Lifelong learning predicting artificial intelligence literacy: A hierarchical multiple linear regression analysis

Abstract

Bu çalışma, öğretmen adaylarının yaşam boyu öğrenme (YBÖ) eğilimleri ile yapay zekâ (YZ) okuryazarlıkları arasındaki ilişkiyi incelemiştir. Bu doğrultuda çalışmanın amacı, öğretmen adaylarının YBÖ’ye olan eğilimlerinin, temel YZ yeterliliklerinin gelişimine katkı sağlayıp sağlamadığını anlamaktır. Türkiye’deki çeşitli üniversite ve bölümlerden toplam 318 öğretmen adayı kolayda örnekleme yöntemiyle çalışmanın örneklemini oluşturmuştur. Sonuçlar, YBÖ eğilimleri ile genel YZ okuryazarlığı arasında ve YZ okuryazarlığının her bir alt boyutu—farkındalık, kullanım, değerlendirme ve etik—arasında anlamlı ve pozitif ilişki olduğunu ortaya koymuştur. Bu bulgular, YBÖ eğilimleri yüksek olan öğretmen adaylarının daha yüksek düzeyde YZ okuryazarlığına sahip olma eğiliminde olduğunu göstermektedir. YZ okuryazarlığını yordayan değişkenleri incelemek amacıyla hiyerarşik çoklu doğrusal regresyon analizi uygulanmıştır. İlk modelde bilgi ve iletişim teknolojileri yeterliliği anlamlı bir yordayıcı olarak bulunmuş, ikinci modelde ise YBÖ eğilimi eklendiğinde modelin yordayıcılık gücü anlamlı düzeyde artmıştır. Sonuç olarak, bilgi ve iletişim teknolojileri yeterliliği ve YBÖ eğilimi, öğretmen adaylarının YZ okuryazarlığını istatistiksel olarak anlamlı şekilde yordayan değişkenler olarak bulunmuştur. Bu bulgular, öğretmen adaylarının mesleki öğrenmelerinde YZ okuryazarlıklarının geliştirilmesi için YBÖ eğilimlerinin artırılmasının önemini ortaya koymaktadır.

Keywords

• Mesleki öğrenme
• Yapay zekâ
• Yaşam boyu öğrenme
• Hizmet öncesi öğretmenler

Lifelong learning predicting artificial intelligence literacy: A hierarchical multiple linear regression analysis

Abstract

This study investigated the relationship between preservice teachers’ lifelong learning (LLL) tendencies and their artificial intelligence (AI) literacy. It aimed to understand whether a more substantial commitment to LLL contributes to developing essential AI competencies among future educators. A total of 318 preservice teachers from various universities and departments in Turkiye, selected through convenience sampling, participated in the study. The results revealed significant positive correlations between LLL tendencies and overall AI literacy, as well as with each AI literacy subdimension, namely awareness, usage, evaluation, and ethics. The results suggested that preservice teachers with higher LLL tendencies tend to be more AI literate. Hierarchical multiple linear regression analysis was utilized to investigate whether demographic variables- gender, year of study, ICT competency, and AI tool usage- and LLL tendencies predicted AI literacy. ICT competency was found to be a significant predictor in the first model, and in the second model, LLL significantly improved the predictive power. As a result, ICT competency and LLL showed statistically significant predictive effects on preservice teachers’ AI literacy. These findings indicate the importance of improving preservice teachers’ LLL tendency to enhance AI literacy in their professional learning.

Keywords

• Professional learning
• Artificial intelligence
• Lifelong learning
• Preservice teachers

References

1. Ahn, H. Y. (2024). AI-powered e-learning for lifelong learners: impact on performance and knowledge application. Sustainability, 16(20), 9066. https://doi.org/10.3390/su16209066
2. Asad, M.M. & Aijaz, A. (2025). Influence of ChatGPT and generative AI on continuous learning and skill enhancement in Education 4.0: contextual insights from the higher education of Pakistan, The TQM Journal, Vol. ahead-of-print No. ahead-of-print. Retrieved April 08, 2025 from https://doi.org/10.1108/TQM-09-2024-0357
3. Bozkurt, A., Xiao, J., Lambert, S., Pazurek, A., Crompton, H., Koseoglu, S., Farrow, R., Bond, M., Nerantzi, C., Honeychurch, S., Bali, M., Dron, J., Mir, K., Stewart, B., Costello, E., Mason, J., Stracke, C. M., Romero-Hall, E., Koutropoulos, A.,…Jandrić, P. (2023). Speculative futures on ChatGPT and generative artificial intelligence (AI): A collective reflection from the educational landscape. Asian Journal of Distance Education, 18(1), 53–130. https://doi.org/10.5281/zenodo.7636568
4. Bozkurt, A., & Sharma, R. C. (2023). Challenging the status quo and exploring the new boundaries in the age of algorithms: Reimagining the role of generative AI in distance education and online learning. Asian Journal of Distance Education, 18(1). https://asianjde.com/ojs/index.php/AsianJDE/article/view/714
5. Boztepe, O., & Demirtas, Z. (2016). The adaptation of lifelong learning scale into Turkish culture. Journal of Family Counseling and Education, 1(1), 10-17. https://doi.org/10.32568/jfce.238294
6. Cachero, C., Tomás, D. & Pujol, F. A. (2025). Gender bias in self-perception of artificial intelligence knowledge, impact, and support among higher education students: an observational study. ACM Transactions in Computer Education. (March 2025). Retrieved April 10, 2025 from https://doi.org/10.1145/3721295
7. Cavalcanti, A. P., Barbosa, A., Carvalho, R., Freitas, F., Tsai, Y. S., Gašević, D., & Mello, R. F. (2021). Automatic feedback in online learning environments: A systematic literature review. Computers and Education: Artificial Intelligence, 2, 100027. https://doi.org/10.1016/j.caeai.2021.100027
8. Celebi, C., Yilmaz, F., Demir, U., Karakus, F. (2023). Artificial intelligence literacy: An adaptation study. Instructional Technology and Lifelong Learning, 4(2), 291-306. https://doi.org/10.52911/itall.1401740

9. Celik, I. (2023). Exploring the determinants of artificial intelligence (Ai) literacy: Digital divide, computational thinking, cognitive absorption. Telematics and Informatics, 83, 102026. https://doi.org/10.1016/j.tele.2023.102026
10. Cetindamar, D., Kitto, K., Wu, M., Zhang, Y., Abedin, B., & Knight, S. (2022). Explicating AI literacy of employees at digital workplaces. Ieee Transactions on Engineering Management, 71, 810–823. https://doi.org/10.1109/TEM.2021.3138503
11. Creswell, J. W. (2014). Research design: Qualitative, quantitative, and mixed methods approaches. Sage
12. Cukurova, M., & Miao, F. (2024). AI competency framework for teachers. UNESCO Publishing. https://unesdoc.unesco.org/ark:/48223/pf0000391104
13. Cummins, P., & Kunkel, S. (2015). A Global Examination of Policies and Practices for Lifelong Learning. New Horizons in Adult Education and Human Resource Development, 27(3), 3–17. https://doi.org/10.1002/nha3.20107
14. Dai, Y., Chai, C.-S., Lin, P.-Y., Jong, M. S.-Y., Guo, Y., & Qin, J. (2020). Promoting students’ well-being by developing their readiness for the artificial intelligence age. Sustainability, 12(16), 6597. https://doi.org/10.3390/su12166597
15. Delors, J. (1996). Learning: The treasure within; report to UNESCO of the International Commission on Education for the Twenty-first Century. UNESCO. Retrieved April 08, 2025 from https://unesdoc.unesco.org/ark:/48223/pf0000109590
16. diSessa, A. A. (2018). Computational literacy and “the big picture” concerning computers in mathematics education. Mathematical Thinking and Learning, 20(1), 3–31. https://doi.org/10.1080/10986065.2018.1403544
17. Fayda-Kinik, F. S. (2023). The impact of digital competences on academic procrastination in higher education: a structural equation modeling approach. Pegem Journal of Education and Instruction, 13(3), 25–35. https://doi.org/10.47750/pegegog.13.03.03
18. Field, A. (2013). Discovering statistics using IBM SPSS statistics (4th ed.). Sage Publications
19. Fietta, V., Zecchinato, F., Di Stasi, B., Polato, M., & Monaro, M. (2022). Dissociation between users’ explicit and implicit attitudes toward artificial intelligence: An experimental study. IEEE Transactions on Human-Machine Systems, 52(3), 481–489. https://doi.org/10.1109/THMS.2021.3125280
20. Gong, X., Tang, Y., Liu, X., Jing, S., Cui, W., Liang, J., & Wang, F. Y. (2020, October). K-9 artificial intelligence education in Qingdao: Issues, challenges and suggestions. In 2020 IEEE international Conference on networking, Sensing and control (ICNSC) (pp. 1–6). IEEE. https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9238087
21. González-Anleo, J. M., Delbello, L., Martínez-Gonzálo, J. M., & Gómez, A. (2024). Sociodemographic impact on the adoption of emerging technologies. Journal of Small Business Strategy, 34(2), 42–50. https://doi.org/10.53703/001c.122089
22. Gummer, E. S., & Mandinach, E. B. (2015). Building a conceptual framework for data literacy. Teachers College Record, 117(4), 1–22. https://doi.org/10.1177/016146811511700401
23. Hagger, H., Burn, K., Mutton, T., & Brindley, S. (2008). Practice makes perfect? Learning to learn as a teacher. Oxford review of education, 34(2), 159–178. https://doi.org/10.1080/03054980701614978
24. Hava, K., & Babayigit, O. (2025). Exploring the relationship between teachers’ competencies in AI-TPACK and digital proficiency. Education and Information Technologies, 30, 3491–3508. https://doi.org/10.1007/s10639-024-12939-x
25. Hong, W., Chan, F. K. Y., Thong, J. Y. L., Chasalow, L., & Dhillon, G. (2014). A framework and guidelines for context-specific theorizing in information systems research. Information Systems Research, 25(1), 111–136. http://hdl.handle.net/20.500.12424/4276068
26. Huck, S. W. (2012). Reading Statistics and Research (6th ed.). Pearson.
27. Humble, N. (2023). A lifelong learning perspective on artificial intelligence: What do we need to know?. In ICERI2023 Proceedings (pp. 595–602). IATED. https://doi.org/10.21125/iceri.2023.0212
28. Jarvis, P. (2004). Adult education and lifelong learning: Theory and practice. Third edition. Routledge.
29. Kacperski, C., Ulloa, R., Bonnay, D., Kulshrestha, J., Selb, P., & Spitz, A. (2025). Characteristics of ChatGPT users from Germany: Implications for the digital divide from web tracking data. PloS one, 20(1), e0309047. https://doi.org/10.1371/journal.pone.0309047
30. Kaya, F., Aydin, F., Schepman, A., Rodway, P., Yetisensoy, O & Kaya, M. D. (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/10.1080/10447318.2022.2151730
31. Kumar, K., & Thakur, G. S. M. (2012). Advanced applications of neural networks and artificial intelligence: A review. International journal of information technology and computer science, 4(6), 57–68. https://doi.org/10.5815/ijitcs.2012.06.08
32. Kuo, I. H., Rabindran, J. M., Broadbent, E., Lee, Y. I., Kerse, N., Stafford, R. M., & MacDonald, B. A. (2009, September). Age and gender factors in user acceptance of healthcare robots. In RO-MAN 2009-The 18th IEEE International Symposium on Robot and Human Interactive Communication (pp. 214–219). IEEE. https://ieeexplore.ieee.org/document/5326292
33. Laal, M. (2011). Lifelong learning: What does it mean?. Procedia-Social and Behavioral Sciences, 28, 470–474. https://doi.org/10.1016/j.sbspro.2011.11.090
34. Laugksch, R. C. (2000). Scientific literacy: A conceptual overview. Science education, 84(1), 71–94. https://doi.org/10.1002/(SICI)1098-237X(200001)84:1<71::AID-SCE6>3.0.CO;2-C
35. Laupichler, M. C., Aster, A., Schirch, J., & Raupach, T. (2022). Artificial intelligence literacy in higher and adult education: A scoping literature review. Computers and Education: Artificial Intelligence 3, 100101. https://doi.org/10.1016/j.caeai.2022.100101
36. Luckin, R., Holmes, W., Griffiths, M., & Forcier, L. B. (2016). Intelligence Unleashed: An Argument for AI in Education. Pearson. Retrieved April 18, 2025 from https://static.googleusercontent.com/media/edu.google.com/en//pdfs/Intelligence-Unleashed-Publication.pdf
37. McCarthy, J., Minsky, M., Rochester, N., & Shannon, C. (1955). A proposal for Dartmouth Summer Project on artificial Intelligence. Retrieved April 28, 2025 from http://jmc.stanford.edu/articles/dartmouth/dartmouth.pdf
38. Méndez-Suárez, M., Monfort, A., & Hervas-Oliver, J. L. (2023). Are you adopting artificial intelligence products? Social-demographic factors to explain customer acceptance. European Research on Management and Business Economics, 29(3), 100223. https://doi.org/10.1016/j.iedeen.2023.100223
39. 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
40. Norman, G. (2010). Likert scales, levels of measurement and the “laws” of statistics. Advances in Health Sciences Education, 15(5), 625–632. https://doi.org/10.1007/s10459-010-9222-y
41. OECD (2021). OECD Skills Outlook 2021: Learning for Life. OECD Publishing. https://doi.org/10.1787/0ae365b4-en.
42. OECD (2024). Compendium of Legal Instruments of the OECD Recommendation of the Council on Artificial Intelligence. Retrieved March 22, 2025 from https://legalinstruments.oecd.org/en/instruments/OECD-LEGAL-0449
43. Ong, C. S., & Lai, J. Y. (2006). Gender differences in perceptions and relationships among dominants of e-learning acceptance. Computers in Human Behavior, 22(5), 816–829. https://doi.org/10.1016/j.chb.2004.03.006
44. Pallant, J. (2020). SPSS Survival Manual: A step by step guide to data analysis using IBM SPSS (7th ed.). Routledge. https://doi.org/10.4324/9781003117452
45. Pokrivcakova, S. (2023). Pre-service teachers’ attitudes towards artificial intelligence and its integration into EFL teaching and learning. Journal of Language and Cultural Education, 11(3), 100–114. https://doi.org/10.2478/jolace-2023-0031
46. Potter, W. J. (2018). Media literacy. Sage.
47. Pu, L., Liang, J., Wang, J., Zhang, N., & Zhong, W. (2024). Impact of AI-based learning, digital literacy, information stewardship on learning outcomes. Professional De La información, 33(5). https://doi.org/10.3145/epi.2024.ene.0515
48. Sahin, M., Akbasli, S., & Yelken, T. Y. (2010). Key competences for lifelong learning: The case of prospective teachers. Educational Research and Reviews, 5(10), 545-556. https://doi.org/10.5897/ERR.9000216
49. Samoili, S., Lopez Cobo, M., Delipetrev, B., Martinez-Plumed, F., Gomez Gutierrez, E. and De Prato, G. (2021). AI Watch, defining artificial intelligence 2.0 – Towards an operational definition and taxonomy for the AI landscape. Publications Office of the European Union. https://doi.org/10.2760/019901
50. Schepman, A., & Rodway, P. (2022). 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/10.1080/10447318.2022.2085400
51. Shiri, A. (2024). Artificial intelligence literacy: a proposed faceted taxonomy. Digital Library Perspectives, 40(4), 681–699. https://doi.org/10.7939/r3-j28d-0q76
52. Sindermann, C., Yang, H., Elhai, J. D., Yang, S., Quan, L., Mi, L., & Montag, C. (2020). Acceptance and fear of artificial intelligence: Associations with personality in a German and a Chinese sample. Discover Psychology, 2, 1–12. https://doi.org/10.1007/s44202-022-00020-y
53. Stordy, P. (2015). Taxonomy of literacies, Journal of Documentation, 71(3), 456–476. https://doi.org/10.1108/JD-10-2013-0128
54. Sunthonkanokpong, W., & Murphy, E. (2019). Quality, equity, inclusion and lifelong learning in pre-service teacher education. Journal of Teacher Education for Sustainability, 21(2), 91–104. https://doi.org/10.2478/jtes-2019-0019
55. Tabachnick, B. G., & Fidell, L. S. (2014). Using multivariate statistics (6th ed.). Pearson Education Limited
56. Taber, K. S. (2018). The use of Cronbach’s alpha when developing and reporting research instruments in science education. Research in Science Education, 48(6), 1273-1296. https://doi.org/10.1007/s11165-016-9602-2
57. Türkiye CumhuriyetiCumhurbaşkanlığı Dijital Dönüşüm Ofisi (2025). Dijital Dönüşüm. Retrieved March 22, 2025 from https://cbddo.gov.tr/
58. United Nations (2015). Transforming our World: The 2030 Agenda for Sustainable Development. Retrieved March 25, 2025 from https://sdgs.un.org/publications/transforming-our-world-2030-agenda-sustainable-development-17981
59. UNESCO (2018). UNESCO ICT Competency Framework for Teachers. Retrieved March 22, 2025 from https://unesdoc.unesco.org/ark:/48223/pf0000265721
60. UNESCO Institute for Lifelong Learning. (2022). CONFINTEA VII Marrakech Framework for Action: Harnessing the transformational power of adult learning and education. Retrieved March 22, 2025 from https://unesdoc.unesco.org/ark:/48223/pf0000382306
61. UNESCO (2025). Learning ecosystems. Retrieved March 28, 2025 from https://www.uil.unesco.org/en/learning-ecosystems
62. Ursachi, G., Horodnic, I. A., & Zait, A. (2015). How reliable are measurement scales? External factors with indirect influence on reliability estimators. Procedia economics and finance, 20, 679-686. https://doi.org/10.1016/S2212-5671(15)00123-9
63. Vazhayil, A., Shetty, R., Bhavani, R. R. & Akshay, N. (2019). Focusing on Teacher Education to Introduce AI in Schools: Perspectives and Illustrative Findings, 2019 IEEE Tenth International Conference on Technology for Education (T4E), Goa, India, 2019, pp. 71–77, https://doi.org/10.1109/T4E.2019.00021
64. Venkatesh, V., Thong, J. Y. L., Chan, F. K. Y., Hu, P. J.-H., & Brown, S. A. (2011). Extending the two-stage information systems continuance model: Incorporating UTAUT predictors and the role of context. Information Systems Journal, 21(6), 527–555. https://doi.org/10.1111/j.1365-2575.2011.00373.x
65. Venkatesh, V. (2022). Adoption and use of AI tools: a research agenda grounded in UTAUT. Annals of Operations Research 308, 641–652. https://doi.org/10.1007/s10479-020-03918-9
66. Vislie, L. (2008). Lifelong learning–a new framework for education in an era of globalisation?. Nordic Studies in Education, 28(3), 161–172. https://www.scup.com/doi/10.18261/ISSN1891-5949-2008-03-01
67. Wang, C. H., & Wu, C. L. (2021). Bridging the digital divide: the smart TV as a platform for digital literacy among the elderly. Behavior & Information Technology, 41(12), 2546–2559. https://doi.org/10.1080/0144929X.2021.1934732
68. 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.2022.2072768
69. World Economic Forum (2024). Global Gender Gap Report. Retrieved March 22, 2025 from https://www3.weforum.org/docs/WEF_GGGR_2024.pdf
70. Williams, R., Ali, S., Alcantara, R., Burghleh, T., Alghowinem, S., & Breazeal, C. (2024). Doodlebot: An Educational Robot for Creativity and AI Literacy. In Proceedings of the 2024 ACM/IEEE International Conference on Human-Robot Interaction (HRI '24). Association for Computing Machinery, New York, USA, 772–780. https://doi.org/10.1145/3610977.3634950
71. Xu, L. (2021). The dilemma and countermeasures of AI in educational application. In proceedings of the 2020 4th International Conference on Computer Science and Artificial Intelligence (CSAI '20). Association for Computing Machinery, New York, USA, 289–294. https://doi.org/10.1145/3445815.3445863
72. Yim, I. H. Y. (2024). Artificial intelligence literacy in primary education: An arts-based approach to overcoming age and gender barriers. Computers and Education: Artificial Intelligence, 7, 100321. https://doi.org/10.1016/j.caeai.2024.100321
73. Zhai, X., Chu, X., Chai, C. S., Jong, M. S. Y., Istenic, A., Spector, M., ... & 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
74. Zhai, X. (2024). Transforming Teachers’ Roles and Agencies in the Era of Generative AI: Perceptions, Acceptance, Knowledge, and Practices. Journal of Science Education and Technology. https://doi.org/10.1007/s10956-024-10174-0
75. Zhang, C., Schießl, J., Plößl, L., Hofmann, F. & Gläser-Zikuda, M. (2023). Acceptance of artificial intelligence among pre-service teachers: a multigroup analysis. International Journal of Educational Technology in Higher Education, 20, 49. https://doi.org/10.1186/s41239-023-00420-7