Large language models for foreign language acquisition
| dc.contributor.author | Cherednichenko, Olga | |
| dc.contributor.author | Yanholenko, Olha | |
| dc.contributor.author | Badan, A. A. | |
| dc.contributor.author | Onishchenko, N. | |
| dc.contributor.author | Akopiants, N. M. | |
| dc.date.accessioned | 2025-11-13T12:18:53Z | |
| dc.date.issued | 2024 | |
| dc.description.abstract | The study is the fourth in a row of consecutive papers on the impact of digital technologies on teaching/acquisition of foreign languages previously via multimedia, and presently via artificial intelligence tools. The joint effort of foreign language teachers teams from National Technical University “Kharkiv Polytechnic Institute” and Vasyl Karazin National University, Kharkiv, Ukraine paved the way for introducing communication simulation techniques into the domain of foreign language teaching / acquisiton as far back as the beginning of the covid pandemic in 2021 to be aggravated by the start of Russian war on Ukraine in 2022 which predominantly focused on distant online learning. The present paper hypothesized the wide use of AI platforms, or Large Language Models (LLM) used here alternatively, like ChatGPT, Bard, and the like on a par with the previously described multimedia techniques and blended traditional learning as efficient for the rapid and intensified skill acquisition. The approach proved completely true both in Listening/Speaking and Reading/Writing for students and the Maxims of effective communication introduced by H.P.Grice and adopted for teachers as 7 Cs (Clarity, Conciseness, Concreteness, Correctness, Cohereness, Completeness, and Courtesy). In addition, the benefits of using ChatGPT for training translators/interpreters proved invaluable for making their skills automated due to the abundance of material and new techniques. Some observations on the challenges and threats of LLM usage by students are also highlighted. As before, the two surveys meant for both students and teachers were proposed for completion at the start and the end of an academic semester, compared, and yielded the results described below. Furthermore, a detailed description of the AI tools used for the research was provided to show how blended learning in the modern digital era is being constantly supplemented with newly developed digital tools, specifically for foreign language learning. | |
| dc.identifier.citation | Large language models for foreign language acquisition [Electronic resource] / Olga Cherednichenko, Olha Yanholenko, Antonina Badan, Nataliia Onishchenko, Nunu Akopiants // CLW-2024: Computational Linguistics Workshop at 8th International Conference on Computational Linguistics and Intelligent Systems (CoLInS-2024), April 12–13, 2024, Lviv, Ukraine/ ed.: Nina Khairova, Victoria Vysotska. – Electronic text data. – Lviv, 2024. – P. 101-130. – URL: https://ceur-ws.org/Vol-3722/paper8.pdf, free (accessed 13.11.2025). | |
| dc.identifier.orcid | https://orcid.org/0000-0002-9391-5220 | |
| dc.identifier.orcid | https://orcid.org/0000-0001-7755-1255 | |
| dc.identifier.orcid | https://orcid.org/0000-0001-5898-4039 | |
| dc.identifier.orcid | https://orcid.org/0000-0002-9387-4991 | |
| dc.identifier.orcid | https://orcid.org/0000-0002-0709-9926 | |
| dc.identifier.uri | https://repository.kpi.kharkov.ua/handle/KhPI-Press/95172 | |
| dc.language.iso | en | |
| dc.publisher | CEUR-W | |
| dc.subject | large language models (LLM) | |
| dc.subject | artificial intelligence (AI) | |
| dc.subject | human-computer interaction (HCI) | |
| dc.subject | digital tools | |
| dc.subject | communication | |
| dc.subject | E-learning | |
| dc.subject | foreign language communicative competence | |
| dc.subject | translators' training | |
| dc.subject | communication simulation | |
| dc.subject | speech synthesis | |
| dc.subject | TTS and STT technologies | |
| dc.title | Large language models for foreign language acquisition | |
| dc.type | Article |
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