Adoption and Integration of Artificial Intelligence in Foreign Language Teaching: A Multi-Institutional Analysis of Higher Education

Authors

Engin Karadag Özyeğin Üniversity Corresponding Author https://orcid.org/0000-0002-9723-3833
Nihat Çalışkan Ahi Evran Universitesi Author
S. Selin Ciftci MEB Author
Murat Polat Anadolu Üniversitesi Author https://orcid.org/0000-0001-5851-2322

DOI:

https://doi.org/10.59045/nalans.2026.93

Keywords:

Artificial intelligence, Foreign language teaching, AI adoption, Higher education, CALL

Abstract

This study examines the adoption and integration of artificial intelligence (AI) tools in foreign language teaching (FLT) in 13 higher education institutions. A total of 327 faculty members and instructors from the School of Languages participated in a quantitative, cross-sectional survey. A purpose-designed Likert-type instrument was employed to measure four key dimensions: Instructional Integration, Material Development, Assessment and Feedback, Self-Efficacy, and Perceived Barriers. In this study, AI adoption was operationally defined as the self-reported behavioral Integration of AI tools into pedagogical practice, distinguishing it from related constructs such as attitudes, self-efficacy beliefs, and perceived contextual barriers, which were treated as predictors and correlates. The results indicated a lower-mid level of self-reported AI adoption (overall M = 2.41, SD = 0.89), highlighting a significant discrepancy between participants’ awareness of AI tools and their pedagogical implementation. The highest adoption was observed in Material Development (M = 2.80), whereas Assessment and Feedback yielded the lowest scores (M = 2.10). Multiple regression analysis identified a lack of institutional training (β = –.31, p < .001), technostress (β = –.24, p < .01), and perceived threat to professional autonomy (β = –.18, p < .01) as the strongest predictors of low integration. These findings suggest that, despite widespread access to AI technologies, substantive pedagogical Integration in FLT remains constrained by infrastructural, psychological, and epistemological barriers. The implications for institutional policy, professional development, and curriculum design in language education are discussed.

Author Biographies

Engin Karadag, Özyeğin Üniversity

Prof. Dr. Engin Karadağ is a leading scholar in higher education policy, institutional performance, and global university rankings. He is affiliated with Özyeğin University (İstanbul), where he conducts research on internationalization, research evaluation, and data-driven governance in higher education. His work focuses on university rankings (e.g., THE, QS), interdisciplinary research performance, and strategic institutional development.
Nihat Çalışkan, Ahi Evran Universitesi

Prof. Dr. Nihat Çalışkan is a faculty member at Ahi Evran University, where he contributes to academic research and higher education development in his field of expertise. His work focuses on advancing scholarly knowledge, supporting teaching and learning processes, and engaging in academic and institutional projects.
S. Selin Ciftci, MEB

Dr. Şükriye Selin Çiftçi is affiliated with the Ministry of National Education, where she contributes to educational policy, curriculum development, and the improvement of teaching and learning processes. Her work focuses on enhancing educational quality through research-informed practices and supporting the professional development of educators.
Murat Polat, Anadolu Üniversitesi

Assoc. Prof. Dr. Murat Polat is a faculty member at Anadolu University. His academic work focuses on advancing research, teaching, and innovation within his field, with particular emphasis on evidence-based approaches and the integration of contemporary methodologies into higher education.

References

Aydogan, H., & Akbarov, A. A. (2014). The four basic language skills, whole language & integrated skill approach in mainstream university classrooms in Turkey. Mediterranean Journal of Social Sciences, 5(9), 672–680. https://doi.org/10.5901/mjss.2014.v5n9p672

Baidoo-Anu, D., & Ansah, L. O. (2023). Education in the era of generative artificial intelligence (AI): Understanding the potential benefits of ChatGPT in promoting teaching and learning. Journal of AI, 7(1), 52–62. https://doi.org/10.61969/jai.1337500

Borg, S. (2006). Teacher cognition and language education: Research and practice. Continuum.

Brod, C. (1984). Technostress: The human cost of the computer revolution. Addison-Wesley.

Chan, C. K. Y., & Tsi, L. H. Y. (2024). Will generative AI replace teachers in higher education? A study of teacher and student perceptions. Studies in Educational Evaluation, 83, 101395. https://doi.org/10.1016/j.stueduc.2024.101395

Chapelle, C. A. (2009). The relationship between second language acquisition theory and computer-assisted language learning. The Modern Language Journal, 93(s1), 741–753. https://doi.org/10.1111/j.1540-4781.2009.00970.x

Costello, A. B., & Osborne, J. W. (2005). Best practices in exploratory factor analysis: Four recommendations for getting the most from your analysis. Practical Assessment, Research, and Evaluation, 10(7), 1–9. https://doi.org/10.7275/jyj1-4868

Creswell, J. W., & Creswell, J. D. (2018). Research design: Qualitative, quantitative, and mixed methods approaches. SAGE Publications.

Crompton, H., & Burke, D. (2023). Artificial intelligence in higher education: The state of the field. International Journal of Educational Technology in Higher Education, 20(22), 1–22. https://doi.org/10.1186/s41239-023-00392-8

Ertmer, P. A., & Ottenbreit-Leftwich, A. T. (2010). Teacher technology change: How knowledge, confidence, beliefs, and culture intersect. Journal of Research on Technology in Education, 42(3), 255–284. https://doi.org/10.1080/15391523.2010.10782551

Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50. https://doi.org/10.1177/002224378101800104

Godwin-Jones, R. (2022). Partnering with AI: Intelligent writing assistance and instructed language learning. Language Learning & Technology, 26(2), 5–24. https://doi.org/10.64152/10125/73474

Hiver, P., & Dörnyei, Z. (2017). Language teacher immunity: A double-edged sword. Applied Linguistics, 38(3), 405–423. https://doi.org/10.1093/applin/amv034

Howard, S. K. (2013). Risk-aversion: Understanding teachers’ resistance to technology integration. Technology, Pedagogy and Education, 22(3), 357–372. https://doi.org/10.1080/1475939X.2013.802995

Huang, X., Zou, D., Cheng, G., Chen, X., & Xie, H. (2023). Trends, research issues, and applications of artificial intelligence in language education. Educational Technology & Society, 26(1), 112–131. https://doi.org/10.30191/ETS.202301_26(1).0009

Kennedy, C., & Levy, M. (2009). Sustainability and computer-assisted language learning: Factors for success in a context of change. Computer Assisted Language Learning, 22(5), 445–463. https://doi.org/10.1080/09588220903345218

Kohnke, L., Moorhouse, B. L., & Zou, D. (2023). ChatGPT for language teaching and learning. RELC Journal, 54(2), 537–550. https://doi.org/10.1177/00336882231162868

Kumaravadivelu, B. (2006). Understanding language teaching: From method to postmethod. Routledge.

Levy, M., & Stockwell, G. (2006). CALL dimensions: Options and issues in computer-assisted language learning. Routledge.

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

Moorhouse, B. L., Yeo, M. A., & Wan, Y. (2023). Generative AI tools and assessment: Guidelines of the world's top-ranking universities. Computers and Education Open, 5, 100151. https://doi.org/10.1016/j.caeo.2023.100151

Podsakoff, P. M., MacKenzie, S. B., Lee, J.-Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879–903. https://doi.org/10.1037/0021-9010.88.5.879

Puentedura, R. R. (2013). SAMR: Moving from enhancement to transformation. Retrieved from https://www.hippasus.com/rrpweblog/archives/2013/05/29/SAMREnhancementToTransformation.pdf

Rogers, E. M. (2003). Diffusion of innovations. Free Press.

Salanova, M., Llorens, S., & Cifre, E. (2013). The dark side of technologies: Technostress among users of information and communication technologies. International Journal of Psychology, 48(3), 422–436. https://doi.org/10.1080/00207594.2012.680460

Sullivan, M., Kelly, A., & McLaughlan, P. (2023). ChatGPT in higher education: Considerations for academic integrity and student learning. Journal of Applied Learning and Teaching, 6(1), 31–40.

Tarafdar, M., Tu, Q., Ragu-Nathan, B. S., & Ragu-Nathan, T. S. (2007). The impact of technostress on role stress and productivity. Journal of Management Information Systems, 24(1), 301–328. https://doi.org/10.2753/MIS0742-1222240109

Warschauer, M., & Healey, D. (1998). Computers and language learning: An overview. Language Teaching, 31(2), 57–71. https://doi.org/10.1017/S0261444800012970

Weijters, B., Baumgartner, H., & Schillewaert, N. (2013). Reversed item bias: An integrative model. Psychological Methods, 18(3), 320–334. https://doi.org/10.1037/a0032121

Zawacki-Richter, O., Marín, 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/s41239-019-0171-0

Downloads

Published

2026-05-30

Issue

Vol. 14 No. 29 | Special Issue (2026): Narrative, Education and Learning in the Age of Artificial Intelligence

Section

Articles

License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

How to Cite

Karadag, E., Çalışkan, N., Ciftci, S. S., & Polat, M. (2026). Adoption and Integration of Artificial Intelligence in Foreign Language Teaching: A Multi-Institutional Analysis of Higher Education. Journal of Narrative and Language Studies, 14(29 | Special Issue), 46-57. https://doi.org/10.59045/nalans.2026.93

Download Citation