AI INTEGRATION IN ENGLISH LANGUAGE TEACHING: INSIGHTS INTO ESP TEACHERS’ VIEWS AND READINESS

  • Latifa Ika Sari Politeknik Ilmu Pelayaran Semarang
  • Ria Hermina Sari Politeknik Ilmu Pelayaran Semarang
DOI: https://doi.org/10.25134/erjee.v13i2.11505
Keywords: Artificial Intelligence (AI), English Language Teaching (ELT), teachers’ perspectives

Abstract

This study investigates ESP teachers’ perspectives on the integration of AI tools in language teaching, focusing on the benefits, negative sides, usage, readiness, and barriers they face. A qualitative approach was employed, utilizing an open-ended questionnaire distributed to 30 teachers in higher vocational institutions. The thematic analysis of their responses reveals several findings. Teachers recognize AI's potential to support personalized learning, enhance skill development, and assist in lesson planning. However, concerns about over-reliance on AI and the lack of human interaction were also highlighted. Additionally, the study identifies barriers such as inadequate training, infrastructure limitations, and ethical concerns. Teachers emphasized the need for professional development programs, including technical training, AI integration in pedagogy, and ethical guidance. Institutional support in providing access to AI resources and managing workloads is also critical. The study suggests a balanced integration of traditional methods and AI-driven approaches.

References

Amaral, L. A., & Meurers, D. (2011). On using intelligent computer-assisted language learning in real-life foreign language teaching and learning. ReCALL, 23(1). https://doi.org/10.1017/S0958344010000261

Ayedoun, E., Hayashi, Y., & Seta, K. (2019). Adding Communicative and Affective Strategies to an Embodied Conversational Agent to Enhance Second Language Learners’ Willingness to Communicate. International Journal of Artificial Intelligence in Education, 29(1). https://doi.org/10.1007/s40593-018-0171-6

Barrot, J. S. (2023). Using automated written corrective feedback in the writing classrooms: effects on L2 writing accuracy. Computer Assisted Language Learning, 36(4). https://doi.org/10.1080/09588221.2021.1936071

Bashori, M., van Hout, R., Strik, H., & Cucchiarini, C. (2024). ‘Look, I can speak correctly’: learning vocabulary and pronunciation through websites equipped with automatic speech recognition technology. Computer Assisted Language Learning, 37(5–6). https://doi.org/10.1080/09588221.2022.2080230

Bibauw, S., François, T., & Desmet, P. (2019). Discussing with a computer to practice a foreign language: research synthesis and conceptual framework of dialogue-based CALL. Computer Assisted Language Learning, 32(8). https://doi.org/10.1080/09588221.2018.1535508

Chen, H. H. J. (2011). Developing and evaluating an oral skills training website supported by automatic speech recognition technology. ReCALL, 23(1). https://doi.org/10.1017/S0958344010000285

Chen, H. H. J., Yang, C. T. Y., & Lai, K. K. W. (2023). Investigating college EFL learners’ perceptions toward the use of Google Assistant for foreign language learning. Interactive Learning Environments, 31(3). https://doi.org/10.1080/10494820.2020.1833043

Chiu, T. K. F., Xia, Q., Zhou, X., Chai, C. S., & Cheng, M. (2023). Systematic literature review on opportunities, challenges, and future research recommendations of artificial intelligence in education. Computers and Education: Artificial Intelligence, 4. https://doi.org/10.1016/j.caeai.2022.100118

Choi, I. C. (2016). Efficacy of an ICALL tutoring system and process-oriented corrective feedback. Computer Assisted Language Learning, 29(2). https://doi.org/10.1080/09588221.2014.960941

Chukharev-Hudilainen, E., & Saricaoglu, A. (2016). Causal discourse analyzer: improving automated feedback on academic ESL writing. Computer Assisted Language Learning, 29(3). https://doi.org/10.1080/09588221.2014.991795

Coniam, D. (2014). The linguistic accuracy of chatbots: Usability from an ESL perspective. In Text and Talk (Vol. 34, Issue 5). https://doi.org/10.1515/text-2014-0018

Cox, T. L., & Davies, R. S. (2012). Using automatic speech recognition technology with elicited oral response testing. CALICO Journal, 29(4). https://doi.org/10.11139/cj.29.4.601-618

Crosthwaite, P., & Steeples, B. (2024). Data-driven learning with younger learners: exploring corpus-assisted development of the passive voice for science writing with female secondary school students. Computer Assisted Language Learning, 37(5–6). https://doi.org/10.1080/09588221.2022.2068615

Dai, Y., & Wu, Z. (2023). Mobile-assisted pronunciation learning with feedback from peers and/or automatic speech recognition: a mixed-methods study. Computer Assisted Language Learning, 36(5–6). https://doi.org/10.1080/09588221.2021.1952272

Daniels, P., & Iwago, K. (2017). The suitability of cloudbased speech recognition engines for language learning. JALT CALL Journal, 13(3). https://doi.org/10.29140/jaltcall.v13n3.220

Dizon, G. (2017). Using Intelligent Personal Assistants for Second Language Learning: A Case Study of Alexa. TESOL Journal, 8(4). https://doi.org/10.1002/tesj.353

Dizon, G. (2020). Evaluating intelligent personal assistants for L2 listening and speaking development. Language Learning and Technology, 24(1). https://doi.org/10.125/44705

Dizon, G., & Gayed, J. M. (2021). Examining The Impact Of Grammarly On The Quality Of Mobile L2 Writing. JALT CALL Journal, 17(2). https://doi.org/10.29140/JALTCALL.V17N2.336

Ebadi, S., & Rahimi, M. (2019). Mediating EFL learners’ academic writing skills in online dynamic assessment using Google Docs. Computer Assisted Language Learning, 32(5–6). https://doi.org/10.1080/09588221.2018.1527362

Ebadi, S., & Saeedian, A. (2015). The Effects of Computerized Dynamic Assessment on Promoting At-Risk Advanced Iranian EFL Students’ Reading Skills. Issues in Language Teaching, 4(2). https://doi.org/10.22054/ILT.2015.7224

Evers, K., & Chen, S. (2022). Effects of an automatic speech recognition system with peer feedback on pronunciation instruction for adults. Computer Assisted Language Learning, 35(8). https://doi.org/10.1080/09588221.2020.1839504

Forsyth, C. M., Luce, C., Zapata-Rivera, D., Jackson, G. T., Evanini, K., & So, Y. (2019). Evaluating English language learners’ conversations: Man vs. Machine. Computer Assisted Language Learning, 32(4). https://doi.org/10.1080/09588221.2018.1517126

Fryer, L., Coniam, D., Carpenter, R., & Lăpușneanu, D. (2020). Bots for language learning now: Current and future directions. Language Learning & Technology, 24(2).

Fryer, L. K., Ainley, M., Thompson, A., Gibson, A., & Sherlock, Z. (2017). Stimulating and sustaining interest in a language course: An experimental comparison of Chatbot and Human task partners. Computers in Human Behavior, 75. https://doi.org/10.1016/j.chb.2017.05.045

Gyawali, Y. P., & Mehandroo, M. (2022). Artificial Intelligence in English Language Teaching: Navigating the Future with Emerging Perspectives. Journal of Language and Linguistics in Society, 26. https://doi.org/10.55529/jlls.26.21.27

Hadley, G., & Charles, M. (2017). Enhancing extensive reading with data-driven learning. Language Learning and Technology, 21(3).

Hamied, F. A., & Musthafa, B. (2019). Policies on language education in Indonesia. Indonesian Journal of Applied Linguistics, 9(2). https://doi.org/10.17509/ijal.v9i2.20279

Han, T., & Sari, E. (2024). An investigation on the use of automated feedback in Turkish EFL students’ writing classes. Computer Assisted Language Learning, 37(4). https://doi.org/10.1080/09588221.2022.2067179

Healey, Justin. (2020). Artificial intelligence (Vol. 450). Spinney Press.

Huang, J.-X., Kwon, O.-W., Lee, K.-S., & Kim, Y.-K. (2018). Improve the chatbot performance for the DB-CALL system using a hybrid method and a domain corpus. In Future-proof CALL: language learning as exploration and encounters – short papers from EUROCALL 2018. https://doi.org/10.14705/rpnet.2018.26.820

Idham, A. Z., Rauf, W., & Rajab, Abd. (2024). Navigating the Transformative Impact of Artificial Intelligence on English Language Teaching: Exploring Challenges and Opportunities. Jurnal Edukasi Saintifik, 4(1). https://doi.org/10.56185/jes.v4i1.620

Kim, H. S., Kim, N. Y., & Cha, Y. (2021). Is it beneficial to use ai chatbots to improve learners’ speaking performance? Journal of Asia TEFL, 18(1). https://doi.org/10.18823/asiatefl.2021.18.1.10.161

Koltovskaia, S. (2023). Postsecondary L2 writing teachers’ use and perceptions of Grammarly as a complement to their feedback. ReCALL, 35(3). https://doi.org/10.1017/S0958344022000179

Lameras, P., & Arnab, S. (2022). Power to the Teachers: An Exploratory Review on Artificial Intelligence in Education. Information (Switzerland), 13(1). https://doi.org/10.3390/info13010014

Lee, C. (2020). A study of adolescent English learners’ cognitive engagement in writing while using an automated content feedback system. Computer Assisted Language Learning, 33(1–2). https://doi.org/10.1080/09588221.2018.1544152

Lee, S. M. (2021). The effectiveness of machine translation in foreign language education: a systematic review and meta-analysis. Computer Assisted Language Learning. https://doi.org/10.1080/09588221.2021.1901745

Li, Z., Feng, H. H., & Saricaoglu, A. (2017). The short-term and long-term effects of AWE feedback on ESL students’ development of grammatical accuracy. CALICO Journal, 34(3). https://doi.org/10.1558/cj.26382

Liang, J. C., Hwang, G. J., Chen, M. R. A., & Darmawansah, D. (2023). Roles and research foci of artificial intelligence in language education: an integrated bibliographic analysis and systematic review approach. In Interactive Learning Environments (Vol. 31, Issue 7). https://doi.org/10.1080/10494820.2021.1958348

Link, S., Dursun, A., Karakaya, K., & Hegelheimer, V. (2014). Towards best ESL practices for implementing automated writing evaluation. CALICO Journal, 31(3). https://doi.org/10.11139/cj.31.3.323-344

Liu, M. (2023). Exploring the Application of Artificial Intelligence in Foreign Language Teaching: Challenges and Future Development. SHS Web of Conferences, 168. https://doi.org/10.1051/shsconf/202316803025

Luckin, R., Holmes, W., Griffiths, M., & Forcier, L. B. (2016). Intelligence Unleashed: An argument for AI in Education. Pearson. https://www.researchgate.net/publication/299561597

Madhakomala, R., Hakim, M. A., & Syifauzzuhrah, N. (2022). Problems of education in Indonesia and alternative solutions. International Journal of Business, Law, and Education, 3(3). https://doi.org/10.56442/ijble.v3i3.64

McCrocklin, S. (2019). Learners’ feedback regarding ASR-based dictation practice for pronunciation learning. CALICO Journal, 36(2). https://doi.org/10.1558/cj.34738

Mehri Kamrood, A., Davoudi, M., Ghaniabadi, S., & Amirian, S. M. R. (2021). Diagnosing L2 learners’ development through online computerized dynamic assessment. Computer Assisted Language Learning, 34(7). https://doi.org/10.1080/09588221.2019.1645181

Mijwil, M. M., Abdulrhman, S. H., Abttan, R. A., Faieq, A. K., & Alkhazraji, A. (2023). Artificial Intelligence Applications in English Language Teaching: A Short Survey. Asian Journal of Applied Sciences, 10(6). https://doi.org/10.24203/ajas.v10i6.7111

Monteiro, K., & Kim, Y. J. (2020). The effect of input characteristics and individual differences on L2 comprehension of authentic and modified listening tasks. System, 94. https://doi.org/10.1016/j.system.2020.102336

Nikonova, E., Yakhyaeva, K., Pivkina, N., & Schetinina, A. (2023). Using Artificial Intelligence Tools in Teaching a Foreign Language in Higher Technical Institutions. European Journal of Contemporary Education, 12(2). https://doi.org/10.13187/ejced.2023.2.578

Ouyang, F., & Jiao, P. (2021). Artificial intelligence in education: The three paradigms. Computers and Education: Artificial Intelligence, 2. https://doi.org/10.1016/j.caeai.2021.100020

Pérez-Paredes, P. (2022). A systematic review of the uses and spread of corpora and data-driven learning in CALL research during 2011–2015. In Computer Assisted Language Learning (Vol. 35, Issues 1–2). https://doi.org/10.1080/09588221.2019.1667832

Pérez-Paredes, P., Ordoñana Guillamón, C., & Aguado Jiménez, P. (2018). Language teachers’ perceptions on the use of OER language processing technologies in MALL. Computer Assisted Language Learning, 31(5–6). https://doi.org/10.1080/09588221.2017.1418754

Qin, T., & Zhang, J. (2018). Computerized dynamic assessment and second language learning: Programmed mediation to promote future development. Journal of Cognitive Education and Psychology, 17(2). https://doi.org/10.1891/1945-8959.17.2.198

Ranalli, J. (2018). Automated written corrective feedback: how well can students make use of it? Computer Assisted Language Learning, 31(7). https://doi.org/10.1080/09588221.2018.1428994

Saricaoglu, A. (2019). The impact of automated feedback on L2 learners’ written causal explanations. ReCALL, 31(2). https://doi.org/10.1017/S095834401800006X

Schmidt, T., & Strasser, T. (2022). Artificial Intelligence in Foreign Language Learning and Teaching: A CALL for Intelligent Practice. Anglistik: International Journal of English Studies, 33(1).

Smutny, P., & Schreiberova, P. (2020). Chatbots for learning: A review of educational chatbots for the Facebook Messenger. Computers and Education, 151. https://doi.org/10.1016/j.compedu.2020.103862

Son, J.-B., Ružić, N. K., & Philpott, A. (2023). Artificial intelligence technologies and applications for language learning and teaching. Journal of China Computer-Assisted Language Learning. https://doi.org/10.1515/jccall-2023-0015

Sotomayor Cantos, K. F., Varas Giler, R. C., & Castro Magayanes, I. E. (2023). Artificial Intelligence In Language Teaching And Learning. Ciencia Latina Revista Científica Multidisciplinar, 7(4). https://doi.org/10.37811/cl_rcm.v7i4.7368

Spring, R., & Tabuchi, R. (2022). The Role of ASR Training in EFL Pronunciation Improvement: An In-depth Look at the Impact of Treatment Length and Guided Practice on Specific Pronunciation Points. CALL-EJ, 23(3).

Tai, T. Y., & Chen, H. H. J. (2023). The impact of Google Assistant on adolescent EFL learners’ willingness to communicate. Interactive Learning Environments, 31(3). https://doi.org/10.1080/10494820.2020.1841801

Tobing, M. B., SNA, F. I., Hayrunnisa, N. R., Haswuri, N. I. T., Sutarsyah, C., & Munifatullah, F. (2023). An exploration of Artificial Intelligence in English Language Teaching as a foreign language. International Journal of Social Science and Human Research, 06(06). https://doi.org/10.47191/ijsshr/v6-i6-78

Tono, Y., Satake, Y., & Miura, A. (2014). The effects of using corpora on revision tasks in L2 writing with coded error feedback. ReCALL, 26(2). https://doi.org/10.1017/S095834401400007X

Wang, J., Hwang, G. H., & Chang, C. Y. (2021). Directions of the 100 most cited chatbot-related human behavior research: A review of academic publications. In Computers and Education: Artificial Intelligence (Vol. 2). https://doi.org/10.1016/j.caeai.2021.100023

Xu, Z., Wijekumar, K., Ramirez, G., Hu, X., & Irey, R. (2019). The effectiveness of intelligent tutoring systems on K-12 students’ reading comprehension: A meta-analysis. In British Journal of Educational Technology (Vol. 50, Issue 6). https://doi.org/10.1111/bjet.12758

Yang, Y., & Qian, D. D. (2020). Promoting L2 English learners’ reading proficiency through computerized dynamic assessment. Computer Assisted Language Learning, 33(5–6). https://doi.org/10.1080/09588221.2019.1585882

Yin, N. (2021). Research on the Impacts of Artificial Intelligence Technology on Language Teaching Innovation. Frontiers in Educational Research, 4(7), 25–31. https://doi.org/10.25236/fer.2021.040706

Zein, S., Sukyadi, D., Hamied, F. A., & Lengkanawati, N. S. (2020). English language education in Indonesia: A review of research (2011-2019). In Language Teaching (Vol. 53, Issue 4). https://doi.org/10.1017/S0261444820000208

Zhai, N., & Ma, X. (2022). Automated writing evaluation (AWE) feedback: a systematic investigation of college students’ acceptance. Computer Assisted Language Learning, 35(9). https://doi.org/10.1080/09588221.2021.1897019

Zhai, X. (2023). ChatGPT User Experience: Implications for Education. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4312418

Zhang, J., & Lu, X. (2019). Measuring and supporting second language development using computerized dynamic assessment. Language and Sociocultural Theory, 6(SpecialIssue1). https://doi.org/10.1558/lst.31710

Published
2025-07-22
How to Cite
Sari, L. I., & Sari, R. H. (2025). AI INTEGRATION IN ENGLISH LANGUAGE TEACHING: INSIGHTS INTO ESP TEACHERS’ VIEWS AND READINESS. English Review: Journal of English Education, 13(2), 779-792. https://doi.org/10.25134/erjee.v13i2.11505
Issue
Vol. 13 No. 2 (2025)
Section
Articles

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