Keywords
Professional Identity Conflict
Assessment
Academic Integrity
AI-human collaboration
How to Cite
Abstract
Background: The rapid integration of generative artificial intelligence (AI) into higher education is transforming assessment practices and creating new challenges for EFL teachers. As generative AI becomes increasingly embedded in students’ academic tasks, EFL teachers face responsibility for identifying AI-generated content and maintaining academic integrity, thereby potentially shifting their role from facilitators toward “detectives” and contributing to identity conflict.
Objective: This study examines how generative AI in assessment reshapes EFL teachers’ professional roles and identities, focusing on responsibility, cognitive workload, identity conflict, institutional ambiguity, and ethical concerns.
Methods: Adopting an interpretivist paradigm, the study involved 15 EFL teachers. Data were collected through semi-structured interviews and analyzed thematically following Braun and Clarke’s (2006) framework. An abductive approach guided analysis through a four-dimensional conceptual model. Network analysis used Jaccard similarity and phi coefficients to examine thematic co-occurrence and association strength.
Results: The findings show that AI is reshaping assessment and teacher roles through increased responsibility, cognitive workload, and identity conflict. Institutional ambiguity regarding AI policies was highly prevalent in co-occurrence, however, its relatively limited structural connectivity within the thematic network suggested that institutional ambiguity functioned primarily as a contextual condition shaping teachers’ experiences rather than as a central organizing factor. Stronger structural dynamics involved frustration, loss of control, demotivation, cognitive workload, and ethical–religious concerns regarding integrity, fairness, and guilt.
Conclusion: The findings highlight the need to move beyond detection-oriented responses toward institutional approaches supporting AI literacy, assessment reform, and human–AI collaboration. Rather than positioning teachers primarily as AI detectors, institutions should support them in using AI as a collaborative thinking partner while preserving human judgment, creativity, critical engagement, and pedagogical responsibility.
References
Akram, A., et al. (2023). An empirical study of AI-generated text detection tools. Advances in Machine Learning & Artificial Intelligence, 4(2), 1–15. https://doi.org/10.48550/arXiv.2310.01423
Alexander, K., Savvidou, C., & Alexander, C. (2023). Who wrote this essay? Detecting AI-generated writing in second language education in higher education. Teaching English with Technology, 23(2), 25–43. https://doi.org/10.56297/BUKA4060/XHLD5365
Asadi, M., Ebadi, S., & Mohammadi, L. (2025). The impact of integrating ChatGPT with teachers’ feedback on EFL writing skills. Thinking Skills and Creativity, 56 (101766). https://doi.org/10.1016/j.tsc.2025.101766
Avidov- Ungar, O., & Forkosh-Baruch, A. (2018). Professional identity of teacher educators in the digital era in light of demands of pedagogical innovation. Teaching and Teacher Education, 73, 183–191. https://doi.org/10.1016/j.tate.2018.03.017.
Benarab, I. H. (2024). Detection of AI-Generated Writing in Students’ Assignments: A Comparative Analysis of Some Tools’ Reliability. Atras Journal, 5 (Special Issue), 271-286. https://doi.org/10.70091/atras/AI.17
Biddle, B. J. (1986). Recent developments in role theory. Annual Review of Sociology, 12, 67–92. https://doi.org/10.1146/annurev.so.12.080186.000435
Bower, M., Torrington, J., Lai, J. W. M., Petocz, P., & Alfano, M. (2024). How should we change teaching and assessment in response to increasingly powerful generative artificial intelligence? Outcomes of the ChatGPT teacher survey. Education and Information Technologies, 29, 15403–15439. https://doi.org/10.1007/s10639-023-12405-0
Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101. https://doi.org/10.1191/1478088706qp063oa
Braun, V., & Clarke, V. (2019). Reflecting on reflexive thematic analysis. Qualitative Research in Sport, Exercise and Health, 11(4), 589–597. https://doi.org/10.1080/2159676X.2019.1628806
Braun, V., & Clarke, V. (2021). One size fits all? What counts as quality practice in (reflexive) thematic analysis? Qualitative Research in Psychology, 18(3), 328–352. https://doi.org/10.1080/14780887.2020.1769238
Braun, V., & Clarke, V. (2022). Conceptual and design thinking for thematic analysis. Qualitative Psychology, 9(1), 3–26.https://doi.org/10.1037/qup0000196
Byrne, D. A. (2022). A worked example of Braun and Clarke’s approach to reflexive thematic analysis. Quality & Quantity, 56, 1391–1412. https://doi.org/10.1007/s11135-021-01182-y
Chaka, C. (2024). Reviewing the performance of AI detection tools in differentiating between AI-generated and human-written texts: A literature and integrative hybrid review. Journal of Applied Learning & Teaching, 7(1), 115-126.
Chan, C. K., & Tsi, L. H. (2024). Will generative AI replace teachers in higher education? A study of teacher and student perceptions. Studies in Educational Evaluation, 83 (101367), 1-11. https://doi.org/10.1016/j.stueduc.2024.101395.
Dasgupta, A., Ghosh, D., McCullers, A., Williams, B. R., Clark, E. M., Schootman, M., Park, C. L., & Knott, C. L. (2026). Integrating artificial intelligence with manual coding of qualitative data: Opportunities and challenges. The Professional Geographer, 78(3), 215–225. https://doi.org/10.1080/00330124.2026.2636573
Davey, R. (2013). The Professional Identity of Teacher Educators: Career on the cusp? (1st ed.). Routledge. https://doi.org/10.4324/9780203584934
Dellermann, D., Ebel, P., Söllner, M., & Leimeister, J. M. (2019). Hybrid intelligence. Business & Information Systems Engineering, 61, 637–643. https://doi.org/10.1007/s12599-019-00595-2
Demerouti, E., Bakker, A. B., Nachreiner, F., & Schaufeli, W. B. (2001). The job demands–resources model of burnout. Journal of Applied Psychology, 86(3), 499–512. https://doi.org/10.1037/0021-9010.86.3.499
Dewey. J. (1938). Logic: The theory of inquiry. Henry Holt and Company.
Field, A. (2018). Discovering Statistics Using IBM SPSS Statistics (5th ed.). Sage.
Geertshuis, S., & Liu, Q. (2022). The challenges we face: A professional identity analysis of learning technology implementation. Innovations in Education and Teaching International, 59(2), 205-215. https://doi.org/10.1080/14703297.2020.1832904
Goundar, P. R. (2025). Researcher Positionality: Ways to Include it in a Qualitative Research Design. International Journal of Qualitative Methods, 24. https://doi.org/10.1177/16094069251321251
Hennink, M., & Kaiser, B. N. (2022). Sample sizes for saturation in qualitative research: A systematic review of empirical tests. Social Science & Medicine, 292(114523), 1-10. https://doi.org/10.1016/j.socscimed.2021.114523.
Karousiou, C., Hajisoteriou, C., & Angelides, P. (2019). Teachers’ professional identity in super-diverse school settings: teachers as agents of intercultural education. Teachers and Teaching, 25(2), 240–258. https://doi.org/10.1080/13540602.2018.1544121
Khlaif, Z. N., Ayyoub, A., Hamamra, B., Bensalem, E., Mitwally, M. A. A., Ayyoub, A., Hattab, M. K., & Shadid, F. (2024). University Teachers’ views on the adoption and integration of generative ai tools for student assessment in higher education. Education Sciences, 14(10), 1090. https://doi.org/10.3390/educsci14101090
Koeneman, S. H., & Cavanaugh, J. E. (2022). An improved asymptotic test for the Jaccard similarity index for binary data. Statistics & Probability Letters, 184, 109375. https://doi.org/10.1016/j.spl.2022.109375
Kumar, R., Eaton, S.E., Mindzak, M., Morrison, R. (2023). Academic Integrity and Artificial Intelligence: An Overview. In: Eaton, S.E. (eds) Handbook of Academic Integrity. Springer, Singapore. https://doi.org/10.1007/978-981-287-079-7_153-1
Lai, C., & Jin, G. (2021). Teacher professional identity and the nature of technology integration. Computers & Education, 175 (104314), 1-11. https://doi.org/10.1016/j.compedu.2021.104314.
Lee, D., Arnold, M., Srivastava, A., Plastow, K., Strelan, P., Ploeckl, F., Lekkas, D., & Palmer, E. (2024). The impact of generative AI on higher education learning and teaching: A study of educators’ perspectives. Computers and Education: Artificial Intelligence, 6 (100221), 1-10. https://doi.org/10.1016/j.caeai.2024.100221
Lim, W. M. (2026). Sample size in qualitative research: Moving from data saturation to theoretical saturation. Journal of Global Marketing, 39(2), 224–234. https://doi.org/10.1080/08911762.2025.2590757
Maguire, M., & Delahunt, B. (2017). Doing a thematic analysis: A practical, step-by-step guide for learning and teaching scholars. AISHE-J: All Ireland Journal of Teaching and Learning in Higher Education, 8(3), 3351–3359.
Mayer, R. C., Davis, J. H., & Schoorman, F. D. (1995). An integrative model of organizational trust. The Academy of Management Review, 20(3), 709–734. https://doi.org/10.2307/258792
McDougall, J. (2010). A crisis of professional identity: How primary teachers are coming to terms with changing views of literacy. Teaching and Teacher Education, 26(3), 679–687.
https://doi.org/10.1016/j.tate.2009.10.003.
Merton, R. K. (1957). The role-set: Problems in sociological theory. The British Journal of Sociology, 8(2), 106–120. Merton, R. K. (1957). https://doi.org/10.2307/587363
Ministère de l’Enseignement Supérieur et de la Recherche Scientifique. (2025). Statistiques du secteur de l’enseignement supérieur et de la recherche scientifique 2024–2025. https://www.mesrs.dz/agregats-ar-2024-2025-2/?
Ministry of Higher Education and Scientific Research. (2024, July 4). Global Conference for the Governance of Artificial Intelligence for Good and for All. https://www.mesrs.dz/en/2024/07/global-conference-for-the-governance-of-artificial-intelligence-for-good-and-for-all/
Ministry of Higher Education and Scientific Research. (2026). LMD system and pedagogical organization. https://circulaire.mesrs.dz/lmd?
Mockler, N. (2019). Navigating professional identity as a teacher of history (Chapter 23). In T. Allender, A. Clark, & R. Parkes (Eds.), Historical thinking for history teachers: A new approach to engaging students and developing historical consciousness (p. 14). Routledge. https://doi.org/10.4324/9781003115977
Nguyen, L., & Barrot, J. S. (2024). Detecting and assessing AI-generated and human-produced texts: The case of second language writing teachers. Assessing Writing, 62 (100870), 1-12. https://doi.org/10.1016/j.asw.2024.100899.
Padillah, R. (2024). Ghostwriting: A reflection of academic dishonesty in the artificial intelligence era. Journal of Public Health, 46(1), e193–e194. https://doi.org/10.1093/pubmed/fdad169
Phan, A. N. Q., & Le, C. (2026). AI as research partner: Key implications of using AI for data visualisation in qualitative research. International Journal of Social Research Methodology, 29(2), 313–320. https://doi.org/10.1080/13645579.2025.2518562
Pillen, M., Beijaard, D., & den Brok, P. (2013). Professional identity tensions of beginning teachers. Teachers and Teaching, 19(6), 660–678. https://doi.org/10.1080/13540602.2013.827455
Rodrigues, F., & Mogarro, M. J. (2019). Student teachers’ professional identity: A review of research contributions. Educational Review, 28 (100286), 1-9. https://doi.org/10.1016/j.edurev.2019.100286.
Rodrigues, M., Silva, R., Borges, A. P., Franco, M., & Oliveira, C. (2025). Artificial intelligence: Threat or asset to academic integrity? A bibliometric analysis. Kybernetes, 54(5), 2939–2970. https://doi.org/10.1108/K-09-2023-1666
Sachs, J. (2005). Teacher education and the development of professional identity: Learning to be a teacher. (Chapter1). In P. M. Denicolo & M. Kompf (Eds.), Connecting policy and practice: Challenges for teaching and learning in schools and universities (pp. 5–21). Routledge. https://doi.org/10.4324/9780203012529
Suarez, V. and J. McGrath (2022), “Teacher professional identity: How to develop and support it in times of change”, OECD Education Working Papers, No. 267, OECD Publishing, Paris. https://doi.org/10.1787/b19f5af7-en.
Sullivan, M. (2025). Collusion, plagiarism, or contract cheating? How generative AI fits into existing academic integrity policies. In Handbook of Artificial Intelligence in Higher Education (pp. 1–15).
Cheltenham, UK: Edward Elgar Publishing. Retrieved May 15, 2026, from https://doi.org/10.4337/9781035338764.00033
Sweller, J. (1988), Cognitive Load During Problem Solving: Effects on Learning. Cognitive Science, 12, 257-285. https://doi.org/10.1207/s15516709cog1202_4
Tschannen-Moran, M., & Hoy, W. K. (2000). A multidisciplinary analysis of the nature, meaning, and measurement of trust. Review of Educational Research, 70(4), 547–593. https://doi.org/10.3102/00346543070004547
Uzun, L. (2023) ChatGPT and Academic Integrity Concerns: Detecting Artificial Intelligence-Generated Content. Language Education and Technology, 3(1), 45-54. http://www.langedutech.com/letjournal/index.php/let/article/view/49
Waked, A. N., Abdelsalam, H., Ashraf, M. W., Pilotti, M., & El Alaoui, K. (2024). The impact of falsely detecting AI-generated text on academic assessment. In M. Shelley & O. T. Ozturk (Eds.), Proceedings of ICRES 2024—International Conference on Research in Education and Science (pp. 573–586). https://eric.ed.gov/?id=ED673127
Wilson, A., & Burleigh, C. (2026). Generative Artificial Intelligence as a Collaborative Thought Partner: Rethinking Cognitive Partnership, Creativity, and Knowledge Work in the Age of Intelligent Systems. International Journal of AI in Pedagogy, Innovation, and Learning Futures, 1(1), 1-20. https://doi.org/10.46787/ijaipil.v2026i1.6963
Xia, Q., Weng, X., Ouyang, F., Lin, T., & Chiu, T. (2024). A scoping review on how generative artificial intelligence transforms assessment in higher education. International Journal of Educational Technology in Higher Education, 21(40), 1–22. https://doi.org/10.1186/s41239-024-00468-z
You, Y., & Zhang, Y. (2025). Developing AI literacies and negotiating professional identities: A study of pre-service English teachers in a ChatGPT-facilitated pedagogy. Journal of China Computer-Assisted Language Learning, 5(2), 260–286. https://doi.org/10.1515/jccall-2025-0007
Yung, R., Moyle, B., & Yang, E. C. L. (2025). Mitigating the impact of ChatGPT: Cultivating professional identity through interactive oral authentic assessments. Journal of Hospitality & Tourism Education, 37(4), 488–494. https://doi.org/10.1080/10963758.2024.2331516
Zhang, J. (2025). ChatGPT or “CheatGPT”? EFL teachers’ perceptions of scoring AI-assisted English writing through the lens of assessment literacy. Digital Applied Linguistics, 2(1), 1–17. https://doi.org/10.29140/dal.v2.102486
Zhao, H. & Zhang, X. (2017). The Influence of Field Teaching Practice on Pre-service Teachers’ Professional Identity: A Mixed Methods Study. Frontiers in psychology, 8(1264), 1-11. https://doi.org/10.3389/fpsyg.2017.01264