Gender and Age Dynamics in Future Educators' Attitudes toward AI Integration in Education: A Sample from State-managed Universities in Zamboanga Peninsula, Philippines
DOI:
https://doi.org/10.56294/mw2025668Keywords:
gender, age, artificial intelligenceAbstract
Gender and age are critical factors in understanding attitudes toward artificial intelligence (AI) in education, yet limited research has directly explored their influence on teacher aspirants’ perspectives on AI integration. This study employed random sampling to select 603 respondents from two state-managed institutions. Findings indicate that prospective teachers generally hold neutral attitudes toward AI (M=2.84), reflecting uncertainty about preferring AI over human interaction in routine tasks, consistent with prior research. Male respondents (M=2.91) exhibited significantly more positive attitudes toward AI in education than females, as evidenced by a t value of -2.66 and a p value of 0.008. Additionally, adults (M=2.86) demonstrated significantly higher attitude scores than adolescents (M=2.80), with a t value of -2.05 and a p value of 0.040. These results highlight the role of demographic variables in shaping perceptions of AI in educational contexts, emphasizing the need for targeted interventions to address concerns and optimize AI adoption in teacher training programs.
References
1. Amiri H, Peiravi S, Rezazadeh Shojaee SS, Rouhparvarzamin M, Nateghi MN, Etemadi MH, ..., Asadi Anar M. Medical, dental, and nursing students’ attitudes and knowledge towards artificial intelligence: a systematic review and meta-analysis. BMC Medical Education. 2024;24(1):412.
2. Baby D, John L, Pia JC, Sreedevi PV, Pattnaik SJ, Varkey A, Gupta S. (2023). Role of robotics and artificial intelligence in oral health education. Knowledge, perception and attitude of dentists in India. Journal of Education and Health Promotion. 2023;12(1):384.
3. Serbaya SH, Khan AA, Surbaya SH, Alzahrani SM. Knowledge, Attitude and Practice Toward Artificial Intelligence Among Healthcare Workers in Private Polyclinics in Jeddah, Saudi Arabia. Advances in Medical Education and Practice. 2024:269-280.
4. Almaraz-López C, Almaraz-Menéndez F, López-Esteban C. Comparative study of the attitudes and perceptions of university students in business administration and management and in education toward artificial intelligence. Education Sciences. 2023;13(6):609.
5. Delcker J, Heil J, Ifenthaler D, Seufert S, Spirgi L. First-year students AI-competence as a predictor for intended and de facto use of AI-tools for supporting learning processes in higher education. International Journal of Educational Technology in Higher Education. 2024;21(1):18.
6. Galindo-Domínguez H, Delgado N, Campo L, Losada, D. Relationship between teachers’ digital competence and attitudes towards artificial intelligence in education. International Journal of Educational Research. 2024;126:102381.
7. Gaber SA, Shahat HA, Alkhateeb IA, Al Hasan SA, Alqatam MA, Almughyirah SM, Keshar Kamel M. Faculty Members’ Awareness of Artificial Intelligence and Its Relationship to Technology Acceptance and Digital Competencies at King Faisal University. International Journal of Learning, Teaching and Educational Research. 2023;22(7):473-496.
8. Obenza BN, Caballo JHS, Caangay RBR, Makigod TEC, Almocera SM, Bayno JLM..., Tua AG. Analyzing University Students’ Attitude and Behavior Toward AI Using the Extended Unified Theory of Acceptance and Use of Technology Model. American Journal of Applied Statistics and Economics. 2024;3(1):99-108.
9. Pörn R, Braskén M, Wingren M, Andersson S. Attitudes towards and expectations on the role of artificial intelligence in the classroom among digitally skilled Finnish K-12 mathematics teachers. LUMAT: International Journal on Math, Science and Technology Education. 2024;12(3):53-77. https://doi.org/10.31129/LUMAT.12.3.2102
10. Boute RN, Udenio M. (2022). AI in logistics and supply chain management. In: Global Logistics and Supply Chain Strategies for the 2020s: Vital Skills for the Next Generation. Springer, p. 49-65.
11. Tsolakis N, Zissis D, Papaefthimiou S, Korfiatis N. Towards AI driven environmental sustainability: an application of automated logistics in container port terminals. International Journal of Production Research. 2022;60(14): 4508-4528.
12. Bergdahl J, Latikka R, Celuch M, Savolainen I, Mantere ES, Savela N, Oksanen A. Self-determination and attitudes toward artificial intelligence: Cross-national and longitudinal perspectives. Telematics and Informatics. 2023;82: 102013.
13. Kengam J. Artificial intelligence in education. Research Gate. 2020;18:1-4.
14. Vuorikari R, Kluzer S, Punie Y. DigComp 2.2: The Digital Competence Framework for Citizens-With new examples of knowledge, skills and attitudes; 2022.
15. Choung H, David P, Ross A. Trust in AI and its role in the acceptance of AI technologies. International Journal of Human–Computer Interaction. 2023;39(9):1727-1739.
16. Kelly S, Kaye SA, Oviedo-Trespalacios O. What factors contribute to the acceptance of artificial intelligence? A systematic review. Telematics and Informatics. 2023;77:101925.
17. Schepman A, Rodway, P. Initial validation of the general attitudes towards artificial intelligence scale. Computers in Human Behavior Reports. 2020;1:100014.
18. Schepman A, Rodway P. 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. 2022:1–18.
19. Flores-Cruz G, Hinkle SD, Roque NA, Mouloua M. ChatGPT as the Ultimate Travel Buddy or Research Assistant: A Study on Perceived Attitudes and Usability. In: Proceedings of the Human Factors and Ergonomics Society Annual Meeting (Vol. 67, No. 1, Sage CA: Los Angeles, CA: SAGE Publications; 2023, p. 459-464.
20. Amante-Nochefranca G, Orbase-Sandal O, Alieto E, Laput I, Albani S, Lucas RI, Tanpoco M. AI-Assisted English Language Learning and Teaching in a Developing Country: An Investigation of ESl Student’s Beliefs and Challenges. 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. Springer; 2023.
21. Ferrari A, Punie Y. DIGCOMP: A framework for developing and understanding digital competence in Europe; 2013.
22. Skantz-Åberg E, Lantz-Andersson A, Lundin M, Williams P. (2022). Teachers’ professional digital competence: An overview of conceptualisations in the literature. Cogent Education. 2022;9(1):2063224.
23. Kreps S, George J, Lushenko P, Rao A. (2023). Exploring the artificial intelligence “Trust paradox”: Evidence from a survey experiment in the United States. Plos one. 2023;18(7): e0288109.
24. Eitel-Porter R. Beyond the promise: implementing ethical AI. AI and Ethics. 2021;1(1):73-80.
25. Kok JN, Boers EJ, Kosters WA, Van der Putten P, Poel M. Artificial intelligence: definition, trends, techniques, and cases. Artificial intelligence. 2009;1(270-299).
26. Chen L, Chen P, Lin Z. Artificial intelligence in education: A review. Ieee Access. 2020;8:75264-75278.
27. Sternberg RJ, editor. Human intelligence: An introduction. Cambridge University Press; 2019.
28. Baker R. “Stupid Tutoring Systems, Intelligent Humans.” International Journal of Artificial Intelligence in Education. 2016;26(2):600-614.
29. Kinshuk Chen NS, Cheng IL, Chew SW. Evolution is not enough: Revolutionizing current learning environments to smart learning environments. International Journal of Artificial Intelligence in Educatio. 2016;26:561-581.
30. Bahroun Z, Anane C, Ahmed V, Zacca A. Transforming education: A comprehensive review of generative artificial intelligence in educational settings through bibliometric and content analysis. Sustainability. 2023;15(17):12983.
31. Lordan G, Stringer EJ. People versus machines: The impact of being in an automatable job on Australian worker’s mental health and life satisfaction. Economics & Human Biology. 2022;46:101144.
32. Arnold KE, Pistilli MD. Course signals at Purdue: Using learning analytics to increase student success. In: Proceedings of the 2nd international conference on learning analytics and knowledge; 2012, p. 267-270.
33. Elias S, Smith W, Barney C. Age as a moderator of attitude towards technology in the workplace: work motivation and overall job satisfaction. Behavior and Information Technology. 2012;31(5):453-467.
34. Ryan RM, Deci EL. Self-determination theory: Basic psychological needs in motivation, development, and wellness. Guilford publications; 2017.
35. Ryan RM, Deci EL. Intrinsic and extrinsic motivation from a self-determination theory perspective: Definitions, theory, practices, and future directions. Contemporary educational psychology. 2020;61:101860.
36. Chiu TK, Chai CS. Sustainable curriculum planning for artificial intelligence education: A self-determination theory perspective. Sustainability. 2020;12(14):5568.
37. Chiu TK, Xia Q, Zhou X, Chai CS, Cheng M. Systematic literature review on opportunities, challenges, and future research recommendations of artificial intelligence in education. Computers and Education: Artificial Intelligence. 2023;4:100118.
38. Chiu TK, Lin TJ, Lonka K. (2021). Motivating online learning: The challenges of COVID-19 and beyond. The Asia-pacific Education Researcher. 2021;30(3):187-190.
39. Hartnett MK. Influences that undermine learners’ perceptions of autonomy, competence and relatedness in an online context. Australasian Journal of Educational Technology. 2015;31(1).
40. Al-Subhy A. The reality of artificial intelligence applications use in education by faculty members at Najran University. Journal of the Faculty of Education. 2020;4(44):319-368.
41. Badawi S, Drăgoicea M. Towards a value co-creation process in collaborative environments for tvet education. Sustainability. 2023;15(3):1792.
42. Jantakun T, Jantakun K, Jantakoon T. A Common Framework for Artificial Intelligence in Higher Education (AAI-HE Mode). International Education Studies. 2021;14(11):94-103.
43. Kim J, Ham Y, Lee SS. Differences in student-AI interaction process on a drawing task: Focusing on students’ attitude towards AI and the level of drawing skills. Australasian Journal of Educational Technology. 2022;40(1):1-23.
44. Kairu C. Students’ attitude towards the use of artificial intelligence and machine learning to measure classroom engagement activities. In: EdMedia+ Innovate Learning. Association for the Advancement of Computing in Education (AACE); 2020, p. 793-802.
45. Marrone R, Taddeo V, Hill G. Creativity and artificial intelligence—A student perspective. Journal of Intelligence. 2022;10(3):65.
46. Álvarez-Álvarez C, Falcon S. Students’ preferences with university teaching practices: Analysis of testimonials with artificial intelligence. Educational technology research and development. 2023;71(4):1709-1724.
47. Sindermann C, Sha P, Zhou M, Wernicke J, Schmitt HS, Li M ..., Montag C. Assessing the attitude towards artificial intelligence: Introduction of a short measure in German, Chinese, and English language. KI-Künstliche intelligenz. 2021;35(1):109-118.
48. Gillespie N, Lockey S, Curtis C. Trust in artificial intelligence: A five country study. The University of Queensland and KPMG Australia; 2021.
49. Kaya F, Aydin F, Schepman A, Rodway P, Yetis ̧Ensoy O, Demir Kaya M. (2022). The roles of personality traits, AI anxiety, and demographic factors in attitudes toward artificial intelligence. International Journal of Human-Computer Interaction. 2022:1–18. Advance online publication.
50. Stockemer D. Quantitative Methods for the Social Sciences: A Practical Introduction with Examples in SPSS and Stata. Springer International Publishing AG. 2019: 31-32.
51. Creswell JW. Research Design: Qualitative, Quantitative, and Mixed Methods Approaches, SAGE Publications; 2009.
52. Leedy P, Ormrod J. Practical research: Planning and design (7th ed.). Upper Saddle River, NJ: Merrill Prentice Hall. Thousand Oaks: SAGE Publications; 2001.
53. Alieto E, Abequibel-Encarnacion B, Estigoy E, Balasa K, Eijansantos A, Torres-Toukoumidis. Teaching inside a digital classroom: A quantitative analysis of attitude, technological competence and access among teachers across subject disciplines. Heliyon. 2024;10(2): e24282.
54. Gonzales LI, Yusop R, 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. Springer; 2024
55. Kwak SG, Kim JH. Central limit theorem: the cornerstone of modern statistics. Korean Journal of Anesthesiology. 2017;70(2):144-156.
56. Rainie L, Anderson J. The Future of Jobs and Jobs Training, Pew Research Center; 2017.
57. Brynjolfsson E, McAfee A. The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies. WW Norton & Company; 2014.
58. Shank D. Technology and Emotions. In: Stets J, Turner J, editors. Handbook of the Sociology of Emotions: Volume II. Handbooks of Sociology and Social Research. Springer; 2014.
59. Osoba OA, Welser IV W, Welser W. An intelligence in our image: The risks of bias and errors in artificial intelligence. Rand Corporation; 2017.
60. Anderson J, Rainie L, Luchsinger A. Artificial intelligence and the future of humans. Pew Research Center. 2018;10(12).
61. Cai Z, Fan X, Dun J. Gender and attitudes toward technology use: A meta-analysis, Computers & Education. 2017;105:1-13.
62. Selwyn N. Degrees of Digital Division: Reconsidering Digital Inequalities and Contemporary Higher Education RUSC. Universities and Knowledge Society Journal. 2010;7(1):33-42.
Published
Issue
Section
License
Copyright (c) 2025 Keir A. Balasa, Alexandhrea Hiedie Dumagay, Ericson O. Alieto, Rubén González Vallejo (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
The article is distributed under the Creative Commons Attribution 4.0 License. Unless otherwise stated, associated published material is distributed under the same licence.
