Leveraging digital technologies for English phraseology research

Abstract

This article explores the transformative potential of digital technologies in researching and teaching English phraseology, with a focus on idiomatic expressions in specialized contexts such as Power Engineering and Electromechanics. Traditional methods for analysing phraseological units – fixed expressions that often carry non-literal meanings – have been limited by manual processes, subjectivity, and scalability issues, particularly in handling the vast, evolving data technical English texts. Drawing on corpus linguistics, natural language processing (NLP), and AI-driven tools like BERT models, this study addresses these challenges by proposing hybrid methodologies for systematic identification, contextual analysis, and educational application of English idioms. The research involved 78 second-year students the Faculty of Power Engineering and Electromechanics over a six-month period, who participated voluntarily with informed consent, ensuring all data remained confidential and anonymized. Through digital platforms, we extracted phraseological patterns corpora like the British National Corpus, the English Web 2021 (enTenTen21), alongside linguistic software tool such as Sketch Engine and specialized engineering databases, then integrated interactive AI modules for real-time learning. Key examples include idioms such as \"get your wires crossed\" (misunderstanding in electrical troubleshooting), \"power up\" (activating systems metaphorically), and \"on the same wavelength\" (synchronization in signal processing), which were analysed for polysemy and applied in gamified quizzes and VR simulations. Quantitative results paired t-tests showed significant gains (p < 0.01) in comprehension and productive use, with error rates in idiom misapplication dropping 28% to 9%, and student satisfaction averaging 4.2/5. Qualitatively, the approach mitigated contextual oversights, enhancing lexical diversity and cultural awareness. Ultimately, this integration bridges theoretical linguistics with practical applications in NLP, translation, and ESP education, offering a replicable framework for advancing phraseology studies in a digitized world. Future prospects include expanding to generative AI for predictive modelling and addressing ethical biases in corpus data.