Using NLP Tools to Improve Pre-Service Teachers’ English Skills to Cover the Language Demands of Math CLIL Course

The article highlights implementation of Natural Language Processing (NLP) specifically spaCy, to analyze the linguistic complexity and grammatical characteristics of the Russian-English Math CLIL course text. Collaboration between subject and language teachers in planning and delivering CLIL lessons causes the need to develop new methods for selecting and delivering educational content in pre-service teacher training in order to improve pre-service teachers’ English language proficiency and academic writing skills. The methodology section describes the research design, including the use of NLP tools such as spaCy for processing and analyzing text data. The results section presents the findings of the study, including the verb tense ratio, sentence mood ratio, active/passive voice ratio, and sentence type distribution. The article concludes by emphasizing the importance of bilingual and multilingual education, the necessity for bilingual teacher training, and the potential of NLP tools in enhancing language instruction in CLIL contexts.