Attitude, Anxiety, and Literacy among Teacher Aspirants’ Embrace of Artificial Intelligence: Implications for Practical and Ethical Challenges in Integrating AI in Education
DOI:
https://doi.org/10.56294/ai2025416Keywords:
Artificial intelligence, Teacher aspirants, Attitude, anxiety, literacyAbstract
Attitude, anxiety, and literacy are essential factors in determining the acceptance of artificial intelligence (AI), particularly in its integration into education. Despite their relevance, few studies have explored their mutual influence and their impact on teacher aspirants’ perspectives toward AI. This study aimed to analyze these constructs and their interrelationships through a descriptive-quantitative-correlational design. Stratified random sampling was employed to select 200 respondents from the education programs of a state university. The results indicated that teacher aspirants hold a positive attitude toward AI (M=4,19), exhibit low anxiety (M=2,44), and demonstrate very high literacy (M=6,22). Significant differences were observed in levels of anxiety, literacy, and attitudes across course programs. Furthermore, a significant interrelationship among the three constructs was established. The findings highlight the pivotal role of attitudes, anxiety, and literacy in shaping teacher aspirants’ acceptance of AI and emphasize their importance in guiding future educational integration.
References
1. Andrade Preciado JS, González Vallejo R. Integrating ChatGPT and generative AI apps in specialized text translation and post-editing: an exploratory study. Semin Med Writ Educ. 2024;3:624. https://doi.org/10.56294/mw2024624
2. Bantoto FMO, Rillo R, Abequibel B, Mangila BB, Alieto EO. Is AI an effective “learning tool” in academic writing? Investigating the perceptions of third-year university students on the use of artificial intelligence in classroom instruction. In: International Conference on Digital Technologies and Applications. Cham: Springer Nature Switzerland; 2024. p. 72–81. https://doi.org/10.1007/978-3-031-68650-4_8
3. Chiu TKF, Xia Q, Zhou X, Chai CS, Cheng M. Systematic literature review on opportunities, challenges, and future research recommendations of artificial intelligence in education. Comput Educ Artif Intell. 2023; 4:100118. https://doi.org/10.1016/j.caeai.2022.100118
4. Clorion FDD, Alieto E, Fuentes J, Suicano DJ, Natividad ER, Miñoza M, et al. Artificial intelligence in academic writing in higher education in a country of emerging economy: An analysis of knowledge, perceived influence, extent of use, and perception. In: Lahby M, Maleh Y, Bucchiarone A, Schaeffer SE, editors. General Aspects of Applying Generative AI in Higher Education. Cham: Springer; 2024. p. 301–326.
5. Francisco CI, Pantaleon CE, Lantaya GMA, Francisco WAR, Alieto EO. Understanding the attitude of senior high school students toward utilizing ChatGPT as a learning tool: A quantitative analysis. In: Sustainable Data Management: Navigating Big Data, Communication Technology, and Business Digital Leadership. Vol. 1. Cham: Springer Nature Switzerland; 2025. p. 37-49. https://doi.org/10.1007/978-3-031-83911-5_4
6. Funa A, Gabay RA. Policy guidelines and recommendations on AI use in teaching and learning: A meta synthesis study. Soc Sci Humanit Open. 2025; 11:101221. https://doi.org/10.1016/j.ssaho.2024.101221
7. Gregorio TAD, Alieto EO, Natividad ERR, Tanpoco MR. Are preservice teachers “totally PACKaged”? A quantitative study of pre-service teachers’ knowledge and skills to ethically integrate Artificial Intelligence (AI)-based tools into Education. In: International Conference on Digital Technologies and Applications. Cham: Springer Nature Switzerland; 2024. p. 45–55. doi:10.1007/978-3-031-68660-3_5
8. Iwasawa M, Kobayashi M, Otori K. Knowledge and attitudes of pharmacy students towards artificial intelligence and the ChatGPT. Pharm Educ. 2023;23(1):665–75. https://doi.org/10.46542/pe.2023.231.665675
9. Li S, Liu B. Joseph E. Aoun: Robot-proof: Higher education in the age of artificial intelligence. High Educ. 2019; 77:757–9. https://doi.org/10.1007/s10734-018-0289-3
10. Zhang, K., & Aslan, A. B. (2021). AI technologies for education: Recent research & future directions. Computers and Education: Artificial Intelligence, 2, 100025. https://doi.org/10.1016/j.caeai.2021.100025
11. Ahmad, S. F., Rahmat, M. K., Mubarik, M. S., Alam, M. M., & Hyder, S. I. (2021). Artificial intelligence and its role in education. Sustainability, 13(22), 12902. https://doi.org/10.3390/su132212902
12. Brauner P, Hick A, Philipsen R, Ziefle M. What does the public think about artificial intelligence? A criticality map to understand bias in the public perception of AI. Front Comput Sci. 2023; 5:1113903. https://doi.org/10.3389/fcomp.2023.1113903
13. Tai, M. C.T. (2020). The impact of artificial intelligence on human society and bioethics. Tzu Chi Medical Journal, 32(4), 339-343. https://doi.org/10.4103/tcmj.tcmj_71_20
14. Alieto E, Abequibel-Encarnacion B, Estigoy E, Balasa K, Eijansantos A, Torres-Toukoumidis A. Teaching inside a digital classroom: A quantitative analysis of attitude, technological competence and access among teachers across subject disciplines. Heliyon. 2024;10(2): e24282. doi: 10.1016/j.heliyon. 2024.e24282
15. Berganio ME, Tanpoco M, Dumagay AH. Preservice teachers’ perceived level of digital literacy: A quantitative study from a developing country. In: Motahhir S, Bossoufi B, editors. ICDTA 2024. Lecture Notes in Networks and Systems, vol. 1101. Cham: Springer; 2024. p. 158–67.
16. Clorion FDD, Fuentes J, Suicano DJ, Estigoy E, Eijansantos A, Rillo R, et al. AI‑Powered Professionals and Digital Natives: A Correlational Analysis of the Use and Benefits of Artificial Intelligence for the Employability Skills of Postgraduate Education Students. Procedia Comput Sci. 2025; 263:107–14. https://doi.org/10.1016/j.procs.2025.07.014
17. Clorion FD, Fuentes JO, Suicano DJ, Estigoy E, Serdenia JR, Alejandrino P, et al. Smartphones and syntax: A quantitative study on harnessing the role of mobile-assisted language learning in the digital classroom and applications for language learning. Procedia Comput Sci. 2025; 257:7–14.
18. Fernandez MA, Cabangcala C, Fanilag E, Cabangcala R, Balasa K, Alieto EO. Technology in education: An attitudinal investigation among prospective teachers from a country of emerging economy. In: Farhaoui Y, Hussain A, Saba T, Taherdoost H, Verma A, editors. Artificial Intelligence, Data Science and Applications. ICAISE 2023. Lecture Notes in Networks and Systems, vol. 837. Cham: Springer; 2024. p. 248–55.
19. Flores B, Amabao K, Aidil-Karanain F, Dumagay AH. Bachelor of Culture and Arts student’s attitude toward using digital games for learning. Sci Int (Lahore). 2023;35(3):357–61.
20. Gonzales LI, Yusoo RJ, Miñoza M, Casimiro A, Devanadera A, Dumagay AH. Reading in the 21st century: Digital reading habit of prospective elementary language teachers. In: Farhaoui Y, Hussain A, Saba T, Taherdoost H, Verma A, editors. Artificial Intelligence, Data Science and Applications. ICAISE 2023. Lecture Notes in Networks and Systems, vol. 837. Cham: Springer; 2024. p. 134–41.
21. Lozada P, Sarona J, Marumas DG, Hasan NN, Aidil-Karanain F, Alieto EO. Correlation among learners’ economic ability, attitude toward ICT, and reading performance: An exploration among twenty-first century teacher aspirants. In: Fortino G, Kumar A, Swaroop A, Shukla P, editors. Proceedings of Third International Conference on Computing and Communication Networks. ICCCN 2023. Lecture Notes in Networks and Systems. Vol. 977. Singapore: Springer; 2023. p. 41-52. https://doi.org/10.1007/978-981-97-2671-4_4
22. Bajaj R, Sharma V. Smart education with artificial intelligence-based determination of learning styles. Procedia Comput Sci. 2018; 132:834–42. https://doi.org/10.1016/j.procs.2018.05.095
23. Celik I, Dindar M, Muukkonen H, Järvelä S. The promises and challenges of artificial intelligence for educators: A systematic review of research. TechTrends. 2022;66(4):616–30. https://doi.org/10.1007/s11528-022-00715-y
24. Domingo AR, Clorion FDD, Mangila B, Hasan NN, Tarroza R, Flores B, Rillo R, Pantaleon C, Francisco CI, Delos Santos M, Alieto EO. Quill & Bytes: A qualitative analysis on the perceived impacts of AI-based paraphrasing tools in academic writing and performance toward higher education students. Procedia Comput Sci. 2025; 263:664–71. https://doi.org/10.1016/j.procs.2025.07.079
25. Galindo-Domínguez H, Delgado H, Campo L, Losada D. Relationship between teachers’ digital competence and attitudes towards artificial intelligence in education. Int J Educ Res. 2024; 126:102381. https://doi.org/10.1016/j.ijer.2024.102381
26. Gapol PAM, Alieto EO, Capacio EA, Dumagay AH, Francisco CI, Vallejo RG. Preservice teachers’ extent of knowledge and willingness to adopt generative AI in higher education. In: González Vallejo R, Moukhliss G, Schaeffer E, Paliktzoglou V, editors. The Second International Symposium on Generative AI and Education (ISGAIE’2025). Lecture Notes on Data Engineering and Communications Technologies. Vol. 262. Cham: Springer; 2025. https://doi.org/10.1007/978-3-031-98476-1_6
27. Hopcan S, Türkmen G, Polat E. Exploring the artificial intelligence anxiety and machine learning attitudes of teacher candidates. Educ Inf Technol. 2024;29:7281–7301. https://doi.org/10.1007/s10639-023-12086-9
28. Torres-Toukoumidis A, Jiménez MMF, Merchán-Romero J, Vega-Ramírez JFA. Gamification and artificial intelligence in the educational context: analysis of scientific literature. In: Schönbohm A, et al., editors. Games and Learning Alliance. GALA 2024. Lecture Notes in Computer Science. Cham: Springer; 2025. https://doi.org/10.1007/978-3-031-78269-5_34
29. Zawacki-Richter, O., Marin, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education – where are the educators? International Journal of Educational Technology in Higher Education, 16(39), 1-27. https://doi.org/10.1186/s41239
30. Dong L, Tang X, Wang X. Examining the effect of artificial intelligence in relation to students’ academic achievement in classroom: a meta-analysis. Comput Educ Artif Intell. 2025;100400. doi:10.1016/j.caeai.2025.100400
31. Knox J. Artificial intelligence and education in China. Learning Media Technol. 2020;45(3):298–311. https://doi.org/10.1080/17439884.2020.1754236
32. Kwak Y, Ahn J, Seo YH. Influence of AI ethics awareness, attitude, anxiety, and self‑efficacy on nursing students’ behavioral intentions. BMC Nurs. 2022;21(1):267. https://doi.org/10.1186/s12912-022-01048-0
33. Ayanwale MA, Sanusi IT, Adelana OP, Aruleba KD, Oyelere SS. Teachers’ readiness and intention to teach artificial intelligence in schools. Comput Educ Artif Intell. 2022;3:100099. https://doi.org/10.1016/j.caeai.2022.100099
34. Balasa KA, Dumagay AH, Alieto EO, Vallejo RG. Gender and age dynamics in future educators’ attitudes toward AI integration in education: a sample from state managed universities in Zamboanga Peninsula, Philippines. Semin Med Writ Educ. 2025;4:668. https://doi.org/10.56294/mw2025668
35. Clorion FDD, Fuentes J, Suicano DJ, Estigoy E, Eijansantos A, Rillo R, Pantaleon C, Francisco CI, Delos Santos M, Alieto EO. AI-Powered professionals and digital natives: a correlational analysis of the use and benefits of artificial intelligence for the employability skills of postgraduate education students. Procedia Comput Sci. 2025;263:107–14. https://doi.org/10.1016/j.procs.2025.07.014
36. Dakakmi D, Safa N. Artificial intelligence in the L2 classroom: Implications and challenges on ethics and equity in higher education: A 21st century Pandora’s box. Comput Educ Artif Intell. 2023;5:100179. https://doi.org/10.1016/j.caeai.2023.100179
37. Dumagay, A. H., Balasa, K. A., Kunting, A. F., & Cabangcala, R. B. (2025). AI acceptance among prospective social studies and culture and arts education students. In K. Arai (Ed.), Intelligent computing. CompCom 2025 (Lecture Notes in Networks and Systems, Vol. 1426). Springer. https://doi.org/10.1007/978-3-031-92611-2_11
38. Elom CO, Ayanwale MA, Ukeje IO, Offiah GA, Umoke CC, Ogbonnaya CE. Does AI knowledge encourage cheating? Investigating student perceptions, ethical engagement, and academic integrity in the digital age. Int J Learn Teach Educ Res. 2025;24(4). https://doi.org/10.26803/ijlter.24.4.33
39. Sanusi IT, Ayanwale MA, Tolorunkele AE. Investigating pre-service teachers’ artificial intelligence perception from the perspective of planned behavior theory. Comput Educ Artif Intell. 2024; 6:100202.
40. Sanusi IT, Ayanwale MA, Chiu T. Investigating the moderating effects of social good and confidence on teachers’ intention to prepare school students for artificial intelligence education. Educ Inf Technol. 2024; 29:29–295. https://doi.org/10.1007/s10639-023-12250-1
41. Chai CS, Lin PY, Jong MSY, Dai Y, Chiu TKF, Huang B. Factors influencing students' behavioral intention to continue artificial intelligence learning. In: Proceedings of the 2020 International Symposium on Educational Technology (ISET); 2020. p. 147–50.
42. Kaya F, Aydin F, Schepman A, Rodway P, Yetişensoy O, Kaya MD. The roles of personality traits, AI anxiety, and demographic factors in attitudes toward artificial intelligence. Int J Hum Comput Interact. 2022;40(2):497–514. doi:10.1080/10447318.2022.2151730
43. Cengiz S, Peker A. Generative artificial intelligence acceptance and artificial intelligence anxiety among university students: the sequential mediating role of attitudes toward artificial intelligence and literacy. Curr Psychol. 2025. https://doi.org/10.1007/s12144-025-07433-7
44. Maghanoy J, Tahil M, Sulasula J, Vallejo RG, Dumagay AH, Alieto EO. Gender and educational attainment dynamics on artificial intelligence anxiety among educators with emerging understanding. In: González Vallejo R, Moukhliss G, Schaeffer E, Paliktzoglou V, editors. The Second International Symposium on Generative AI and Education (ISGAIE’2025). Lecture Notes on Data Engineering and Communications Technologies. Vol. 262. Springer; 2025. https://doi.org/10.1007/978-3-031-98476-1_40
45. Serdenia JR, Dumagay AH, Balasa KA, Capacio EA, Lauzon LDS. Attitude, acceptability, and perceived effectiveness of artificial intelligence in education: A quantitative cross-sectional study among future teachers. LatIA. 2025;3:313
46. Wang YY, Wang YS. Development and validation of an artificial intelligence anxiety scale: An initial application in predicting motivated learning behavior. Interact Learn Environ. 2022;30(4):619–34. https://doi.org/10.1080/10494820.2019.1674887
47. Khasawneh OY. Technophobia: Examining its hidden factors and defining it. Technol Soc. 2018;54:93–100. http://doi.org/10.1016/j.techsoc.2018.03.008
48. Ni A, Cheung A. Understanding secondary students’ continuance intention to adopt AI-powered intelligent tutoring system for English learning. Educ Inf Technol. 2023;28(3):3191–216. http://doi.org/10.1007/s10639-022-11305-z
49. Almaiah MA, Alfaisal R, Salloum SA, Hajjej F, Thabit S, El-Qirem FA, et al. Examining the impact of artificial intelligence and social and computer anxiety in E-learning settings: Students’ perceptions at the university level. Electronics. 2022;11(22):3662. http://doi.org/10.3390/electronics11223662
50. Schepman A, Rodway P. Initial validation of the general attitudes toward artificial intelligence scale. Comput Human Behav Rep. 2020; 1:100014. https://doi.org/10.1016/j.chbr.2020.100014
51. Zhou C. Integration of modern technologies in higher education on the example of artificial intelligence use. Educ Inf Technol. 2023;28(4):3893–3910. https://doi.org/10.1007/s10639-022-11309-9
52. Cao G, Duan Y, Edwards JS, Dwivedi YK. Understanding managers’ attitudes and behavioral intentions toward using artificial intelligence for organizational decision-making. Technovation. 2021;106:102312. https://doi.org/10.1016/j.technovation.2021.102312
53. Edison SW, Geissler GL. Measuring attitudes toward general technology: Antecedents, hypotheses and scale development. J Target Meas Anal Mark. 2003;12(2):137–156. https://doi.org/10.1057/palgrave.jt.5740104
54. Walter Y. Embracing the future of Artificial Intelligence in the classroom: the relevance of AI literacy, prompt engineering, and critical thinking in modern education. Int J Educ Technol High Educ. 2024;21(1). https://doi.org/10.1186/s41239-024-00448-3
55. Kim J. Leading teachers’ perspective on teacher-AI collaboration in education. Educ Inf Technol. 2023;29(7):8693–8724. https://doi.org/10.1007/s10639-023-12109-5
56. Kong S, Cheung MW, Tsang O. Developing an artificial intelligence literacy framework: Evaluation of a literacy course for senior secondary students using a project-based learning approach. Comput Educ Artif Intell. 2024;6:100214. https://doi.org/10.1016/j.caeai.2024.100214
57. Ajzen I, Schmidt P. Changing behavior using the theory of planned behavior. Cambridge University Press eBooks. 2020;17–31. https://doi.org/10.1017/9781108677318.002
58. Ajzen I. New directions in attitude measurement. Berlin: Walter de Gruyter; 1993.
59. Alieto E. Language shift from English to Mother Tongue: Exploring language attitude and willingness to teach among preservice teachers. TESOL Int J. 2018;13(3):134–46.
60. Alieto EO, Rillo R. Language attitudes of English language teachers (ELTs) towards Philippine English. Dimension J Humanit Soc Sci. 2018;13(1):84–110.
61. Alves H, Yzerbyt V, Unkelbach C. Attitude formation in more- and less-complex social environments. Pers Soc Psychol Bull. 2024;0(0):1–15. https://doi.org/10.1177/01461672241235387
62. Cabangcala R, Alieto EO, Estigoy E, Delos Santos M, Torres J. When language learning suddenly becomes online: Analyzing English as second language learners' (ELLs) attitude and technological competence. TESOL Int J. 2021;16(4.3):115–131.
63. Jacinto MJ, Alieto E. Virtual teaching attitude and technological competence among English as second language (ESL) teachers. Asian EFL. 2020;27(4.4):403–32.
64. Mumbing L, Abequibel B, Buslon J, Alieto EO. Digital education, the new frontier: Determining attitude and technological competence of language teachers from a developing country. Asian ESP J. 2021;17(4.3):300–328.
65. Ricohermoso C, Abequibel B, Alieto E. Attitude toward English and Filipino as correlates of cognition toward mother tongue: An analysis among would-be language teachers. Asian EFL J. 2019;26(6.1):5–22.
66. Somblingo R, Alieto EO. English language attitude among Filipino prospective language teachers: An analysis through the mentalist theoretical lens. Asian ESP J. 2019;15(2):23–41.
67. Vargas-Sánchez A, Plaza-Mejía MÁ, Porras-Bueno N. Attitude. Springer eBooks. 2016;58–62. https://doi.org/10.1007/978-3-319-01384-8_11
68. Santos ZMB, Cadano KJ, Gyawali YP, Alieto EO, Clorion FD. Navigating between conditions and convictions: Investigating the influence of sociogeographical factors on interest and attitudes toward artificial intelligence among secondary school teachers. Lect Notes Netw Syst. 2024;168–77. https://doi.org/10.1007/978-3-031-68675-7_17
69. Fuentes JO, Clorion FD, Abequibel B, Valerio S, Alieto EO. Understanding the attitude of teacher education students toward utilizing ChatGPT as a learning tool: A quantitative analysis. In: Motahhir S, Bossoufi B, editors. Digital technologies and applications. ICDTA 2024. Lect Notes Netw Syst. 2024;1098:82–93. https://doi.org/10.1007/978-3-031-68650-4_9
70. Park, J., & Woo, S. E. (2022). Who Likes Artificial Intelligence? Personality Predictors of Attitudes toward Artificial Intelligence. The Journal of psychology, 156(1), 68–94. https://doi.org/10.1080/00223980.2021.2012109
71. Park J, Woo SE. Who likes artificial intelligence? Personality predictors of attitudes toward artificial intelligence. J Psychol. 2022;156(1):68–94. https://doi.org/10.1080/00223980.2021.2012109
72. Schepman A, Rodway P. The general attitudes toward artificial intelligence scale (GAAIS): Confirmatory validation and associations with personality, corporate distrust, and general trust. Int J Hum Comput Interact. 2022;39(13):2724–41. https://doi.org/10.1080/10447318.2022.2085400
73. Zhang B, Dafoe A. Artificial intelligence: American attitudes and trends. SSRN Electron J. 2019;0(0):5. https://doi.org/10.2139/ssrn.3312874
74. European Commission: Directorate-General for Communications Networks, Content and Technology. Attitudes toward the impact of digitization and automation on daily life: report. Eur Comm. 2017; 1:1–112. https://doi.org/10.2759/835661
75. Craske MG, Rauch SL, Ursano R, Prenoveau J, Pine DS, Zinbarg RE. What is an anxiety disorder? Focus (Am Psychiatr Publ). 2011;9(3):369–88. https://doi.org/10.1176/foc.9.3.foc369
76. Li J, Huang J-S. Dimensions of artificial intelligence anxiety based on integrated fear acquisition theory. Technol Soc. 2020;63:101410. https://doi.org/10.1016/j.techsoc.2020.101410
77. Wang Y, Wei C, Lin H, Wang S, Wang Y. What drives students’ AI learning behavior: A perspective of AI anxiety. Interact Learn Environ. 2022;1–17. https://doi.org/10.1080/10494820.2022.2153147
78. Parasuraman S, Igbaria M. An examination of gender differences in the determinants of computer anxiety and attitudes toward microcomputers among managers. Int J Man Mach Stud. 1990;32(3):327–40. https://doi.org/10.1016/S0020-7373(08)80006-5
79. Rosen LD, Weil MM. Computers, classroom instruction and the computer-phobic university student. Coll Microcomput. 1990;8(4):257–83.
80. Heinssen RK Jr, Glass CR, Knight LA. Assessing computer anxiety: Development and validation of the computer anxiety rating scale. Comput Human Behav. 1987;3(1):49–59. https://doi.org/10.1016/0747-5632(87)90010-0
81. Epstein S. The nature of anxiety with emphasis upon its relationship to expectancy. In: Anxiety. 1972. p. 291–342. https://doi.org/10.1016/b978-0-12-657402-9.50007-7
82. Johnson DG, Verdicchio M. AI anxiety. J Assoc Inf Sci Technol. 2017;68(9):2267–70. https://doi.org/10.1002/asi.23867
83. Jeon J. Exploring AI chatbot affordances in the EFL classroom: Young learners’ experiences and perspectives. Comput Assist Lang Learn. 2022;37(3):1–26. http://doi.org/10.1080/09588221.2021.2021241
84. Hsu T. Learning English with augmented reality: Do learning styles matter? Comput Educ. 2017; 106:137–49. https://doi.org/10.1016/j.compedu.2016.12.007
85. Beer JM, Fisk AD, Rogers WA. Toward a framework for levels of robot autonomy in human-robot interaction. J Human-Robot Interact. 2014;3(2):74–99. https://doi.org/10.5898/JHRI.3.2.Beer
86. Haladjian HH, Montemayor C. Artificial consciousness and consciousness-attention dissociation. Conscious Cogn. 2016;45:210–25. https://doi.org/10.1016/j.concog.2016.08.011
87. Etzioni A, Etzioni O. Incorporating ethics into artificial intelligence. J Ethics. 2017;21(4):403–18. https://doi.org/10.1007/s10892-017-9252-2
88. Russell S, Hauert S, Altman RB, Veloso M. Robotics: Ethics of artificial intelligence. Nature. 2015;521(7553):415–18. https://doi.org/10.1038/521415a
89. Stahl BC, Wright D. Ethics and privacy in AI and big data: Implementing responsible research and innovation. IEEE Secur Priv. 2018;16(3):26–33. https://doi.org/10.1109/MSP.2018.2701164
90. Kim S, Chen RP, Zhang K. Anthropomorphized helpers undermine autonomy and enjoyment in computer games. J Consum Res. 2016;43(2):282–302. https://doi.org/10.1093/jcr/ucw016
91. Cohen T, Jones P. Technological advances relevant to transport – understanding what drives them. Transp Res A Policy Pract. 2020;135:80–95. https://doi.org/10.1016/j.tra.2020.03.002
92. Černý M. University students’ conceptualization of AI literacy: Theory and empirical evidence. Soc Sci. 2024;13(3):129. https://doi.org/10.3390/socsci13030129
93. Hur JW. Fostering AI literacy: Overcoming concerns and nurturing confidence among preservice teachers. Inf Learn Sci. 2025;126(1/2):56–74. doi:10.1108/ILS-11-2023-0170
94. Ayanwale MA, Owolabi PA, Molefi RR, Adeeko O, Ishola AM. Examining artificial intelligence literacy among pre-service teachers for future classrooms. Comput Educ Open. 2024; 6:100179. https://doi.org/10.1016/j.caeo.2024.100179
95. Bewersdorff A, Hornberger M, Nerdel C, Schiff DS. AI advocates and cautious critics: How AI attitudes, AI interest, use of AI, and AI literacy build university students’ AI self-efficacy. Comput Educ Artif Intell. 2025;8:100340. https://doi.org/10.1016/j.caeai.2024.100340
96. Ng DTK, Leung JKL, Chu SKW, Qiao MS. Conceptualizing AI literacy: An exploratory review. Comput Educ Artif Intell. 2021;2:100041. https://doi.org/10.1016/j.caeai.2021.100041
97. Du H, Sun Y, Jiang H, Islam AYMA, Gu X. Exploring the effects of AI literacy in teacher learning: An empirical study. Humanit Soc Sci Commun. 2024;11(1). https://doi.org/10.1057/s41599-024-03101-6
98. Wu R, Yu Z. Do AI chatbots improve students’ learning outcomes? Evidence from a meta‐analysis. Br J Educ Technol. 2023;55(1):10–33. https://doi.org/10.1111/bjet.13334
99. Long D, Magerko B. What is AI literacy? Competencies and design considerations. Proc 2020 CHI Conf Hum Factors Comput Syst. 2020;1–16. https://doi.org/10.1145/3313831.3376727
100. Martin A, Grudziecki J. DIGEULIT: Concepts and tools for digital literacy development. Innov Teach Learn Inf Comput Sci. 2006;5(4):249–67. https://doi.org/10.11120/ital.2006.05040249
101. Zhang H, Lee I, Ali S, DiPaola D, Cheng Y, Breazeal C. Integrating ethics and career futures with technical learning to promote AI literacy for middle school students: An exploratory study. Int J Artif Intell Educ. 2023;33(2):290–324. https://doi.org/10.1007/s40593-022-00293-3
102. Casal-Otero L, Catala A, Fernández-Morante C, Taboada M, Cebreiro B, Barro S. AI literacy in K-12: A systematic literature review. Int J STEM Educ. 2023;10(1):29. https://doi.org/10.1186/s40594-023-00418-7
103. Chiu TKF. The impact of Generative AI (GenAI) on practices, policies, and research direction in education: A case of ChatGPT and Midjourney. Interact Learn Environ. 2023;1–17. https://doi.org/10.1080/10494820.2023.2253861
104. Kothari CR. Research methodology: Methods and techniques. 2nd ed. New Delhi: New Age International Publishers; 2004.
105. Privitera GJ. Research methods for the behavioral sciences. Thousand Oaks, CA: SAGE Publications, Inc; 2013.
106. Ganeshpurkar A, Pandey V, Asati S, Maheshwari R, Tekade M, Tekade RK. Experimental design and analysis of variance. In: Tekade RK, editor. Elsevier eBooks. 2018. p. 281–301. https://doi.org/10.1016/b978-0-12-814421-3.00008-7
107. Wang X, Cheng Z. Cross-sectional studies. Chest. 2020;158(1):S65–71. https://doi.org/10.1016/j.chest.2020.03.012
108. Glasgow G. Stratified sampling types. In: Elsevier eBooks. 2005. p. 683–8. https://doi.org/10.1016/b0-12-369398-5/00066-9
109. Abequibel B, Ricohermoso C, Alieto E, Barredo C, Lucas RI. Prospective reading teachers’ digital reading habit: A cross-sectional design. TESOL Int J. 2021;16(4.4):246–60.
110. Alieto EO. Cognition as predictor of willingness to teach in the mother tongue and the mother tongue as a subject among prospective language teachers. Online Submiss. 2019; 31:135–9.
111. Alieto E, Devanadera A, Buslon J. Women of K-12: Exploring teachers' cognition in language policy implementation. Asian EFL J. 2019;24(4.1):143–62.
112. Bacang B, Rillo R, Alieto O. The gender construct in the use of rhetorical appeals, hedges and boosters in ESL writing: A discourse analysis. Asian EFL J. 2019;25(52):210–24.
113. Casiano PKM, Encarnacion BA, Jaafar SH, Alieto EO. Digital-game-based language learning: An exploration of attitudes among teacher aspirants in a non-metropolitan area. In: Fortino G, Kumar A, Swaroop A, Shukla P, editors. Proceedings of Third International Conference on Computing and Communication Networks. ICCCN 2023. Lecture Notes in Networks and Systems, vol 917. Singapore: Springer.
114. Devanadera A, Alieto O. Lexical bias among Tagalog-speaking Filipino preschool children. Asian EFL J. 2019;24(4):207–28.
115. Gapol PA, Bantoto FM, Fuentes J, Pil AO, Sarona J, Lacao-Lacao L, et al. Is sustainability a ‘lesson plan’ for preservice teachers? Extent of environmental awareness in the framework of waste management among preservice teachers. Procedia Comput Sci. 2024;236:527-532.
116. Lee A, Alieto E. Analyzing teaching self-efficacy correlates in virtual education: A gender-driven structural equation modeling approach. Malays J ELT Res. 2023;20(2):110–28.
117. Pahulaya V, Reyes A, Buslon J, Alieto EO. Gender divide in attitude towards Chavacano and cognition towards mother tongue among prospective language teachers. Asian EFL. 2020;27(3.1):41–64.
118. Wang B, Rau PPL, Yuan T. Measuring user competence in using artificial intelligence: Validity and reliability of artificial intelligence literacy scale. Behav Inf Technol. 2022;42(9):1324–37. https://doi.org/10.1080/0144929x.2022.2072768
119. Taber KS. The use of Cronbach’s Alpha when developing and reporting research instruments in science education. Res Sci Educ. 2018;48:1273–96. https://doi.org/10.1007/s11165-016-9602-2
120. Hair JF, Black WC, Babin BJ, Anderson RE. Multivariate data analysis: A global perspective. Upper Saddle River, NJ: Pearson Education International; 2010.
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