• Lazar Stоšić Faculty of Management, Sremski Karlovci, University UNION Nikola Tesla, Belgrade, Serbia. Don State Technical University, Rostov-on-Don, Russian Federation
  • Elena N. Malyuga Head of Foreign Languages Department at the Faculty of Economics, Peoples’ Friendship University of Russia named after Patrice Lumumba (RUDN University), Moscow, Russian Federation



The integration of artificial intelligence (AI) into language testing marks a transformative shift in how we evaluate linguistic capabilities. AI-driven tools offer unparalleled precision, personalization, and innovative assessment methods, revolutionizing language assessment’s accuracy and adaptability. Through advanced algorithms and tailored approaches, AI ensures precise skill evaluation, customized testing, and individualized improvement strategies, promising a more effective language learning experience. However, alongside these advancements, ethical considerations arise concerning data privacy, algorithmic biases, and operational transparency in AI-based language testing. Striking a balance between technological innovation and ethical implications becomes paramount for harnessing AI’s potential in enhancing language skills while addressing ethical concerns. Despite these challenges, the future of language testing with AI appears promising. As AI continues to evolve, its role in language assessment is poised to revolutionize educational practices and evaluation methodologies. With a conscientious approach to ethical considerations and continuous technological development, AI holds the promise of significantly enhancing language proficiency and learning processes in the foreseeable future.


Akopova, A. S. (2023). English for Specific Purposes: Tailoring English language instruction for history majors. Training, Language and Culture, 7(3), 31-40.

Bakhodirov, O., & Rahmanova, G. (2023, October). The role of AI in language learning apps. In International Conference on Higher Education Teaching (Vol. 1, No. 11, pp. 9-12).

Fryer, L. & Carpenter, R. (2006). Bots as language learning tools. Language Learning & Technology, 10(3), 8-14.

Godwin-Jones, R. (2001). Language testing tools and technologies. Language Learning & Technology, 5(2), 8-13.

Gorozhanov, A. I., & Guseynova, I. A. (2020). Programming for specific purposes in linguistics: A new challenge for the humanitarian curricula. Training, Language and Culture, 4(4), 23-38.

Grishechko, E. G. (2023). Language and cognition behind simile construction: A Python-powered corpus research. Training, Language and Culture, 7(2), 80-92.

Grünhage-Monetti, M. (2020). Which competences for whom? Supporting the supporters of work-related L2 development by migrants and ethnic minorities. Training, Language and Culture, 4(4), 62-77.

Huang, C., Zhang, Z., Mao, B., & Yao, X. (2023). An overview of artificial intelligence ethics. IEEE Transactions on Artificial Intelligence, 4(4), 799-819.

Indari, A. (2023). The detection of pronunciation errors in English speaking skills based on artificial intelligence (AI): Pronunciation, English speaking skills, AI, ELSA application. Jurnal Serunai Bahasa Inggris, 15(2).

Li, X., & Zhang, T. (2017, April). An exploration on artificial intelligence application: From security, privacy and ethic perspective. In 2017 IEEE 2nd International Conference on Cloud Computing and Big Data Analysis (ICCCBDA) (pp. 416-420). IEEE.

Lukácsi, Z. (2020). Standard setting and internal validation of a novel approach adopted for assessing speaking. Training, Language and Culture, 4(4), 9-22.

Malyuga, E. N. (2019). Functional approach to professional discourse exploration in linguistics. Springer.

Malyuga, E. N. (2023). A corpus-based approach to corporate communication research. Russian Journal of Linguistics, 27(1), 152-172.

Malyuga, E. N. & Akopova, A. S. (2021). Precedence-setting tokens: Issues of classification and functional attribution. Training, Language and Culture, 5(4), 65-76.

Malyuga, E. N., Shvets, A., & Tikhomirov, I. (2016). Computer-based analysis of business communication language. In Proceedings of 2016 SAI Computing Conference SAI 2016 (pp. 229-232). IEEE.

Noviyanti, S. D. (2020). Artificial intelligence (AI)-based pronunciation checker: An alternative for independent learning in pandemic situation. ELT Echo: The Journal of English Language Teaching in Foreign Language Context, 5(2), 162-169.

Pokrivcakova, S. (2019). Preparing teachers for the application of AI-powered technologies in foreign language education. Journal of Language and Cultural Education, 7(3), 135-153.

Richardson, M., & Clesham, R. (2021). Rise of the machines? The evolving role of Artificial Intelligence (AI) technologies in high stakes assessment. London Review of Education, 19(1), 1-13.

Sibul, V. V., Vetrinskaya, V. V., & Grishechko, E. G. (2019). Study of precedent text pragmatic function in modern economic discourse. In E. N. Malyuga (Ed.), Functional approach to professional discourse exploration in linguistics (pp. 131-163). Springer.

Stošić, L. (2015). The importance of educational technology in teaching. International Journal of Cognitive Research in Science, Engineering and Education (IJCRSEE), 3(1), 111-114.

Stošić, L., & Janković, A. (2023). The impact of artificial intelligence (AI) on education – balancing advancements and ethical considerations on human rights. Pravo - Teorija i Praksa, 40(4), 58–72.

Turing, A. M. (1950). Computing machinery and intelligence. Mind, 59(236), 433-460.

Wang, Z. (2022). Assessing intercultural competence using videotapes: A comparison study of home students’ performance. Training, Language and Culture, 6(2), 9-19.

Weizenbaum, J. (1966). ELIZA: A computer program for the study of natural language communication between man and machine. Communications of the ACM, 9(1), 36-45.

Winograd, T. (1972). Understanding natural language. Addison Wesley.

Zhang, Y., Wu, M., Tian, G. Y., Zhang, G., & Lu, J. (2021). Ethics and privacy of artificial intelligence: Understandings from bibliometrics. Knowledge-Based Systems, 222, Article 106994.

Zou, B., Liviero, S., Wei, K., Sun, L., Qi, Y., Yang, X., & Fu, J. (2021). Case study 11, Mainland China: The impact of pronunciation and accents in artificial intelligence speech evaluation systems. Language Learning with Technology: Perspectives from Asia, 223-235.




How to Cite

Lazar Stоšić, & Elena N. Malyuga. (2024). APPLICATION OF ARTIFICIAL INTELLIGENCE IN LANGUAGE SKILLS TESTING. ANGLISTICUM. Journal of the Association-Institute for English Language and American Studies, 13(1), 22–34.



Volume 13, No.1, February 2024