INTEGRATING APPLIED LINGUISTICS AND LANGUAGE STUDIES: FOREIGN EXPERIENCE AND IMPLICATIONS FOR UKRAINE
Bibliographic record
Abstract
The integration of applied linguistics and language studies is a key factor in enhancing the quality and relevance of language education, particularly in countries undergoing educational transformation such as Ukraine. As global trends shift toward learner-centred, evidence-based education, applied linguistics offers the tools to bridge theoretical insights with classroom realities. This article explores international experience of applying linguistic theory to educational practice and evaluates their potential for improving Ukraine’s language educational system. Drawing on examples from the UK, Finland, the Netherlands, Canada, and others, the study highlights best practices in curriculum development, teacher training, bilingual education, assessment, and language policy. The research adopts a qualitative comparative methodology, analyzing relevant scholarly literature. The findings reveal that successfully integrating applied linguistics in foreign contexts fosters communicative, inclusive, and research-informed language instruction. For Ukraine, aligning language curricula with applied linguistic principles can enhance communicative competence and learner autonomy. Teacher education programmes should emphasize practical skills such as classroom research, materials design, and assessment literacy. Expanding the use of Content and Language Integrated Learning (CLIL) and English as a Medium of Instruction (EMI) can promote bilingualism and interdisciplinary learning. Moreover, language assessment practices need to reflect real-world use, and language policy should be informed by sociolinguistic research to ensure inclusivity and cultural sensitivity. These insights point to the value of a systematic, research-grounded approach to educational innovation. It is determined that by strategically integrating applied linguistics into language education, Ukraine can enhance the quality, relevance, and global competitiveness of its educational system, ultimately contributing to broader social and cultural development. The article concludes with recommendations and prospects for further research to contextualise foreign experience to support systemic reform in Ukraine’s language education landscape.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.009 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.014 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".