USING AI TOOLS FOR PERSONALISING LEARNING IN THE ENGLISH LANGUAGE CLASSROOM: COMPUTER SCIENCE UNDERGRADUATE STUDENTS’ PERCEPTIONS

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 it proves 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 well as 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.

Authors

Institutions

Publication Details

Journal
Environment Technology Resources Proceedings of the International Scientific and Practical Conference
Published
2025-06-08
DOI
https://doi.org/10.17770/etr2025vol3.8536
Primary Topic
Online Learning and Analytics
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

USING AI TOOLS FOR PERSONALISING LEARNING IN THE ENGLISH LANGUAGE CLASSROOM: COMPUTER SCIENCE UNDERGRADUATE STUDENTS’ PERCEPTIONS

Oleksandr Malykhin, Liudmyla Dybkova, Наталія Арістова
Environment Technology Resources Proceedings of the International Scientific and Practical Conference
Online Learning and Analytics
article

USING AI TOOLS FOR PERSONALISING LEARNING IN THE ENGLISH LANGUAGE CLASSROOM: COMPUTER SCIENCE UNDERGRADUATE STUDENTS’ PERCEPTIONS

Oleksandr Malykhin, Liudmyla Dybkova, Наталія Арістова
article en

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 it proves 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 well as 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.

Environment Technology Resources Proceedings of the International Scientific and Practical ConferenceVol. 3
National Academy of Educational Sciences of Ukraine (UA), Kyiv National Economic University named after Vadym Hetman (UA), Institute of International Relations Prague (CZ)
Openalex Percentile: Top 87%
Online Learning and Analytics
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.