Exploring the Relationship between AI Competency and Professional Identity in Language Education: A Mixed-methods Study

Abstract

The integration of artifi cial intelligence (AI) in education is increasingly prevalent; however, the implications of AI competency forprofessional identity remain underexplored. Building on Sociocultural Theory and Possible Selves Theory, this convergent mixed-methods study examines the relationship between AI competency and instructor characteristics with professional identity among 111Turkish EFL instructors. Validated scales measuring AI competency and professional identity were administered alongside semi-structured interviews with 12 participants. Quantitative analyses revealed that AI competency signifi cantly predicts professionalidentity (β = .295, p = .002). Prior AI exposure markedly diff erentiated competency levels, while age and teaching experience had nosignifi cant eff ect. Qualitative fi ndings identifi ed three identity trajectories (strengthening, neutrality, and questioning) shaped by beliefs,emotions, institutional support, and instructional experience. We propose a novel interpretive mapping of these trajectories onto thehoped-for, expected, and feared dimensions of professional self-representation, off ering a theoretically parsimonious lens forunderstanding why instructors with equivalent AI competency levels exhibit divergent identity responses. The study highlights theimportance of professional development that goes beyond technical training to include guided, refl ective, and emotionally resonantexploration connecting AI skills with pedagogical values and ethical awareness.

Keywords

• AI competency
• teacher professional identity
• Possible Selves Theory
• Sociocultural Theory
• mixed methods
• EFL education
• Turkish higher education

Supporting Institution

REPUBLIC OF TÜRKIYE ATILIM UNIVERSITY RECTORATE Human Research Ethics Committee

Project Number

E-59394181-604.01-100557

Ethical Statement

Your scientific research project proposal entitled "Examining the Relationships Between Artificial Intelligence Competency, Self-Efficacy, and Teacher Identity: A Sequential Explanatory Mixed-Methods Study" has been reviewed and approved by the members of the Atılım University Human Research Ethics Committee. Yours sincerely, Prof. Dr. Nurhan BAYRAKTAR Chairperson

References

1. Bakhadirov, M., & Alasgarova, R. (2024). Factors influencing teachers' use of artificial intelligence for instructional purposes. IAFOR Journal of Education, 12(2), 9–32. https://doi.org/10.22492/ije.12.2.01
2. Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory. Prentice-Hall.
3. Barjesteh, H., Rezaee, A. A., & Nasiri, M. (2026). From skill acquisition to professional agency: Rethinking EFL teachers' professional development in the age of AI. Indonesian Journal of Pedagogy and Teacher Education, 5(1), 1–18. https://doi.org/10.58723/ijopate.v4i1.598
4. Bogoslov, I., Corman, S., & Lungu, A. (2024). Perspectives on artificial intelligence adoption for European Union elderly in the context of digital skills development. Sustainability, 16(11), 4579. https://doi.org/10.3390/su16114579
5. Cabero-Almenara, J., Palacios-Rodríguez, A., Loaiza-Aguirre, M., & Rivas-Manzano, M. (2024). Acceptance of educational artificial intelligence by teachers and its relationship with some variables and pedagogical beliefs. Education Sciences, 14(7), 740. https://doi.org/10.3390/educsci14070740
6. Celik, I. (2023). Towards Intelligent-TPACK: An empirical study on teachers' professional knowledge to ethically integrate artificial intelligence (AI)-based tools into education. Computers in Human Behavior, 138, 107468. https://doi.org/10.1016/j.chb.2022.107468
7. Çelik Demirci, S., Besalti, M., & Kul, Ü. (2026). Psychometric validation of the Turkish brief general AI self-efficacy scale: Eexamining its role in AI anxiety, acceptance, and demographic predictors. Frontiers in Psychology, 17, 1714147. https://doi.org/10.3389/fpsyg.2026.1714147
8. Chan, K., & Tang, W. (2024). Evaluating English teachers' artificial intelligence readiness and training needs with a TPACK-based model. World Journal of English Language, 15(1), 129. https://doi.org/10.5430/wjel.v15n1p129

9. Choi, S., Jang, Y., & Kim, H. (2023). Influence of pedagogical beliefs and perceived trust on teachers' acceptance of educational artificial intelligence tools. International Journal of Human–Computer Interaction,39(4), 910–922. https://doi.org/10.1080/10447318.2022.2049145
10. Collie, R., Martin, A., & Gasevic, D. (2024). Teachers' generative AI self-efficacy, valuing, and integration at work. Computers and Education: Artificial Intelligence, 7, 100333. https://doi.org/10.1016/j.caeai.2024.100333
11. Creswell, J., & Plano Clark, V. (2018). Designing and conducting mixed methods research. SAGE.
12. Davies, B., & Harré, R. (1990). Positioning: The discursive production of selves. Journal for the Theory of Social Behaviour, 20(1), 43–63. https://doi.org/10.1111/j.1468-5914.1990.tb00174.x
13. Ding, A., Shi, L., Yang, H., & Choi, I. (2024). Enhancing teacher AI literacy and integration through different types of cases in teacher professional development. Computers and Education Open, 6, 100178. https://doi.org/10.1016/j.caeo.2024.100178
14. Dong, S., & Aziz, S. (2024). Identity formation and self-efficacy: An examination of pre-service physical education teachers. International Journal of Academic Research in Progressive Education and Development, 13(3), 2190–2198.
15. Dörnyei, Z. (2005). The psychology of the language learner: Individual differences in second language acquisition. Lawrence Erlbaum.
16. Duan, H., & Zhao, W. (2024). The effects of educational artificial intelligence-powered applications on teachers' perceived autonomy, professional development, and digital burnout. The International Review of Research in Open and Distributed Learning, 25(3), 57–76. https://doi.org/10.19173/irrodl.v25i3.7659
17. El-Sayed, B., Alsenany, S., & Asal, M. (2025). The role of artificial intelligence literacy and innovation mindset in shaping nursing students' career and talent self-efficacy. Nurse Education in Practice, 82, 104208. https://doi.org/10.1016/j.nepr.2024.104208
18. Engeström, Y. (1987). Learning by expanding: An activity-theoretical approach to developmental research. Orienta-Konsultit.
19. Falloon, G. (2020). From digital literacy to digital competence: The teacher digital competency (TDC) framework. Educational Technology Research and Development, 68(5), 2449–2472. https://doi.org/10.1007/s11423-020-09767-4
20. Flogie, A., & Krabonja, M. (2023). Artificial intelligence in education: Developing competencies and supporting teachers. 2023 12th Mediterranean Conference on Embedded Computing (MECO), 1–6. https://doi.org/10.1109/MECO58584.2023.10155054
21. Gil-Flores, J. (2017). The role of personal characteristics and school characteristics in explaining teacher job satisfaction. Revista de Psicodidáctica, 22(1), 16–22. https://doi.org/10.1387/RevPsicodidact.15501
22. Guetterman, T., Fetters, M., & Creswell, J. (2015). Integrating quantitative and qualitative results in health science mixed methods research through joint displays. The Annals of Family Medicine, 13(6), 554–561. https://doi.org/10.1370/afm.1865
23. Hannon, O., Ciriello, R., & Gal, U. (2024). Just because we can, doesn't mean we should: Algorithm aversion as a principled resistance. Hawaii International Conference on System Sciences. https://doi.org/10.24251/HICSS.2024.730
24. Heitink, M., Voogt, J., Fisser, P., Verplanken, L., & Van Braak, J. (2017). Eliciting teachers' technological pedagogical knowledge. Australasian Journal of Educational Technology, 33(3). https://doi.org/10.14742/ajet.3505
25. Holland, D., Lachicotte, W., Skinner, D., & Cain, C. (1998). Identity and agency in cultural worlds. Harvard University Press.
26. Isop, W. A. (2025). A conceptual ethical framework to preserve natural human presence in the use of AI systems in education. Frontiers in Artificial Intelligence, 7, 1377938. https://doi.org/10.3389/frai.2024.1377938
27. Kitcharoen, P., Howimanporn, S., & Chookaew, S. (2024). Enhancing teachers' AI competencies through artificial intelligence of things professional development training. International Journal of Interactive Mobile Technologies, 18(02), 4–15. https://doi.org/10.3991/ijim.v18i02.46613
28. Kurt, B., Arık Karamık, G., & Özkaya, A. (2026). Investigating the correlation between candidate teachers' acceptance of generative artificial intelligence and artificial intelligence literacy across various disciplines. PLOS ONE, 21(3), e0342853. https://doi.org/10.1371/journal.pone.0342853
29. Lan, Y. (2024). Through tensions to identity-based motivations: Exploring teacher professional identity in artificial intelligence-enhanced teacher training. Teaching and Teacher Education, 151, 104736. https://doi.org/10.1016/j.tate.2024.104736 Lave, J., & Wenger, E. (1991). Situated learning: Legitimate peripheral participation. Cambridge University Press.
30. Lee, H., & Bryan, L. (2024). The impact of AI-based learning environments on preservice teacher competencies. In Proceedings of SITE 2024 (pp. 97–101).
31. Lincoln, Y. S., & Guba, E. G. (1985). Naturalistic inquiry. SAGE.
32. Liu, X., & Li, Y. (2022). Redefining teacher qualification in the artificial intelligence era: A professional capital perspective. Proceedings of the 5th International Conference on Big Data and Education, 35–39.
33. Lu, J., Zheng, R., Gong, Z., & Xu, H. (2024). Supporting teachers' professional development with generative AI. IEEE Transactions on Learning Technologies, 17, 1267–1277. https://doi.org/10.1109/TLT.2024.3369690
34. Luo, H., & Han, B. (2024). Research on teaching competence and improvement path of university teachers in 5G+AI era. Advances in Educational Technology and Psychology, 8(2). https://dx.doi.org/10.23977/aetp.2024.080213
35. Marisa, R., Rachmajanti, S., & Anugerahwati, M. (2024). Investigating the digital proficiency of EFL educators in association with their professional identity. The JALT CALL Journal, 20(2), 1–31. https://doi.org/10.29140/jaltcall.v20n2.1315
36. Markauskaite, L., Marrone, R. L., Poquet, O., Knight, S., Martinez-Maldonado, R., Howard, S., Tondeur, J., de Laat, M., Buckingham Shum, S., Gašević, D., & Siemens, G. (2022). Rethinking the entwinement between artificial intelligence and human learning. Computers and Education: Artificial Intelligence, 3, 100056. https://doi.org/10.1016/j.caeai.2022.100056
37. Markus, H., & Nurius, P. (1986). Possible selves. American Psychologist, 41(9), 954–969. https://psycnet.apa.org/doi/10.1037/0003-066X.41.9.954
38. Masry Herzallah, A., & Makaldy, R. (2025). Technological self-efficacy and sense of coherence: Key drivers in teachers' AI acceptance and adoption. Computers and Education: Artificial Intelligence, 8, 100377. https://doi.org/10.1016/j.caeai.2025.100377
39. Miles, M. B., Huberman, A. M., & Saldaña, J. (2020). Qualitative data analysis: A methods sourcebook (4th ed.). SAGE.
40. Mishra, P., & Koehler, M. J. (2006). Technological pedagogical content knowledge: A framework for teacher knowledge. Teachers College Record, 108(6), 1017–1054. https://doi.org/10.1111/j.1467-9620.2006.00684.x
41. Moorhouse, B. (2024). Beginning and first-year language teachers' readiness for the generative AI age. Computers and Education: Artificial Intelligence, 6, 100201. https://doi.org/10.1016/j.caeai.2024.100201
42. Morris, T. H. (2019). Self-directed learning: A fundamental competence in a rapidly changing world. International Review of Education, 65(4), 633–653. https://doi.org/10.1007/s11159-019-09793-2
43. Ng, D., Leung, J., Su, J., Ng, R., & Chu, S. (2023). Teachers' AI digital competencies and twenty-first century skills in the post-pandemic world. Educational Technology Research and Development, 71(1), 137–161. https://doi.org/10.1007/s11423-023-10203-6
44. Nguyen, T. C. (2024). University teachers' perceptions of using ChatGPT in language teaching and assessment. Proceedings of the AsiaCALL International Conference, 4, 116–128. https://doi.org/10.54855/paic.2349
45. Novawan, A., Walker, S., & Ikeda, O. (2024). The new face of technology-enhanced language learning with AI. Journal of English in Academic and Professional Communication, 10(1), 1–18. https://publikasi.polije.ac.id/jeapco/article/view/4565/2458
46. Oran, B. (2023). Correlation between artificial intelligence in education and teacher self-efficacy beliefs. RumeliDE Dil ve Edebiyat Araştırmaları Dergisi, 34, 1354–1365. https://doi.org/10.29000/rumelide.1316378
47. Ost-Mor, S., Wittenberg, N., & Birati, Y. (2024). Exploring artificial intelligence usage, personality and other characteristics in older adults. Innovation in Aging, 8, 1319. https://doi.org/10.1093/geroni/igae098.4213
48. Pan, Z., & Wang, Y. (2025). From technology-challenged teachers to empowered digitalized citizens: Exploring profiles and antecedents of teacher AI literacy in the Chinese EFL context. European Journal of Education, 60(1), e70020. https://doi.org/10.1111/ejed.70020
49. Prensky, M. (2001). Digital natives, digital immigrants. On the Horizon, 9(5), 1–6.
50. Satvati, N., Kamali, J., Safian Boldaji, F., Khodadadi, M., & Akhondi, S. (2023). Exploring the role of AI integration into language education in language teacher identity construction: An ecological perspective. SSRN. https://doi.org/10.2139/ssrn.4670840
51. Soomro, K., Kale, U., Curtis, R., Akcaoglu, M., & Bernstein, M. (2020). Digital divide among higher education faculty. International Journal of Educational Technology in Higher Education, 17(1), 21. https://doi.org/10.1186/s41239-020-00191-5
52. Sun, J., Ma, H., Zeng, Y., Han, D., & Jin, Y. (2023). Promoting the AI teaching competency of K-12 computer science teachers. Education and Information Technologies, 28(2), 1509–1533. https://doi.org/10.1007/s10639-022-11256-5
53. Swindell, A., Greeley, L., Farag, A., & Verdone, B. (2024). Against artificial education: Towards an ethical framework for generative AI use in education. Online Learning, 28(2). https://doi.org/10.24059/olj.v28i2.4438
54. Tashakkori, A., Johnson, B., & Teddlie, C. (2021). Foundations of mixed methods research (2nd ed.). SAGE. https://doi.org/10.4135/9781506350288
55. Tin, T. T. (2024). Demographic factors shaping AI perspectives: Exploring their impact on university students' academic performance. Pakistan Journal of Life and Social Sciences, 22(2). https://doi.org/10.57239/PJLSS-2024-22.2.000876
56. Trujillo-Cabezas, R. (2020). Towards the development of future trend scenarios through dynamic analysis. 2020 15th Iberian Conference on Information Systems and Technologies (CISTI), 1–6. https://doi.org/10.23919/CISTI49556.2020.9140880
57. van Langenhove, L., & Harré, R. (1999). Introducing positioning theory. In R. Harré & L. van Langenhove (Eds.), Positioning theory (pp. 14–31). Blackwell.
58. Vygotsky, L. S. (1978). Mind in society: The development of higher psychological processes. Harvard University Press.
59. Wang, M., & Yu, L. (2024). Digitalization's impact on Chinese higher vocational teachers: Identity, satisfaction, resignation. Journal of Information Systems Engineering and Management, 9(4). https://doi.org/10.55267/iadt.07.15438
60. Wertsch, J. V. (1991). Voices of the mind: A sociocultural approach to mediated action. Harvard University Press.
61. Wu, T., Lee, H., Li, P., Huang, C., & Huang, Y. (2024). Promoting self-regulation progress and knowledge construction via ChatGPT-based learning aid. Journal of Educational Computing Research, 61(8), 3–31. https://doi.org/10.1177/07356331231191125
62. Yan, Y., Bava Harji, M., & Kannan, S. (2024). English teacher identity measure: Development and validation in a Chinese EFL context. Cogent Education, 11(1). https://doi.org/10.1080/2331186X.2023.2293983
63. Yang, F., Kangxi, L., & Ruilin, L. (2024). AI in language education: Enhancing learners' speaking awareness through AI-supported training. International Journal of Information and Education Technology, 14(6), 828–833. https://doi.org/10.18178/ijiet.2024.14.6.2108
64. Yang, Y., Xia, Q., Liu, C., & Chiu, T. K. (2025). The impact of TPACK on teachers' willingness to integrate generative AI. Teaching and Teacher Education, 154. https://doi.org/10.1016/j.tate.2024.104877
65. Yuliya, S. (2023). Teachers as digital immigrants: Does hybrid identity facilitate integration in the digital world? SCIREA Journal of Education. https://doi.org/10.54647/education880431
66. Zhang, H., Ibrahim, N. A., & Abd. Wahid, N. S. (2026). Artificial intelligence in language teaching: Teachers’ roles, identity, and professional development: A systematic literature review. Malaysian Journal of ELT Research, 23(1). https://doi.org/10.52696/RMIC4694
67. Zhou, C., & Hou, F. (2024). Can AI empower L2 education? Exploring its influence on behavioural, cognitive and emotional engagement of EFL teachers. European Journal of Education, 59: e12750. https://doi.org/10.1111/ejed.12750
68. Zhou, Q., Hashim, H., & Sulaiman, N. A. (2024). Digital echoes: Crafting Chinese EFL teacher identity in the era of AI-enhanced instruction. Forum for Linguistic Studies, 6(5), 32–50. https://doi.org/10.30564/fls.v6i5.6864