The Effect of Artificial Intelligence on the Academic Development of Iranian EFL Students
محورهای موضوعی : نشریه تخصصی زبان، فرهنگ، و ترجمه (دوفصلنامه)
1 - Assistant Professor, Department of English Language, Imam Hossein Comprehensive University, Tehran, Iran
کلید واژه: Academic Development, Artificial Intelligence, Critical Thinking, Education Technology,
چکیده مقاله :
The incorporation of Artificial Intelligence (AI) with teaching and education has changed academic learning and created new opportunities and challenges for students’ academic development. The current research examines the effect of AI on EFL students’ academic performance and learning processes, focusing on their opportunities and challenges related to AI. The participants of the study consisted of ninety-five EFL students of Tehran University who used AI to learn courses better in the academic year of 2024-2025. These students were chosen using purposive sampling to ensure relevance. This research used a mixed methods design, including both quantitative and qualitative data collection techniques. A self-structured questionnaire consisted of eleven items: seven closed-ended questions measuring the perception, usage, and effectiveness of AI tools, and four open-ended questions that examined experiences, expectations, and concerns. Using SPSS version 24 software, quantitative data were analyzed by considering frequency and percentage, while qualitative answers underwent thematic analysis. The results show that 96.6 percent of participants apply AI in their academic tasks; 89.2 percent of virtual assistants are the most widely employed AI applications, supporting real-time feedback, task management, and information retrieval. Moreover, 43.4 percent of participants apply AI-powered learning platforms, indicating a shift towards interactive and personalized learning. This research also emphasizes the necessity of a structured framework for integrating AI with support from ethical guidelines. Consequently, while AI has enormous potential to improve academic performance and increase learning efficiency, its successful implementation is contingent on addressing concerns about accuracy, cognitive disengagement, and ethical implications.
The incorporation of Artificial Intelligence (AI) with teaching and education has changed academic learning and created new opportunities and challenges for students’ academic development. The current research examines the effect of AI on EFL students’ academic performance and learning processes, focusing on their opportunities and challenges related to AI. The participants of the study consisted of ninety-five EFL students of Tehran University who used AI to learn courses better in the academic year of 2024-2025. These students were chosen using purposive sampling to ensure relevance. This research used a mixed methods design, including both quantitative and qualitative data collection techniques. A self-structured questionnaire consisted of eleven items: seven closed-ended questions measuring the perception, usage, and effectiveness of AI tools, and four open-ended questions that examined experiences, expectations, and concerns. Using SPSS version 24 software, quantitative data were analyzed by considering frequency and percentage, while qualitative answers underwent thematic analysis. The results show that 96.6 percent of participants apply AI in their academic tasks; 89.2 percent of virtual assistants are the most widely employed AI applications, supporting real-time feedback, task management, and information retrieval. Moreover, 43.4 percent of participants apply AI-powered learning platforms, indicating a shift towards interactive and personalized learning. This research also emphasizes the necessity of a structured framework for integrating AI with support from ethical guidelines. Consequently, while AI has enormous potential to improve academic performance and increase learning efficiency, its successful implementation is contingent on addressing concerns about accuracy, cognitive disengagement, and ethical implications.
Baker, J.A. (2021). AI in education: Bringing it all together. In OECD digital education outlook 2021: Pushing the frontiers with AI, blockchain, and robotics, 43–56.
Baker, T., Smith, L., & Anissa, N. (2019). Educ-AI-tion rebooted? Exploring the future of AI in schools and colleges. Available online: https://www.nesta.org.uk/report/education-rebooted/ (accessed on 29 January 2025).
Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101.
Castaneda, L., & Selwyn, N. (2018). More than tools? Making sense of the ongoing digitization of higher education. International Journal of Educational Technology in Higher Education, 15, 22.
Chen, X., Xie, H., & Hwang, G.J. (2020). A multi-perspective study on AI in education: Grants, conferences, journals, software tools, institutions, and researchers. Computer & Education: AI, 1, 100005.
Du Boulay, B. (2000). Can we learn from ITSs? In The international conference on intelligent tutoring systems, 9–17.
Magazine, E. (2020). Successful AI examples in higher education that can inspire our future. EdTech: Focus on Higher Education.
Ezzy, D. (2013). Qualitative analysis. Routledge.
Facione, P.A. (2011). Critical thinking: What it is and why it counts. Insight Assessment. Available online: https://insightassessment.com/ unlock-resources/ (accessed on 29 January 2025).
Hennekeuser, D., Vaziri, D.D., Golchinfar, D., Schreiber, D., & Stevens, G. (2024). Enlarged education—Exploring the use of generative AI to support lecturing in higher education. International Journal of AI in Education, 1–33.
Holmes, W., Bialik, M., & Fadel, C. (2019). AI in education: Promises and implications for teaching and learning. Center for Curriculum Redesign. ISBN-13: 978-1-794-29370-0.
Holmes, W., & Tuomi, I. (2022). State of the art and practice in AI in education. European Journal of Education, 57, 542–570.
Hwang, G.J., Xie, H., Wah, B.W., & Gasevic, D. (2020). Vision, challenges, roles, and research issues of AI in education. Computers & Education: AI, 1, 100001.
Jafari, F. & Keykha, A. (2024). Identifying the opportunities and challenges of artificial intelligence in higher education: a qualitative study. Journal of Applied Research in Higher Education,16(4), 1228-1245. doi:10.1108/JARHE-09-2023-0426.
Jayawardena, C.K., Gunathilake, Y., Ihalagedara, D. (2025). Dental students’ learning experience: artificial intelligence vs human feedback on assignments, Int Dent J., 75(1), 100-108, doi: 10.1016/j.identj.2024.12.022.
Jia, J. (2024). Artificial Intelligence Implementation in Higher Education in China: Case Study of Beijing Technology and Business University. Journal of Current Social Issues Studies, 1(1), 17-31.
Johnson, A., & Smith, B. (2019). The effect of personalized learning on student attitudes and self effcacy in mathematics. Educational Technology Research and Development, 38(2), 201–218.
Ju, Q. (2023). Experimental evidence on the negative effect of generative AI on scientifc learning outcomes. arXiv, arXiv:2311.05629.
Krause, S., Panchal, B.H., & Ubhe, N. (2025). The evolution of learning: Assessing the transformative effect of generative AI on higher education. arXiv, arXiv:2404.10551.
Liang, Y., & Xu, J. (2013, June). An intelligent tutoring system based on speech assessment for spoken English learning in China. In International Conference on Brain Inspired Cognitive Systems (pp. 358-365). Berlin, Heidelberg: Springer Berlin Heidelberg.
Lodzikowski, K., Foltz, P.W., & Behrens, J.T. (2024). Generative AI and its educational implications. Available online: https://link.springer.com/chapter/10.1007/978-3-031-64487-0_2.
Luckin, R., Holmes, W., Griffths, M., & Forcier, L.B. (2016). Intelligence unleashed: An argument for AI in education. Pearson Education.
O’Neil, C. (2017). Weapons of math destruction: How big data increases inequality and threatens democracy. Crown Publishing Group.
Pinkwart, N. (2016). Another 25 years of AIED? Challenges and opportunities for intelligent educational technologies of the future. International Journal of AI in Education, 26(2), 771–783.
Qadir, J. (2023). Engineering education in the era of ChatGPT: Promise and pitfalls of generative AI for education. 2023 IEEE Global Engineering Education Conference, 1–9.
Roll, I., & Wylie, R. (2016). Evolution and revolution in AI in education, Int. J. Artif. Intell. Edu., 26(2), 582-599.
Saseanu, A.S., Gogonea, R.M., & Ghit, A.S.I. (2024). The social effect of using AI in education. Amfteatru Economic, 26(65), 89–105.
Schiff, D. (2021). Out of the laboratory and into the classroom: The future of artificial intelligence in education, AI & Society, 36 (1), 331-348, 10.1007/s00146-020-01033-8
Selwyn, N. (2016). Is technology good for education? Polity Press.
Selwyn, N. (2019). Should robots replace teachers? AI and the future of education. Polity Press.
Serholt, S., Basedow, C.A., Barendregt, W., & Obaid, M. (2014). Comparing a humanoid tutor to a human tutor delivering an instructional task to children, Proc. IEEE-RAS Int. Conf. Humanoid Robots, 1134-1141.
Wazir, S., Mohani, S.S., Affandi, H., Rafique, A.A., & Soomro, M. (2025). Impact of artificial intelligence and machine learning on predicting student performance and engagement. Dialogue Social Science Review, 3(1),1298-1311.
Williamson, B. (2017). Big data in education: The digital future of learning, policy and practice. SAGE Publications.
Wu, Y. (2023). Integrating generative AI in education: How ChatGPT brings challenges for future learning and teaching. Journal of Advanced Research in Education, 2(4), 6–10. Available online: https://www.pioneerpublisher.com/jare
Zawacki-Richter, O., Marin, V.I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on AI applications in higher education—Where are the educators? International Journal of Educational Technology in Higher Education, 16(1), 39.
