Artificial Intelligence for the Foreign Language Classes at Higher Education Institution

Background. The article considers the possibility of artificial intelligence technologies application to the foreign language course in higher education. The novelty of constantly changing technologies, which are not sufficiently described in the scientific literature makes the issue relevant. Objectives. The aim of the work is to analyse the possibilities of AI application for foreign language teaching in higher education taking into account the functions of available information and communication tools. The theoretical significance of the work lies in the description of AI tools for classroom and autonomous language learning. Methods. The methods of the work include bibliographic analysis of publications on the research topic and an experiment on the introduction of AI tools into foreign language classes during 2022–2024. There were carried out observations and continuous analyses of existing free AI tools that are available in Russia and do not require special technical skills. Results. Based on the experience of practical work with students and analysis of the possibilities of different types of tools, the article presents the selected AI tools that can be applied by teachers at foreign language classes at the university. Conclusions. Conclusions are drawn about the key options for applying AI in the classroom. AI may help 1) adapt and personalise learning; 2) automate testing and revision; 3) generate and verify learning materials; 4) develop linguo-cultural skills, speech competence and creative writing; 5) become an integral part of digital and meta-skills of both teacher and student.

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Publication Details

Journal
SHILAP Revista de lepidopterología
Published
2025-03-01
DOI
https://doi.org/10.55959/lpej-25-07
Primary Topic
Artificial Intelligence in Education
Type
article
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0.00
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Artificial Intelligence for the Foreign Language Classes at Higher Education Institution

SHILAP Revista de lepidopterología
Artificial Intelligence in Education
article

Artificial Intelligence for the Foreign Language Classes at Higher Education Institution

article en

Abstract

Background. The article considers the possibility of artificial intelligence technologies application to the foreign language course in higher education. The novelty of constantly changing technologies, which are not sufficiently described in the scientific literature makes the issue relevant. Objectives. The aim of the work is to analyse the possibilities of AI application for foreign language teaching in higher education taking into account the functions of available information and communication tools. The theoretical significance of the work lies in the description of AI tools for classroom and autonomous language learning. Methods. The methods of the work include bibliographic analysis of publications on the research topic and an experiment on the introduction of AI tools into foreign language classes during 2022–2024. There were carried out observations and continuous analyses of existing free AI tools that are available in Russia and do not require special technical skills. Results. Based on the experience of practical work with students and analysis of the possibilities of different types of tools, the article presents the selected AI tools that can be applied by teachers at foreign language classes at the university. Conclusions. Conclusions are drawn about the key options for applying AI in the classroom. AI may help 1) adapt and personalise learning; 2) automate testing and revision; 3) generate and verify learning materials; 4) develop linguo-cultural skills, speech competence and creative writing; 5) become an integral part of digital and meta-skills of both teacher and student.

SHILAP Revista de lepidopterología
Financial University (RU)
Quality Education
Openalex Percentile: Top 41%
Artificial Intelligence in Education
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Artificial Intelligence for the Foreign Language Classes at Higher Education Institution · SHILAP Revista de lepidopterología (2025) | TGRS Research Map | TGRS