Preservice Teachers and AI in Education 5.0: Examining Literacy, Anxiety, and Attitudes across Gender, Socioeconomic Status, and Training
DOI:
https://doi.org/10.56294/ai2025432Keywords:
Artificial Intelligence, Preservice Teachers, AI Attitudes, AI Anxiety, AI LiteracyAbstract
Education 5,0 underscores the central role of artificial intelligence (AI) in reshaping teaching and learning, yet the readiness of preservice teachers to engage with these technologies remains at an early stage. This study set out to examine the levels of AI literacy, anxiety, and attitudes among preservice teachers in state universities and colleges in the Zamboanga Peninsula, taking into account gender, socioeconomic status, and training as key demographic variables. Using a descriptive-quantitative, correlational-comparative design, data were gathered from 378 respondents and analyzed through descriptive statistics, independent samples t-tests, one-way ANOVA, and Pearson correlation. Results revealed that preservice teachers demonstrated moderately high literacy (M = 3,80), moderate anxiety (M = 3,00), and generally positive attitudes (M = 3,60). Gender differences were evident, with males reporting higher literacy but lower anxiety, while females showed greater anxiety and slightly more positive attitudes. Socioeconomic status also influenced literacy and anxiety, favoring students from higher-income groups, though attitudes showed little variation. Training enhanced literacy but had negligible effects on anxiety and attitudes. Correlation analysis confirmed that higher literacy was strongly linked to lower anxiety and moderately associated with more positive attitudes, while anxiety was related to less favorable attitudes. These findings highlight the pivotal role of literacy in reducing apprehension and strengthening acceptance of AI. The study recommends embedding structured AI literacy programs in teacher education curricula, alongside targeted interventions for female and low-income students, to ensure equitable and confident readiness for AI integration in line with the goals of Education 5,0.
References
1. Ahmad S, Umirzakova S, Mujtaba G, Amin MS, Whangbo T. Education 5.0: Requirements, enabling technologies, and future directions. arXiv preprint. 2023. https://doi.org/10.48550/arXiv.2307.15846
2. Lucas M, Bem-haja P, Zhang Y, Llorente-Cejudo C, Palacios-Rodriguez A. A comparative analysis of pre-service teachers’ readiness for AI integration. Comput Educ Artif Intell. 2025; 8:100396. https://doi.org/10.1016/j.caeai.2025.100396 DOI: https://doi.org/10.1016/j.caeai.2025.100396
3. Luckin R, Cukurova M, Kent C, du Boulay B. Empowering educators to be AI-ready. Comput Educ Artif Intell. 2022; 3:100076. https://doi.org/10.1016/j.caeai.2022.100076 DOI: https://doi.org/10.1016/j.caeai.2022.100076
4. Miao F, Holmes W, Huang R, Zhang H. AI and education: A guidance for policymakers. Paris: UNESCO Publishing; 2021. https://unesdoc.unesco.org/ark:/48223/pf0000376709
5. Chan CKY. A comprehensive AI policy education framework for university teaching and learning. Int J Educ Technol High Educ. 2023;20(38). https://doi.org/10.1186/s41239-023-00408-3 DOI: https://doi.org/10.1186/s41239-023-00408-3
6. Tan Q, Tang X. Unveiling AI literacy in K-12 education: A systematic literature review of empirical research. Interact Learn Environ. 2025:1-17. https://doi.org/10.1080/10494820.2025.2482586 DOI: https://doi.org/10.1080/10494820.2025.2482586
7. Ayanwale MA, Frimpong EK, Opesemowo OAG, Sanusi IT. Exploring factors that support pre-service teachers’ engagement in learning artificial intelligence. J STEM Educ Res. 2025; 8:199-229. https://doi.org/10.1007/s41979-024-00121-4 DOI: https://doi.org/10.1007/s41979-024-00121-4
8. 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 DOI: https://doi.org/10.1016/j.ssaho.2024.101221
9. 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 DOI: https://doi.org/10.1016/j.procs.2025.07.079
10. Gao LX, López-Pérez ME, Melero-Polo I, Trifu A. Ask ChatGPT first! Transforming learning experiences in the age of artificial intelligence. Stud High Educ. 2024;49(12):2772-96. https://doi.org/10.1080/03075079.2024.2323571 DOI: https://doi.org/10.1080/03075079.2024.2323571
11. Gruetzemacher R, Whittlestone J. The transformative potential of artificial intelligence. Futures. 2022; 135:102884. https://doi.org/10.1016/j.futures.2021.102884 DOI: https://doi.org/10.1016/j.futures.2021.102884
12. Marcus G, Davis E. Rebooting AI: Building artificial intelligence we can trust. New York: Pantheon Books; 2019.
13. Baker R. Using learning analytics in personalized learning. In: Handbook on personalized learning for states, districts, and schools. Philadelphia: Temple University; 2016. p. 165-74.
14. Wang S, Wang F, Zhu Z, Wang J, Tran T, Du Z. Artificial intelligence in education: A systematic literature review. Expert Syst Appl. 2024; 213:118591. https://doi.org/10.1016/j.eswa.2024.124167 DOI: https://doi.org/10.1016/j.eswa.2024.124167
15. Agarwal V, Verma P, Ferrigno G. Education 5.0 challenges and sustainable development goals in emerging economies: A mixed-method approach. Technol Soc. 2025; 81:102814. https://doi.org/10.1016/j.techsoc.2025.102814 DOI: https://doi.org/10.1016/j.techsoc.2025.102814
16. Clorion FDD, Alieto EO, Fuentes JO, Suicano DJ, Natividad ER, Miñoza M, Pil A, Aidil-Karanain F, González Vallejo R. 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. 301-26. https://doi.org/10.1007/978-3-031-65691-0_16 DOI: https://doi.org/10.1007/978-3-031-65691-0_16
17. Shahidi Hamedani S, Aslam S, Mundher Oraibi BA, Wah YB, Shahidi Hamedani S. Transitioning toward tomorrow’s workforce: Education 5.0 in the landscape of Society 5.0: A systematic literature review. Educ Sci. 2024;14(10):1041. https://doi.org/10.3390/educsci14101041 DOI: https://doi.org/10.3390/educsci14101041
18. Laupichler MC, Aster A, Schirch J, Raupach T. Artificial intelligence literacy in higher and adult education: A scoping literature review. Comput Educ Artif Intell. 2022; 3:100101. https://doi.org/10.1016/j.caeai.2022.100101 DOI: https://doi.org/10.1016/j.caeai.2022.100101
19. Sperling K, Stenberg CJ, McGrath C, Åkerfeldt A, Heintz F, Stenliden L. In search of artificial intelligence (AI) literacy in teacher education: A scoping review. Comput Educ Open. 2024; 6:100169. https://doi.org/10.1016/j.caeo.2024.100169 DOI: https://doi.org/10.1016/j.caeo.2024.100169
20. Kaya F, Aydin F, Schepman A, Rodway P, Yetişensoy O, Demir Kaya M. 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. https://doi.org/10.1080/10447318.2022.2151730 DOI: https://doi.org/10.1080/10447318.2022.2151730
21. Santos ZM, Cadanao 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. In: Motahhir S, Bossoufi B, editors. Digital technologies and applications. ICDTA 2024. (Lecture Notes in Networks and Systems, vol. 1101). Cham: Springer; 2024. p. 168-77. https://doi.org/10.1007/978-3-031-68675-7_17 DOI: https://doi.org/10.1007/978-3-031-68675-7_17
22. Schepman A, Rodway P. Initial validation of the general attitudes toward artificial intelligence scale. Comput Hum Behav Rep. 2020; 1:100014. https://doi.org/10.1016/j.chbr.2020.100014 DOI: https://doi.org/10.1016/j.chbr.2020.100014
23. 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 DOI: https://doi.org/10.1080/10494820.2019.1674887
24. Wu X, Li H. A systematic review of AI anxiety in education. AI Ethics. 2025. Epub ahead of print. https://doi.org/10.1007/s43681-025-00783-9 DOI: https://doi.org/10.1007/s43681-025-00783-9
25. Ayduğ D, Altınpulluk H. Are Turkish pre-service teachers worried about AI? A study on AI anxiety and digital literacy. AI Soc. 2025. Epub ahead of print. https://doi.org/10.1007/s00146-025-02348-0 DOI: https://doi.org/10.1007/s00146-025-02348-0
26. Grassini S. Development and validation of the AI attitude scale (AIAS-4): A brief measure of general attitude toward artificial intelligence. Front Psychol. 2023; 14:1191628. https://doi.org/10.3389/fpsyg.2023.1191628 DOI: https://doi.org/10.3389/fpsyg.2023.1191628
27. Hajam KB, Gahir S. Unveiling the attitudes of university students toward artificial intelligence. J Educ Technol Syst. 2024;52(3):335-45. https://doi.org/10.1177/0047239523122592 DOI: https://doi.org/10.1177/00472395231225920
28. Laru J, Celik I, Jokela I, Mäkitalo K. The antecedents of pre-service teachers’ AI literacy: Perceptions about own AI driven applications, attitude toward AI and knowledge in machine learning. Eur J Teach Educ. 2025:1-23. https://doi.org/10.1080/02619768.2025.2535623 DOI: https://doi.org/10.1080/02619768.2025.2535623
29. Kohnke L, Zou D, Ou AW, Gu MM. Preparing future educators for AI-enhanced classrooms: Insights into AI literacy and integration. Comput Educ Artif Intell. 2025; 8:100398. https://doi.org/10.1016/j.caeai.2025.100398 DOI: https://doi.org/10.1016/j.caeai.2025.100398
30. Alieto EO, Dumagay AH, Serdenia JRC, Labad EM, Galang SK, Vallejo RG. Attitude toward artificial intelligence among teacher aspirants in an emerging AI landscape: A gender-based analysis. 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_39 DOI: https://doi.org/10.1007/978-3-031-98476-1_39
31. 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 DOI: https://doi.org/10.1007/978-3-031-98476-1_6
32. Özüdoğru G, Durak HY. Conceptualizing pre-service teachers’ artificial intelligence readiness and examining its relationship with various variables: The role of artificial intelligence literacy, digital citizenship, artificial intelligence-enhanced innovation and perceived threats from artificial intelligence. Inf Dev. 2025;41(3):916-32. https://doi.org/10.1177/02666669251335657 DOI: https://doi.org/10.1177/02666669251335657
33. Cabato JU. From awareness to practice: Exploring the knowledge, attitudes, and practices of secondary ESL teachers in the Philippines toward ChatGPT in education. LatIA. 2025; 3:360. https://doi.org/10.62486/latia2025360 DOI: https://doi.org/10.62486/latia2025360
34. Bond M, Khosravi H, De Laat M, Bergdahl N, Negrea V, Oxley E, Pham O, Chong SW, Siemens G. A meta systematic review of artificial intelligence in higher education: A call for increased ethics, collaboration, and rigour. Int J Educ Technol High Educ. 2024; 21:4. https://doi.org/10.1186/s41239-023-00436-z DOI: https://doi.org/10.1186/s41239-023-00436-z
35. Zawacki-Richter O, Marín VI, Bond M, Gouverneur F. Systematic review of research on artificial intelligence applications in higher education–where are the educators? Int J Educ Technol High Educ. 2019; 16:39. https://doi.org/10.1186/s41239-019-0171-0 DOI: https://doi.org/10.1186/s41239-019-0171-0
36. Pereira DSM, Falcão F, Costa L, Lunn BS, Pêgo JM, Costa P. Here’s to the future: Conversational agents in higher education – a scoping review. Int J Educ Res. 2023; 122:102233. https://doi.org/10.1016/j.ijer.2023.102233 DOI: https://doi.org/10.1016/j.ijer.2023.102233
37. Su J, Yang W. Artificial intelligence in early childhood education: A scoping review. Comput Educ Artif Intell. 2022; 3:100049. https://doi.org/10.1016/j.caeai.2022.100049 DOI: https://doi.org/10.1016/j.caeai.2022.100049
38. Xia Q, Weng X, Ouyang F, Lin TJ, Chiu TKF. A scoping review on how generative artificial intelligence transforms assessment in higher education. Int J Educ Technol High Educ. 2024; 21:40. https://doi.org/10.1186/s41239-024-00468-z DOI: https://doi.org/10.1186/s41239-024-00468-z
39. Schiff D. Education for AI, not AI for education: The role of education and ethics in national AI policy strategies. Int J Artif Intell Educ. 2022; 32:527-63. https://doi.org/10.1007/s40593-021-00270-2 DOI: https://doi.org/10.1007/s40593-021-00270-2
40. Seo K, Dodson S, Harandi NM, Roberson N, Fels S, Roll I. Active learning with online video: The impact of learning context on engagement. Comput Educ. 2021; 165:104132. https://doi.org/10.1016/j.compedu.2021.104132 DOI: https://doi.org/10.1016/j.compedu.2021.104132
41. Verboom ADPR, Pais L, Zijlstra FRH, Oswald FL, dos Santos NR. Perceptions of artificial intelligence in academic teaching and research: A qualitative study from AI experts and professors’ perspectives. Int J Educ Technol High Educ. 2025; 22:46. https://doi.org/10.1186/s41239-025-00546-w DOI: https://doi.org/10.1186/s41239-025-00546-w
42. Alharbi A. Implementation of Education 5.0 in developed and developing countries: A comparative study. Creat Educ. 2023; 14:914-42. https://doi.org/10.4236/ce.2023.145059 DOI: https://doi.org/10.4236/ce.2023.145059
43. Clorion FD, Fuentes J, Suicano DJ, Estigoy E, Serdenia JR, Alejandrino P, Albani S, Idris DL, Paclibar D, Torres-Toukoumidis A, Alieto EO. 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. DOI: https://doi.org/10.1016/j.procs.2025.03.004
44. Estrellado CJ, Miranda JC. Artificial intelligence in the Philippine educational context: Circumspection and future inquiries. Int J Sci Res Publ. 2023;13(5):375-81. Available from: https://ssrn.com/abstract=4442136 DOI: https://doi.org/10.29322/IJSRP.13.05.2023.p13704
45. Fernandez MA, Cabangcala C, Fanilag E, Cabangcala C, Balasa K, Alieto E. 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; 2023. p. 248-55. https://doi.org/10.1007/978-3-031-48465-0_33 DOI: https://doi.org/10.1007/978-3-031-48465-0_33
46. 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. DOI: https://doi.org/10.1007/978-3-031-48465-0_18
47. Alieto EO, 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. https://doi.org/10.1016/j.heliyon.2024.e24282 DOI: https://doi.org/10.1016/j.heliyon.2024.e24282
48. Asio JM, Soriano ID. The state of artificial intelligence (AI) use in higher education institutions (HEIs) in the Philippines. In: Mobo F, editor. Impacts of AI on students and teachers in education 5.0. Hershey, PA: IGI Global Scientific Publishing; 2025. p. 523-52. https://doi.org/10.4018/979-8-3693-8191-5.ch019 DOI: https://doi.org/10.4018/979-8-3693-8191-5.ch019
49. Long D, Magerko B. What is AI literacy? Competencies and design considerations. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems. 2020. p. 1-16. https://doi.org/10.1145/3313831.3376727 DOI: https://doi.org/10.1145/3313831.3376727
50. 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 DOI: https://doi.org/10.1016/j.caeai.2021.100041
51. Schiavo G, Businaro S, Zancanaro M. Comprehension, apprehension, and acceptance: Understanding the influence of literacy and anxiety on acceptance of artificial intelligence. Technol Soc. 2024; 77:102537. https://doi.org/10.1016/j.techsoc.2024.102537 DOI: https://doi.org/10.1016/j.techsoc.2024.102537
52. Dumagay AH, Balasa KA, Kunting AF, Cabangcala RB. AI acceptance among prospective social studies and culture and arts education students. In: Arai K, editor. Intelligent computing. CompCom 2025. (Lecture Notes in Networks and Systems, vol. 1426). Cham: Springer; 2025. https://doi.org/10.1007/978-3-031-92611-2_11 DOI: https://doi.org/10.1007/978-3-031-92611-2_11
53. 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. https://doi.org/10.1016/j.caeai.2024.100202 DOI: https://doi.org/10.1016/j.caeai.2024.100202
54. 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. DOI: https://doi.org/10.1007/978-3-031-68675-7_16
55. Casiano PK, Encarnacion B, Jaafar S, Alieto EO. Digital-game-based language learning: An exploration of attitudes among teacher aspirants in a nonmetropolitan 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; 2023. p. 427-42. https://doi.org/10.1007/978-981-97-0892-5_34 DOI: https://doi.org/10.1007/978-981-97-0892-5_34
56. Flores B, Amabao K, Aidil-Karanain F, Dumagay AH. Bachelor of culture and arts students’ attitude toward using digital games for learning. Sci Int (Lahore). 2023;35(3):357-61.
57. Gregorio TA, Alieto EO, Natividad ER, Tanpoco M. 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: Motahhir S, Bossoufi B, editors. Digital technologies and applications. ICDTA 2024. (Lecture Notes in Networks and Systems, vol. 100). Cham: Springer; 2024. p. 45-55. https://doi.org/10.1007/978-3-031-68660-3_5 DOI: https://doi.org/10.1007/978-3-031-68660-3_5
58. Johnson D, Verdicchio M. AI anxiety. J Assoc Inf Sci Technol. 2017;68:2267-70. https://doi.org/10.1002/asi.23867 DOI: https://doi.org/10.1002/asi.23867
59. Wilson ML, Huggins-Manley AC, Ritzhaupt AD, Ruggles K. Development of the Abbreviated Technology Anxiety Scale (ATAS). Behav Res. 2023; 55:185-99. https://doi.org/10.3758/s13428-022-01820-9 DOI: https://doi.org/10.3758/s13428-022-01820-9
60. 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 DOI: https://doi.org/10.1007/978-3-031-98476-1_40
61. Hopcan S, Türkmen G, Polat E. Exploring the artificial intelligence anxiety and machine learning attitudes of teacher candidates. Educ Inf Technol. 2023; 29:7281-301. https://doi.org/10.1007/s10639-023-12086-9 DOI: https://doi.org/10.1007/s10639-023-12086-9
62. Klimova B, Pikhart M. Exploring the effects of artificial intelligence on student and academic well-being in higher education: A mini review. Front Psychol. 2025; 16:1498132. https://doi.org/10.3389/fpsyg.2025.1498132 DOI: https://doi.org/10.3389/fpsyg.2025.1498132
63. Lund BD, Mannuru NR, Agbaji D. AI anxiety and fear: A look at perspectives of information science students and professionals towards artificial intelligence. J Inf Sci. 2024;0(0). https://doi.org/10.1177/01655515241282001 DOI: https://doi.org/10.1177/01655515241282001
64. Stein JP, Messingschlager T, Gnambs T, Hutmacher F, Appel M. Attitudes towards AI: measurement and associations with personality. Sci Rep. 2024; 14:2909. https://doi.org/10.1038/s41598-024-53335-2 DOI: https://doi.org/10.1038/s41598-024-53335-2
65. Brauner P, Glawe F, Liehner GL, Vervier L, Ziefle M. Mapping public perception of artificial intelligence: Expectations, risk–benefit tradeoffs, and value as determinants for societal acceptance. Technol Forecast Soc Change. 2025; 220:124304. https://doi.org/10.1016/j.techfore.2025.124304 DOI: https://doi.org/10.1016/j.techfore.2025.124304
66. Serdenia JR, Dumagay AH, Balasa K, Capacio E, Lauzon LD. Attitude, acceptability, and perceived effectiveness of artificial intelligence in education: A quantitative cross-sectional study among future teachers. LatIA. 2025; 3:313. https://doi.org/10.62486/latia2025313 DOI: https://doi.org/10.62486/latia2025313
67. Balasa K, Dumagay AH, Alieto EO, González Vallejo R. 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). Available from: https://doi.org/10.56294/mw2025668 DOI: https://doi.org/10.56294/mw2025668
68. Francisco CI, Pantaleon S, Lantaya GM, Francisco WA, Alieto EO. Understanding the attitude of senior high school students toward utilizing ChatGPT as a learning tool: A quantitative analysis. In: Hamdan RK, editor. Sustainable data management. Vol. 171. Cham: Springer; 2025. p. 37-49. https://doi.org/10.1007/978-3-031-83911-5_4 DOI: https://doi.org/10.1007/978-3-031-83911-5_4
69. Kalniņa D, Nīmante D, Baranova S. Artificial intelligence for higher education: Benefits and challenges for pre-service teachers. Front Educ. 2024; 9:1501819. https://doi.org/10.3389/feduc.2024.1501819 DOI: https://doi.org/10.3389/feduc.2024.1501819
70. Sharma H, Soetan T, Farinloye T, Mogaji E, Noite MDF. AI adoption in universities in emerging economies: Prospects, challenges and recommendations. In: Mogaji E, Jain V, Maringe F, Hinson RE, editors. Reimagining educational futures in developing countries. Cham: Palgrave Macmillan; 2022. p. 157-76. https://doi.org/10.1007/978-3-030-88234-1_9 DOI: https://doi.org/10.1007/978-3-030-88234-1_9
71. Abdulayeva A, Zhanatbekova N, Andasbayev Y, Boribekova F. Fostering AI literacy in pre-service physics teachers: Inputs from training and covariables. Front Educ. 2025; 10:1505420. Available from: https://doi.org/10.3389/feduc.2025.1505420 DOI: https://doi.org/10.3389/feduc.2025.1505420
72. Molefi RR, Ayanwale MA, Kurata L, Chere-Masopha J. Do in-service teachers accept artificial intelligence-driven technology? The mediating role of school support and resources. Comput Educ Open. 2024; 6:100191. https://doi.org/10.1016/j.caeo.2024.100191 DOI: https://doi.org/10.1016/j.caeo.2024.100191
73. Ofosu-Ampong K. Beyond the hype: exploring faculty perceptions and acceptability of AI in teaching practices. Discov Educ. 2024; 3:38. https://doi.org/10.1007/s44217-024-00128-4 DOI: https://doi.org/10.1007/s44217-024-00128-4
74. Creswell JW, Creswell JD. Research design: Qualitative, quantitative, and mixed methods approaches. 5th ed. Thousand Oaks, CA: SAGE Publications; 2018
75. Creswell JW. Research design: Qualitative, quantitative, and mixed methods approaches. 3rd ed. Thousand Oaks, CA: SAGE Publications, Inc.; 2009
76. 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 DOI: https://doi.org/10.1016/j.caeo.2024.100179
77. Guan L, Zhang Y, Gu MM. Pre-service teachers’ preparedness for AI-integrated education: An investigation from perceptions, capabilities, and teachers’ identity changes. Comput Educ Artif Intell. 2025; 8:100341. https://doi.org/10.1016/j.caeai.2024.100341 DOI: https://doi.org/10.1016/j.caeai.2024.100341
78. Perla L, Agrati LS, Beri A. Post teaching and professional learning: an investigation on teachers’ attitudes toward AI. Prof Dev Educ. 2025;51(3):466–77. https://doi.org/10.1080/19415257.2025.2465970 DOI: https://doi.org/10.1080/19415257.2025.2465970
79. Dilek M, Baran E, Aleman E. AI literacy in teacher education: Empowering educators through critical co-discovery. J Teach Educ. 2025;76(3):294–311. https://doi.org/10.1177/00224871251325083. DOI: https://doi.org/10.1177/00224871251325083
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