AI in Iranian higher education: A mixed-methods study of ethical tensions and L2 learning challenges

Hossein Isaee

doi:10.59400/fes3881

AI in Iranian higher education: A mixed-methods study of ethical tensions and L2 learning challenges

Hossein Isaee
Department of English Language, Am.C., Islamic Azad University, Amol 46134, Iran

Article ID: 3881

Keywords: academic integrity, AI literacy, artificial intelligence; ethics, EFL teaching and learning, equity, higher education, mixed-methods research

Abstract

This mixed-methods study examines how artificial intelligence (AI) technologies are reshaping English as a Foreign Language (EFL) learning and teaching within Iranian higher education. Drawing on survey data from 46 students and semi-structured interviews with six EFL instructors and six students, the study explores (1) the extent of students’ AI use, (2) their motivations for using AI, (3) instructors’ attitudes toward AI integration, and (4) perceived risks related to ethics, equity, privacy, and academic integrity. Descriptive findings indicate that more than 85% of students regularly use AI tools (most commonly ChatGPT, Bard, Quillbot, and Grammarly) for idea generation, language refinement, task structuring, and grade maximisation. Interview data demonstrated a clear “assisted learning” orientation among students but also highlighted ethically problematic practices involving plagiarism, overreliance, and strategic paraphrasing to avoid detection. Instructors expressed marked scepticism, with the majority viewing AI as a catalyst for academic dishonesty and a threat to creativity, deep learning, and assessment validity. Despite recognising AI’s potential benefits, both students and instructors voiced substantive concerns regarding data privacy, job displacement, inequity, and algorithmic bias. Collectively, the findings reveal a complex landscape in which AI is simultaneously valued, distrusted, and inadequately regulated. The study concludes by outlining implications for ethical governance, AI literacy integration, and the development of context-sensitive EFL pedagogies in Iranian universities.

Published

2026-01-22

How to Cite

Isaee, H. (2026). AI in Iranian higher education: A mixed-methods study of ethical tensions and L2 learning challenges. Forum for Education Studies, 4(1). https://doi.org/10.59400/fes3881

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Vol. 4 No. 1 (2026): In Progress

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Article

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

References

[1]Isaee H. Iranian EFL instructors’ perspectives on integrating artificial intelligence applications into English language teaching and learning. Journal of AI in ELT and Applied Linguistics. 2026; 2(1): 14–24.

[2]Available online: https://www.researchgate.net/profile/Hossein-Isaee/publication/401263954_Iranian_EFL_Instructors'_Perspectives_on_Integrating_Artificial_Intelligence_Applications_into_English_Language_Teaching_and_Learning/links/69a1dc5016faea00ba9a3ef5/Iranian-EFL-Instructors-Perspectives-on-Integrating-Artificial-Intelligence-Applications-into-English-Language-Teaching-and-Learning.pdf

[3]Isaee H, Barjesteh H. Exploring teachers’ and learners’ perceptions of AI-supported pedagogical tools in English language teaching. Discover Artificial Intelligence. 2026; 6(1): 224. doi: 10.1007/s44163-026-00933-w

[4]Barjesteh H, Isaee H. Technology is an Asset: Enhancing EFL Learners’ Vocabulary Knowledge and Listening Comprehension through CALL. 2023; doi: 10.21203/rs.3.rs-3136533/v1

[5]Manoocherzadeh M, Isaee H, Barjesteh H. Artificial Intelligence in Project-Based Learning: A Systematic Review of Its Role in English Language Acquisition and Pedagogical Innovation. Indonesian Journal of Pedagogy and Teacher Education. 2025; 3(3): 81–91. doi: 10.58723/ijopate.v3i3.502

[6]Acemoglu D, Restrepo P. The wrong kind of AI? Artificial intelligence and the future of labour demand. National Bureau of Economic Research; 2019. Available online: https://www.nber.org/papers/w25682

[7]Adipat S. An Artificial Intelligence-Enhanced Phenomenon-Based Learning Approach for Interdisciplinary Understanding and Speaking Skills. International Journal of Instruction. 2023; 16(3): 531–550. doi: 10.29333/iji.2023.16329a

[8]Aiken RM, Epstein RG. Ethical guidelines in AI in education: Starting a conversation. International Journal of Artificial Intelligence in Education. 2000; 11: 163–176.

[9]Available online: https://www.researchgate.net/publication/228600407_Ethical_guidelines_for_AI_in_education_Starting_a_conversation

[10]Abalkheel A. Amalgamating Bloom’s Taxonomy and Artificial Intelligence to Face the Challenges of Online EFL Learning Amid Post-COVID-19 In Saudi Arabia. International Journal of English Language and Literature Studies. 2022; 11(1): 16–30. doi: 10.18488/5019.v11i1.4409

[11]Cabitza F, Campagner A, Simone C. The need to move away from agential-AI: Empirical investigations, useful concepts and open issues. International Journal of Human-Computer Studies. 2021; 155: 102696. doi: 10.1016/j.ijhcs.2021.102696

[12]Choi EPH, Lee JJ, Ho MH, et al. Chatting or cheating? The impacts of ChatGPT and other artificial intelligence language models on nurse education. Nurse Education Today. 2023; 125: 105796. doi: 10.1016/j.nedt.2023.105796

[13]Eke DO. ChatGPT and the rise of generative AI: Threat to academic integrity? Journal of Responsible Technology. 2023; 13: 100060. doi: 10.1016/j.jrt.2023.100060

[14]De La Vall RF, González Araya F. Exploring the Benefits and Challenges of AI-Language Learning Tools. International Journal of Social Sciences and Humanities Invention. 2023; 10(01): 7569–7576. doi: 10.18535/ijsshi/v10i01.02

[15]Gao X. Role of 5G network technology and artificial intelligence for research and reform of english situational teaching in higher vocational colleges. Journal of Intelligent & Fuzzy Systems. 2021; 40(2): 3643–3654. doi: 10.3233/JIFS-189399

[16]Isaee H, Barjesteh H. EFL teachers’ professional development needs: A comparative phenomenological analysis for face-to-face and online instruction. Journal of Studies in Learning and Teaching English. 2023; 12(2): 45–56.

[17]Georgieff A, Hyee R. Artificial Intelligence and Employment: New Cross-Country Evidence. Frontiers in Artificial Intelligence. 2022; 5: 832736. doi: 10.3389/frai.2022.832736

[18]Holmes W, Tuomi I. State of the art and practice in AI in education. European Journal of Education. 2022; 57(4): 542–570. doi: 10.1111/ejed.12533

[19]Liang H, Guo Y, Chen X, et al. Artificial intelligence for stepwise diagnosis and monitoring of COVID-19. European Radiology. 2022; 32(4): 2235–2245. doi: 10.1007/s00330-021-08334-6

[20]Risdianto E, Shirzadi S, Fatehi Rad N, et al. Advancing English Language Education through Artificial Intelligence: A Review of Benefits and Challenges. Journal of New Trends in English Language Learning (JNTELL). 2024; 4(Special Issue). doi: 10.57647/JNTELL.2025.SI-01

[21]Sweeney S. Who wrote this? Essay mills and assessment—Considerations regarding contract cheating and AI in higher education. The International Journal of Management Education. 2023; 21(2): 100818. doi: 10.1016/j.ijme.2023.100818

[22]Dakakni D, Safa N. Reading Patterns, Scanning, and the “Control F”/Search Icon: How Students Really (Don’t) Read. International Research in Education. 2023; 11(1): 128. doi: 10.5296/ire.v11i1.20943

[23]Hou Z. Research on Adopting Artificial Intelligence Technology to Improve Effectiveness of Vocational College English Learning. Journal of Physics: Conference Series. 2021; 1744(4): 042122. doi: 10.1088/1742-6596/1744/4/042122

[24]Wogu IAP, Mistra S, Olu-Owolabi EF, et al. Artificial intelligence, artificial teachers and the fate of learners in the 21st century education sector: Implications for theory and practice. International Journal of Pure and Applied Mathematics. 2018; 119(16): 2245–2259.

[25]Popenici SAD, Kerr S. Exploring the impact of artificial intelligence on teaching and learning in higher education. Research and Practice in Technology Enhanced Learning. 2017; 12(1): 22. doi: 10.1186/s41039-017-0062-8

[26]Bostrom N. The Vulnerable World Hypothesis. Global Policy. 2019; 10(4): 455–476. doi: 10.1111/1758-5899.12718

[27]Park YJ, Lee H, Jones-Jang SM, et al. Digital assistants: Inequalities and social context of access, use, and perceptual understanding. Poetics. 2022; 93: 101689. doi: 10.1016/j.poetic.2022.101689

[28]Hwang EJ, Park J, Hong W, et al. Artificial intelligence system for identification of false-negative interpretations in chest radiographs. European Radiology. 2022; 32(7): 4468–4478. doi: 10.1007/s00330-022-08593-x

[29]Lesia Viktorivna K, Andrii Oleksandrovych V, Iryna Oleksandrivna K, et al. Artificial Intelligence in Language Learning: What Are We Afraid of. Arab World English Journal. 2022; (8): 262–273. doi: 10.24093/awej/call8.18

[30]Yong Q. Application of Artificial Intelligence to Higher Vocational English Teaching in the Information Environment. Journal of Physics: Conference Series. 2020; 1533(3): 032030. doi: 10.1088/1742-6596/1533/3/032030

[31]Abdullah Sharadgah T, Abdulatif Sa’di R. A Systematic Review of Research on the Use of Artificial Intelligence in English Language Teaching and Learning (2015–2021): What are the Current Effects? Journal of Information Technology Education: Research. 2022; 21: 337–377. doi: 10.28945/4999

[32]Lutz C. Digital inequalities in the age of artificial intelligence and big data. Human Behavior and Emerging Technologies. 2019; 1(2): 141–148. doi: 10.1002/hbe2.140

[33]Wang X. Research on Oral English Learning System Integrating AI Speech Data Recognition and Speech Quality Evaluation Algorithm. Journal of Electrical Systems. 2024; 20(5s): 2466–2477. doi: 10.52783/jes.2688

[34]Shu X, Xu C. Artificial Intelligence-Based English Self-Learning Effect Evaluation and Adaptive Influencing Factors Analysis. Mathematical Problems in Engineering. 2022; 2022: 1–9. doi: 10.1155/2022/2776823

[35]Barjesteh H, Isaee H, Manoochehrzadeh M. From skill acquisition to professional agency: Rethinking EFL teachers’ professional development in the age of AI. Indonesian Journal of Pedagogy and Teacher Education. 2026; 4(1): 20–30.

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