A. A., Shateri, K., Amini, M., & Shokrpour, N. (2020). Relationships between academic self-efficacy, learning-related emotions, and metacognitive learning strategies with academic performance in medical students: A structural equation model. BMC Medical Education, 20(1), 76. https://doi.org/10.1186/s12909-020-01995-9
27. Hornberger, M., Bewersdorff, A., Schiff, D. S., & Nerdel, C. (2025). A multinational assessment of AI literacy among university students in Germany, the UK, and the US. Computers in Human Behavior: Artificial Humans, 4, 100132. https://doi.org/10.1016/j.chbah.2024.100132
28. Hsu, T.-C., Hsu, T.-P., & Lin, Y.-T. (2023). The artificial intelligence learning anxiety and self-efficacy of in-service teachers taking AI training courses. 2023 International Conference on Artificial Intelligence and Education (ICAIE), 97–101. https://doi.org/10.1109/ICAIE59116.2023.00024
29. Jha, N., Shankar, P. R., Al-Betar, M. A., Mukhia, R., Hada, K., & Palaian, S. (2022). Undergraduate medical students’ and interns’ knowledge and perception of artificial intelligence in medicine. Advances in Medical Education and Practice, 13, 927. https://doi.org/10.2147/AMEP.S374489
30. Kaya, F., Aydin, F., Schepman, A., Rodway, P., Yetişensoy, O., & Demir Kaya, M. (2024). The roles of personality traits, AI anxiety, and demographic factors in attitudes toward artificial intelligence. International Journal of Human–Computer Interaction, 40(2), 497–514. https://doi.org/10.1080/10447318.2023.2281340
31. Khan, N., Sarwar, A., Chen, T. B., & Khan, S. (2022). Connecting digital literacy in higher education to the 21st century workforce. Knowledge Management & E-Learning, 14(1), 46–61. https://doi.org/10.34105/j.kmel.2022.14.003
32. Klimova, B., & Pikhart, M. (2025). Exploring the effects of artificial intelligence on student and academic well-being in higher education: A mini-review. Frontiers in Psychology, 16, 1498132.
33. Lacity, M. C., & Willcocks, L. P. (2016). A new approach to automating services. MIT Sloan Management Review, 57(4), 41–49. https://sloanreview.mit.edu/article/a-new-approach-to-automating-services
34. Lin, Y., & Yu, Z. (2025). An integrated bibliometric analysis and systematic review modelling students’ technostress in higher education. Behaviour & Information Technology, 44(4), 631–655. https://doi.org/10.1080/0144929X.2023.2277483
35. Lițan, D.-E. (2025). The impact of technostress generated by artificial intelligence on the quality of life: The mediating role of positive and negative affect. Behavioral Sciences, 15(4), 552. https://doi.org/10.3390/bs15040552
36. 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, 1–16. https://doi.org/10.1145/3313831.3376727
37. Makridakis, S. (2017). The forthcoming artificial intelligence (AI) revolution: Its impact on society and firms. Futures, 90, 46–60. https://doi.org/10.1016/j.futures.2017.03.006
38. Mansoor, H. M. H., Bawazir, A., Alsabri, M. A., Alharbi, A., & Okela, A. H. (2024). Artificial intelligence literacy among university students. A comparative transnational survey. Frontiers in Communication, 9, 1478476. https://doi.org/10.3389/fcomm.2024.1478476
39. Matyushok, V., Vera Krasavina, V., Berezin, A., & Sendra Garcia, J. (2021). The global economy in technological transformation conditions: A review of modern trends. Economic Research-Ekonomska Istraživanja, 34(1), 1471–1497. https://doi.org/10.1080/1331677X.2020.1860792
40. Mena-Guacas, A. F., Meza-Morales, J. A., Fernández, E., & López-Meneses, E. (2023). Digital collaboration in higher education: A study of digital skills and collaborative attitudes in students from diverse universities. Education Sciences, 14(1), 36. https://doi.org/10.3390/educsci14010036
41. Mertala, P., Fagerlund, J., & Calderon, O. (2022). Finnish 5th and 6th grade students’ pre-instructional conceptions of artificial intelligence (AI) and their implications for AI literacy education. Computers and Education: Artificial Intelligence, 3, 100095. https://doi.org/10.1016/j.caeai.2022.100095
42. Mohd Daud, N. (2024). Examining the direct and indirect effects of perceived usefulness and perceived resistance on managerial productivity in the Malaysian ship and maritime industry. Australian Journal of Maritime & Ocean Affairs, 16(2), 162–174. https://doi.org/10.1080/18366503.2024.2327181
43. Ng, D. T. K., Leung, J. K. L., Chu, S. K. W., & Qiao, M. S. (2021). Conceptualizing AI literacy: An exploratory review. Computers and Education: Artificial Intelligence, 2, 100041. https://doi.org/10.1016/j.caeai.2021.100041
44. Ng, D. T. K., Luo, W., Chan, H. M. Y., & Chu, S. K. W. (2022). Using digital story writing as a pedagogy to develop AI literacy among primary students. Computers and Education: Artificial Intelligence, 3, 100054. https://doi.org/10.1016/j.caeai.2022.100054
45. Pertiwi, P. C., Segoh, D., & Rohmadhani, A. (2025). Artificial intelligence in academic environments: Reducing or increasing foreign language anxiety? Inspiring, 100–131.
46. Ravšelj, D., Keržič, D., Tomaževič, N., Umek, L., Brezovar, N., A. Iahad, N., Abdulla, A. A., Akopyan, A., Aldana Segura, M. W., & AlHumaid, J. (2025). Higher education students’ perceptions of ChatGPT: A global study of early reactions. PLoS One, 20(2), e0315011. https://doi.org/10.1371/journal.pone.0315011
47. Rodafinos, A., Barkoukis, V., Tzafilkou, K., Ourda, D., Economides, A. A., & Perifanou, M. (2024). Exploring the impact of digital competence and technology acceptance on academic performance in physical education and sports science students. Journal of Information Technology Education: Research, 23, 19–42. https://doi.org/10.28945/5230
48. Rodrigues, J. M. F., Cardoso, P. J. S., & Chinnici, M. (2023). Artificial intelligence applications and innovations: Day-to-day life impact. Applied Sciences (13(23), 12742. https://doi.org/10.3390/app132312742
49. Saleem, F., Chikhaoui, E., & Malik, M. I. (2024). Technostress in students and quality of online learning: Role of instructor and university support. Frontiers in Education, 9, 1309642. https://doi.org/10.3389/feduc.2024.1309642
50. Sarıkahya, S. D., Özbay, Ö., Torpuş, K., Usta, G., & Özbay, S. Ç. (2025). The impact of ChatGPT on nursing education: A qualitative study based on the experiences of faculty members. Nurse Education Today, 152, 106755. https://doi.org/10.1016/j.nedt.2025.106755
51. Schepman, A., & Rodway, P. (2020). Initial validation of the general attitudes towards Artificial Intelligence Scale. Computers in Human Behavior Reports, 1, 100014. https://doi.org/10.1016/j.chbr.2020.100014
52. Schepman, A., & Rodway, P. (2023). The General Attitudes towards Artificial Intelligence Scale (GAAIS): Confirmatory validation and associations with personality, corporate distrust, and general trust. International Journal of Human–Computer Interaction, 39(13), 2724–2741. https://doi.org/10.1080/10447318.2022.2118092
53. Semerci Şahin, R., Özbay, Ö., Çınar Özbay, S., & Durmuş Sarıkahya, S. (2025). Development of the generative artificial intelligence awareness scale for secondary school students in Türkiye. European Journal of Pediatrics, 184, 585. https://doi.org/10.1007/s00431-025-06435-8
54. Sergeeva, O. V, Masalimova, A. R., Zheltukhina, M. R., Chikileva, L. S., Lutskovskai, L. Y., & Luzin, A. (2025). Impact of digital media literacy on attitude toward generative AI acceptance in higher education. Frontiers in Education, 10, 1563148. https://doi.org/10.3389/feduc.2025.1563148
55. Shima, B., & Jaupaj, A. (2025). The Impact of Digital Competencies on Students' Attitudes towards the Use of Information and Communication Technology. WSEAS Transactions on Environment and Development, 21, 714-728. https://doi.org/10.37394/232015.2025.21.59
56. Sindermann, C., Montag, C., & Elhai, J. D. (2022). The design of social media platforms: Initial evidence on relations between personality, fear of missing out, design element-driven increased social media use, and problematic social media use. Computers in Human Behavior, 128, 107126. https://doi.org/10.1016/j.chb.2021.107126
57. Southworth, J., Migliaccio, K., Glover, J., Glover, J., Reed, D., McCarty, C., Brendemuhl, J., & Thomas, A. (2023). Developing a model for AI Across the curriculum: Transforming the higher education landscape via innovation in AI literacy. Computers and Education: Artificial Intelligence, 4, 100127. https://doi.org/10.1016/j.caeai.2023.100127
58. Stahl, B. C., & Wright, D. (2018). Ethics and privacy in AI and big data: Implementing responsible research and innovation. IEEE Security & Privacy, 16(3), 26–33. https://doi.org/10.1109/MSP.2018.2701164
59. Steinbauer, G., Kandlhofer, M., Chklovski, T., Heintz, F., & Koenig, S. (2021). A differentiated discussion about AI education K-12. KI-Künstliche Intelligenz, 35(2), 131–137. https://doi.org/10.1007/s13218-021-00728-6
60. Torales, J., Torres-Romero, A. D., Di Giuseppe, M. F., Rolón-Méndez, E. R., Martínez-López, P. L., Heinichen-Mansfeld, K. V., Barrios, I., O’Higgins, M., Almirón-Santacruz, J., & Melgarejo, O. (2022). Technostress, anxiety, and depression among university students: A report from Paraguay. International Journal of Social Psychiatry, 68(5), 1063–1070. https://doi.org/10.1177/00207640211062374
61. Ulfert-Blank, A.-S., & Schmidt, I. (2022). Assessing digital self-efficacy: Review and scale development. Computers & Education, 191, 104626. https://doi.org/10.1016/j.compedu.2022.104626
62. Upadhyaya, P., & Vrinda. (2021). Impact of technostress on academic productivity of university students. Education and Information Technologies, 26(2), 1647–1664. https://doi.org/10.1007/s10639-020-10319-9
63. Vallone, F., Galvin, J., Cattaneo Della Volta, M. F., Akhtar, A., Chua, S., Ghio, E., Giovazolias, T., Kazakou, Z., Kritikou, M., & Koutra, K. (2023). Technostress and academic motivation: Direct and indirect effects on university students’ psychological health. Frontiers in Psychology, 14, 1211134. https://doi.org/10.3389/fpsyg.2023.1211134
64. Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 425–478. https://doi.org/10.2307/30036540
65. Wang, B., Rau, P.-L. P., & Yuan, T. (2023). Measuring user competence in using artificial intelligence: Validity and reliability of artificial intelligence literacy scale. Behaviour & Information Technology, 42(9), 1324–1337. https://doi.org/10.1080/0144929X.2022.2095894
66. Wang, J. (2024). The effect of Chinese EFL students’ digital literacy on their technostress and academic productivity. The Asia-Pacific Education Researcher, 33(4), 987–996. https://doi.org/10.1007/s40299-023-00757-5
67. Wang, K., Shu, Q., & Tu, Q. (2008). Technostress under different organizational environments: An empirical investigation. Computers in Human Behavior, 24(6), 3002–3013. https://doi.org/10.1016/j.chb.2008.05.007
68. 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. https://doi.org/10.1080/10494820.2019.1674887
69. Yang, Y., Zhang, Y., Sun, D., He, W., & Wei, Y. (2025). Navigating the landscape of AI literacy education: Insights from a decade of research (2014–2024). Humanities and Social Sciences Communications, 12(1), 1–12. https://doi.org/10.1057/s41599-024-03245-6
70. Zeng, Q., Huang, X., Zhu, J., Su, S., Hu, Y., & Zhang, X. (2025). Mechanisms of nurses' AI use intention formation in Sichuan, Yunnan, and Beijing, China: Mediating effects of AI literacy via self-efficacy-to-attitude pathways. Frontiers in Public Health, 13, 1622802. https://doi.org/10.3389/fpubh.2025.1622802
71. Zhai, X., Chu, X., Chai, C. S., Jong, M. S. Y., Istenic, A., Spector, M., Liu, J.-B., Yuan, J., & Li, Y. (2021). A review of artificial intelligence (AI) in education from 2010 to 2020. Complexity, 2021(1), 8812542. https://doi.org/10.1155/2021/8812542
72. Zhan, E. S., Molina, M. D., Rheu, M., & Peng, W. (2024). What is there to fear? Understanding multi-dimensional fear of AI from a technological affordance perspective. International Journal of Human–Computer Interaction, 40(22), 7127–7144. https://doi.org/10.1080/10447318.2024.2350942
73. Zhu, W., Huang, L., Zhou, X., Li, X., Shi, G., Ying, J., & Wang, C. (2025). Could AI ethical anxiety, perceived ethical risks and ethical awareness about AI influence university students’ use of generative AI products? An ethical perspective. International Journal of Human–Computer Interaction, 41(1), 742–764. https://doi.org/10.1080/10447318.2024.2311562