Generative artificial intelligence as a tool for differentiated English language learning in higher education: task adaptation according to CEFR A2–B2 levels

Мельник, Оксана Володимирівна та Кисельова, Ірина Іллівна та Терещук, Марія Олександрівна (2026) Generative artificial intelligence as a tool for differentiated English language learning in higher education: task adaptation according to CEFR A2–B2 levels Академічні візії, 10 (59). с. 1-10. ISSN 2786-586X

Текст (Generative artificial intelligence as a tool for differentiated English language learning in higher education: task adaptation according to CEFR A2–B2 levels) Melnyk_Kyselova_Tereshchuk_AV_КАМК.pdf Download (104kB)

Анотація

The article substantiates the pedagogical potential of generative artificial intelligence (GenAI) as a tool for differentiated English language learning in higher education. The relevance of the study is determined by the heterogeneity of students’ language proficiency, the need to adapt instructional materials to individual learning needs, and the rapid expansion of generative models in digital educational environments. Rather than treating artificial intelligence primarily as a tool for automatic text generation or translation, the study conceptualizes GenAI as a pedagogical instrument for controlled adaptation of learning tasks to A2, B1, and B2 proficiency levels according to the Common European Framework of Reference for Languages (CEFR). The methodological framework combines principles of differentiated and personalized learning, the competence-based approach, CEFR descriptors, task-based language teaching, and recent research on GenAI in language education. The study applies analysis, synthesis, comparison, systematization and generalization of scholarly sources, pedagogical modelling, and content analysis of examples of AI-supported tasks. The main result is an author-developed AI-supported differentiation model comprising learner-level diagnosis, definition of the learning objective, prompt design, adaptation of vocabulary, grammar, task length and cognitive complexity, student performance, AI-supported feedback, teacher verification, and reflection. The analysis demonstrates that GenAI is most pedagogically appropriate when it supports rather than replaces the teacher: it can facilitate the preparation of multi-level materials, provide scaffolding, vary instructions, model professional communication situations, and support formative feedback. The study identifies key conditions for effective GenAI integration: clearly defined learning outcomes, alignment with CEFR, gradual increase in complexity, AI and digital literacy of teachers and students, mandatory verification of AI-generated output, academic integrity, and data protection. The scholarly contribution lies in integrating differentiated instruction, CEFR-oriented task design, and generative AI within a single pedagogical model. The practical value of the study is associated with the possibility of applying the model in English language courses for non-linguistic majors, including English for Specific Purposes (ESP), Moodle-based learning, and other digital environments.

Тип елементу :	Стаття
Ключові слова:	generative artificial intelligence; ChatGPT; differentiated learning; personalization; English language; CEFR; digitalization; higher education; English for Specific Purposes; task adaptation
Типологія:	Статті у періодичних виданнях > Фахові (входять до переліку фахових, затверджений МОН)
Підрозділи:	Факультет романо-германської філології > Кафедра англійської мови та комунікації
Користувач, що депонує:	Оксана Володимирівна Мельник
Дата внесення:	03 Жов 2026 15:43
Останні зміни:	03 Жов 2026 15:43
URI:	https://elibrary.kubg.edu.ua/id/eprint/59665

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