- Malykhin, Oleksandr Volodymyrovych (orcid.org/0000-0001-6042-6298), Aristova, Nataliia Oleksandrivna (orcid.org/0000-0002-0943-8039) and Dybkova, Liudmyla Mykolaiyvna (orcid.org/0000-0002-3920-118X) (2025) Using AI tools for personalising learning in the English Language classroom: computer science undergraduate students’ perceptions Environment. Technology. Resources. Proceedings of the 16th International Scientific and Practical Conference on June 19-20, 2025, 3. pp. 213-217. ISSN 2256-070X
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Abstract
Up-to-date higher education in general and foreign language training, in particular, require skilled university teachers and the possibility to use the latest scientific and technical achievements. Due to this, higher education institutions and their faculty are constantly searching for brand-new technologies, methods, and tools to train their students. English teachers are always at the forefront of utilising innovative information technologies since these technologies can effectively provide multimodal learning inside and outside the English language classroom. Artificial Intelligence is quickly becoming one of those advances whose appearance can profoundly change the teaching experience and make learning more personal. But until itproves its reliability in providing the appropriate education quality and developing students’ subject matter and key competencies for lifelong learning, scientific interest will continue to grow, and heated debates will rage on. The problem of using AI tools for personalising learning in the English Language classroom must be approached comprehensively, taking into account the views of researchers, stakeholders, policymakers, as wellas university teachers and students. Thus, this research aims to determine computer science undergraduate students’perceptions of using AI tools for personalising learning in the English Language classroom. To reach the aim, researchers developed a paper-based questionnaire that contained close-ended questions and statements for a rating on a 5-point Likert scale (5 –“strongly agree,” 4 –“agree,” 3 – “neither agree nor disagree,” 2 –“disagree,” 1 –“strongly disagree”). To select participants, the researchers used a purposive sampling method. As a result, the research population comprised 136 computer science undergraduate students from Kyiv National University of Technologies and Design (Kyiv, Ukraine) and Kyiv National Economic University named after Vadym Hetman (Kyiv, Ukraine). The survey was carried out in September-October 2024. The obtained data were analysed using frequency and mean percentage to interpret respondents’responses. The results showed that computer science undergraduate students unanimously believe that AI tools are highly effective at helping personalise learning in the English language classroom.
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