ARTIFICIAL INTELLIGENCE IN PERSONALIZED LANGUAGE LEARNING: BENEFITS, ATTITUDES, AND MOTIVATION OF BANGLADESHI HIGHER EDUCATION EFL STUDENTS
Main Article Content
Abstract
The integration of Artificial Intelligence (AI) into English language learning has transformed personalized learning through immediate feedback, adaptive content, and tailor-made learning paths for students. This study examines the potential of AI to enhance English as a Foreign Language (EFL) learning among higher education students in Bangladesh by exploring the benefits, learners’ attitudes, and motivation for AI-based personalized language learning. Based on Sociocultural Theory, Self-Determination Theory, and the Theory of Planned Behavior, this study employed a quantitative design and collected data via an online survey administered to 203 undergraduate and graduate students. Stratified random sampling assured satisfactory representation in terms of academic years and disciplines. The findings show that artificial intelligence, valuing its ability to personalize learning, motivates students through immediate feedback and provides suggestions for further improvement. Concerns were noted that over-reliance on AI may detract from human instruction and raise issues regarding its reliability. This study extends the literature by addressing some important gaps in knowledge about the role of AI in EFL learning in resource-scarce settings, such as Bangladesh. The impacts of AI on motivation and language acquisition, along with the development of teacher-training and student-orientation programs, underscore a critical need for cooperation among universities, policymakers, and developers to ensure that AI tools align with educational requirements and facilitate effective, tailored English-language instruction.
Downloads
Article Details
Section
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
How to Cite
References
Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179-211. https://doi.org/10.1016/0749-5978(91)90020-t
Bandura, A. (1997). Self-efficacy: The exercise of control. W H Freeman/Times Books/ Henry Holt & Co.
Bryman, A. (2012). Social research methods (4th ed.). Oxford University Press.
Cerny, M. (2023). Perceptions of the design and use chatbots for educational purposes: a dialogue partner, Journal of Educators Online, 20(4), 1-26. https://eric.ed.gov/?id=EJ1407696
Chen, X., Zou, D., Cheng, G., & Xie, H. (2020). Detecting latent topics and trends in educational technologies over four decades using structural topic modeling: A retrospective of all volumes of computers & education. Computers & Education, 151, Article 103855. https://doi.org/10.1016/j.compedu.2020.103855
Cohen, L., Manion, L., & Morrison, K. (2018). Research methods in education. Routledge.
Cortez, P. M., Ong, A. K. S., Diaz, J. F. T., German, J. D., & Jagdeep, S. J. S. S. (2024). Analyzing preceding factors affecting behavioral intention on communicational artificial intelligence as an educational tool. Heliyon, 10(3), Article e25896. https://doi.org/10.1016/j.heliyon.2024.e25896
Creswell, J. W. (2014). Research design: Qualitative, quantitative, and mixed methods approaches. Sage Publications.
Creswell, J. W., & Poth, C. N. (2018). Qualitative inquiry and research design: Choosing among five approaches. Sage Publications.
Deci, E. L., & Ryan, R. M. (1985). Intrinsic motivation and self-determination in human behavior. Springer eBooks. https://doi.org/10.1007/978-1-4899-2271-7
Drost, E. A. (2011). Validity and reliability in social science research. Education Research and Perspectives, 38(1), 105-123. https://search.informit.org/doi/10.3316/informit.491551710186460
Ellikkal A., & Rajamohan S. (2025). AI-enabled personalized learning: empowering management students for improving engagement and academic performance. VILAKSHAN - XIMB Journal of Management, 22(1), 28-44. https://doi.org/10.1108/XJM-02-2024-0023
Frankfort-Nachmias, C., & Nachmias, D. (2008). Research methods in the social sciences (7th ed.). Worth Publishers.
Ginting, P., Batubara, H. M., & Hasnah, Y. (2023). Artificial intelligence powered writing tools as adaptable aids for academic writing: Insight from EFL college learners in writing final project. International Journal of Multidisciplinary Research and Analysis, 06(10), 4640-4650. https://doi.org/10.47191/ijmra/v6-i10-15
Haryanto, E., & Ali, R. M. (2019). Students’ attitudes towards the use of artificial intelligence SIRI in EFL learning at one public university. International Seminar and Annual Meeting BKS-PTN Wilayah Barat, 1(1), 190-195. https://conference.unsri.ac.id/index.php/semirata/article/view/1102
Hasan, M. M., Fatema, K., & Mahmud, R. (2026). Bangladeshi university EFL teachers’ vision for the future role of artificial intelligence in teaching and teachers’ new identity. International Journal of Evaluation and Research in Education (IJERE), 15(1), 837–847. http://doi.org/10.11591/ijere.v15i1.33638
Islam, M., Hasan, M. M., & Mahmud, R. (2024). EFL teachers’ perceptions of AI’s impact on academic integrity and pedagogy in Bangladeshi universities. Language Literacy: Journal of Linguistics, Literature, and Language Teaching, 8(2), 564-579. https://doi.org/10.30743/ll.v8i2.10082
Jiang, R. (2022). How does artificial intelligence empower EFL teaching and learning nowadays? A review on artificial intelligence in the EFL context. Frontiers in Psychology, 13, Article 1049401. https://doi.org/10.3389/fpsyg.2022.1049401
Khan, A. L., Hasan, M. M., Islam, M. N., & Uddin, M. S. (2024). Artificial intelligence tools in developing english writing skills: Bangladeshi university EFL students’ perceptions. English Education: Jurnal Tadris Bahasa Inggris, 17(2), 345-371. https://dx.doi.org/10.24042/ee-jtbi.v17i2.24369
Kovalchuk, V., Reva, S., Volch, I., Shcherbyna, S., Mykhailyshyn, H., & Lychova, T. (2025). Artificial intelligence as an effective tool for personalized learning in modern education. Environment. Technology. Resources. Proceedings of the International Scientific and Practical Conference, 3, 187-194. https://doi.org/10.17770/etr2025vol3.8534
Krashen, S. (1985). The input hypothesis: Issues and implications. Longman.
Krejcie, R. V., & Morgan, D. W. (1970). Sample size determination table. Educational and Psychological Measurement, 30(3), 607-610.
Laak, K.-J., & Aru, J. (2025). AI and personalized learning: Bridging the gap with modern educational goals. Educational Technology & Society, 28(4), 133-150. https://www.jstor.org/stable/48839360
Li, Y., & Chai, Y. (2025). Bridging regional disparities through AI-driven personalized learning paths: Evidence from Chinese high school education. Interactive Learning Environments, 1-16. https://doi.org/10.1080/10494820.2025.2523388
Lin, H., & Chen, Q. (2024). Artificial intelligence (AI)-integrated educational applications and college students’ creativity and academic emotions: students and teachers’ perceptions and attitudes. BMC Psychology, 12(1), Article 487. https://doi.org/10.1186/s40359-024-01979-0
Liu, L. (2025). Impact of AI gamification on EFL learning outcomes and nonlinear dynamic motivation: Comparing adaptive learning paths, conversational agents, and storytelling. Education and Information Technologies, 30, 11299-11338. https://doi.org/10.1007/s10639-024-13296-5
Mahmud, R. (2024). Perceptions of tertiary level students about peer feedback in writing classes in Bangladesh. International Journal of English Linguistics, 14(6), 160-170. https://doi.org/10.5539/ijel.v14n6p160
Molla, C., Mani, L., Bhuiyan, M. R. I., & Hossain, R. (2023). Examining the potential usages, features, and challenges of using ChatGPT technology: A PRISMA-Based Systematic Review. Migration Letters, 20(S9), 927-945. https://doi.org/10.59670/ml.v20is9.4918
McMillan, J. H., & Schumacher, S. (2010). Research in education: Evidence-Based inquiry. Pearson.
Merino-Campos, C. (2025). The impact of artificial intelligence on personalized learning in higher education: A systematic review. Trends in Higher Education, 4(2), 1-15. https://doi.org/10.3390/higheredu4020017
Moybeka, A. M. S., Syariatin, N., Tatipang, D. P., Mushthoza, D. A., Dewi, N. P. J. L., & Tineh, S. (2023). Artificial intelligence and English classroom: The implications of AI toward EFL students’ motivation. Edumaspul - Jurnal Pendidikan, 7(2), 2444-2454. https://doi.org/10.33487/edumaspul.v7i2.6669
Neuman, W. L. (2014). Social Research methods: Qualitative and quantitative approaches. Pearson.
Ngoc, B. N., & Dan, T. C. (2023). EFL student’s perceptions and practices on translation learning strategies at school of foreign languages, Can Tho University, Vietnam. European Journal of Multilingualism and Translation Studies, 3(1), 42-85. https://doi.org/10.46827/ejmts.v3i1.437
Ng, P. M., Chan, J. K., Kwong, R., Kwok, M. L. J., Lau, M. M., & Chow, P. K. (2025). Bonus or burden? Exploring the interplay of FOMO and attitudes on DeepSeek adoption, managing information and firm performance in China. Journal of Enterprise Information Management, 39(2), 506-531. https://doi.org/10.1108/JEIM-03-2025-0228
Pallant, J. (2020). SPSS survival manual: A step-by-step guide to data analysis using IBM SPSS. Routledge.
Rizvi, I., Bose, C., Tripathi, N. (2025). Transforming education: Adaptive learning, AI, and online platforms for personalization. Rizvi, I., Bose, C., & Tripathi, N. (2025). Transforming education: Adaptive learning, AI, and online platforms for personalization. In L. O. Yesufu & P. N. E. Nohuddin (Eds.), Technology for societal transformation. Springer. https://doi.org/10.1007/978-981-96-1721-0_4
Rusmiyanto, R., Huriati, N., Fitriani, N., Tyas, N. K., Rofi’i, A., & Sari, M. N. (2023). The role of artificial intelligence (AI) in developing English language learner’s communication skills. Journal on Education, 6(1), 750-757. https://doi.org/10.31004/joe.v6i1.2990
Sharma, S., Mittal, P., Kumar, M. (2025). The role of large language models in personalized learning: a systematic review of educational impact. Discover Sustainability, 6, Article 243. https://doi.org/10.1007/s43621-025-01094-z
Shi, G., Li, J., & Yang, J. (2024). A study on the influencing factors of university students’ online persistent learning supported by intelligent technology in the post-pandemic era: an empirical study with PLS-SEM. Interactive Learning Environments, 32(9), 4789-4811. https://doi.org/10.1080/10494820.2023.2205901
Son, J., Ružić, N. & Philpott, A. (2025). Artificial intelligence technologies and applications for language learning and teaching. Journal of China Computer-Assisted Language Learning, 5(1), 94-112. https://doi.org/10.1515/jccall-2023-0015
Sumakul, D. T. Y. G., & Hamied, F. A. (2023). Amotivation in AI injected EFL classrooms: Implications for teachers. Indonesian Journal of Applied Linguistics, 13(1), 26-34. https://doi.org/10.17509/ijal.v13i1.58254
Teddlie, C., & Yu, F. (2007). Mixed methods sampling. Journal of Mixed Methods Research, 1(1), 77-100. https://doi.org/10.1177/1558689806292430
Uddin, M. K., Uzir, M. U. H., Hasan, M. M., Hassan, M. S., & Sahabuddin, M. (2020). A scientific novel way of article and thesis writing: Findings from a survey on Keyword, Sequence, and Importance (KSI) technique. Universal Journal of Educational Research, 8(12A), 7894-904. https://doi.org/10.13189/ujer.2020.082578
Vygotsky, L. S. (1978). Mind in society: The development of higher psychological processes. Harvard University Press.
Yaşar, A., & Yılmaz, N. P. (2026). Artificial intelligence and the future of personalized education. In A. Bozkurt (Ed.), Artificial intelligence and the future of personalized education (pp. 1-80). IGI Global Scientific Publishing. https://doi.org/10.4018/979-8-3373-7574-8.ch001