This study explores the potential of artificial intelligence for the early identification of learning difficulties among EFL learners and the organization of timely pedagogical support. The research was designed as a mixed-methods study involving 40 EFL learners over an 8-week period. The study focused on such indicators as platform engagement, assignment completion, recurring grammatical and lexical errors, test performance, and participation in speaking activities. AI-assisted analysis was used to detect learners who showed early signs of academic difficulty. Based on the identified problems, differentiated pedagogical support was provided through adapted tasks, targeted grammar and vocabulary exercises, gradual speaking practice, and individualized feedback. The findings showed positive changes in learner participation, assignment completion, test performance, and the reduction of recurring errors, particularly among the at-risk group. The study concludes that artificial intelligence can serve as an effective supportive tool in EFL education when it is used to strengthen early diagnosis and personalize pedagogical intervention rather than replace the teacher. The results highlight the importance of combining AI-based monitoring with human-centered instructional support in order to improve learner achievement and engagement.

artificial intelligence, EFL learners, early identification, learning difficulties, pedagogical support, learning analytics, personalized instruction, language learning, student engagement, adaptive learning.

Azizah, Z., Ohyama, T., Zhao, X., Ohkawa, Y., & Mitsuishi, T. (2024). Predicting at-risk students in the early stage of a blended learning course via machine learning using limited data. Computers and Education: Artificial Intelligence, 7, 100261. https://doi.org/10.1016/j.caeai.2024.100261

Toscu, S. (2025). A systematic review on AI technologies in English as a foreign language education. Sakarya University Journal of Education, 15(2), 156–172. https://doi.org/10.19126/suje.1623401

Kabudi, T., Pappas, I., & Olsen, D. H. (2021). AI-enabled adaptive learning systems: A systematic mapping of the literature. Computers and Education: Artificial Intelligence, 2, 100017. https://doi.org/10.1016/j.caeai.2021.100017

Lin, C.-J., & Hwang, G.-J. (2018). A learning analytics approach to investigating factors affecting EFL students' oral performance in a flipped classroom. Educational Technology & Society, 21(2), 205–219.

Azizah, Z., Ohyama, T., Zhao, X., Ohkawa, Y., & Mitsuishi, T. (2024). Predicting at-risk students in the early stage of a blended learning course via machine learning using limited data. Computers and Education: Artificial Intelligence, 7, 100261. https://doi.org/10.1016/j.caeai.2024.100261

Chen, A., Zhang, Y., Jia, J., Liang, M., Cha, Y., & Lim, C. P. (2025). A systematic review and meta-analysis of AI-enabled assessment in language learning: Design, implementation, and effectiveness. Journal of Computer Assisted Learning, 41(1), e13064. https://doi.org/10.1111/jcal.13064

Alangari, T. S. (2025). The effect of AI-assisted learning on EFL writing proficiency: Quasi-experimental and cluster analysis. Educational Process: International Journal, 17, e2025345. doi:10.22521/edupij.2025.17.345

Guo, K., & Li, D. (2024). Understanding EFL students’ use of self-made AI chatbots as personalized writing assistance tools: A mixed methods study. System, 124, 103362. doi:10.1016/j.system.2024.103362

Russell, J. E., Smith, A., & Larsen, R. (2020). Elements of success: Supporting at-risk student resilience through learning analytics. Computers & Education, 152, 103890. doi:10.1016/j.compedu.2020.103890

Yuan, L., & Liu, X. (2025). The effect of artificial intelligence tools on EFL learners' engagement, enjoyment, and motivation. Computers in Human Behavior, 162, 108474. doi:10.1016/j.chb.2024.108474.