Fostering digital competence among Philology students in Higher Education
DOI:
https://doi.org/10.46502/issn.1856-7576/2026.20.03.14Keywords:
digital competence, philology students, artificial intelligence, higher education, and pedagogical intervention.Abstract
The digital transformation of higher education has increased the need to develop digital competence among future philologists. This study aimed to evaluate the effectiveness of a pedagogical system for developing digital competence in philology students through a quasi-experimental intervention. The research employed a quasi-experimental design involving 126 undergraduate philology students, including an experimental group (n = 62) and a control group (n = 64). The intervention included three stages: ascertaining, formative, and control. Data were collected using a structured Digital Competence Questionnaire developed on the basis of the European Digital Competence Framework for Citizens (DigComp 2.2). Digital competence was evaluated according to three dimensions (motivational, content, and activity), and Pearson's χ² test was used to determine the statistical significance of the observed between-group differences. The findings demonstrated that the proposed pedagogical system significantly improved students' digital competence in the experimental group compared with the control group. According to the motivational criterion, the proportion of students with a high level of digital competence increased from 8.1% to 40.3%, while the proportion with a low level decreased from 40.3% to 4.8%. Similar positive changes were observed for the content criterion (high level: 8.1% to 41.9%; low level: 38.7% to 4.8%) and the activity criterion (high level: 9.7% to 50.0%; low level: 50.0% to 4.8%). Statistical analysis confirmed the significance of the observed improvements. The study concludes that integrating digital educational resources, artificial intelligence technologies, and interactive pedagogical approaches effectively enhances the digital competence of philology students.
References
Beatty, K. (2013). Teaching and researching computer-assisted language learning (2nd ed.). Routledge.
Boulton, A., & Cobb, T. (2017). Corpus use in language learning: A meta-analysis. Language Learning, 67(2), 348–393. https://doi.org/10.1111/lang.12224
Bowker, L., & Fisher, D. (2010). Computer-aided translation. In Y. Gambier & L. van Doorslaer (Eds.), Handbook of translation studies (Vol. 1, pp. 60–65). John Benjamins Publishing Company. https://benjamins.com/online/hts/articles/comp2
Díaz-Noguera, M. D., Hervás-Gómez, C., De la Calle-Cabrera, A. M., & López-Meneses, E. (2022). Autonomy, motivation, and digital pedagogy are key factors in the perceptions of Spanish higher-education students toward online learning during the COVID-19 pandemic. International Journal of Environmental Research and Public Health, 19(2), 654. https://doi.org/10.3390/ijerph19020654
Falloon, G. (2020). From digital literacy to digital competence: The teacher digital competency (TDC) framework. Educational Technology Research and Development, 68(5), 2449–2472. https://doi.org/10.1007/s11423-020-09767-4
Haleem, A., Javaid, M., Qadri, M. A., & Suman, R. (2022). Understanding the role of digital technologies in education: A review. Sustainable Operations and Computers, 3, 275–285. https://doi.org/10.1016/j.susoc.2022.05.004
Hazari, S. (2024). Justification and roadmap for artificial intelligence (AI) literacy courses in higher education. Journal of Educational Research and Practice, 14(1), 106–118. https://doi.org/10.5590/JERAP.2024.14.1.07
Horváthová, B. (2023). Philological study programs in the digital age: A comprehensive analysis of Moodle integration and utilization. Journal of Language and Cultural Education, 11(2), 77–85. https://doi.org/10.2478/jolace-2023-0017
Hubbard, P., & Levy, M. (2016). Theory in computer-assisted language learning research and practice. In F. Farr & L. Murray (Eds.), The Routledge handbook of language learning and technology (pp. 24–38). Routledge. https://doi.org/10.4324/9781315657899
Kasneci, E., Seßler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., ... & Kasneci, G. (2023). ChatGPT for good? On opportunities and challenges of large language models for education. Learning and Individual Differences, 103, 102274. https://doi.org/10.1016/j.lindif.2023.102274
McEnery, T., & Hardie, A. (2011). Corpus linguistics: Method, theory and practice. Cambridge University Press.
Ng, D. T. K., Leung, J. K. L., Su, J., Ng, R. C. W., & Chu, S. K. W. (2023). Teachers’ AI digital competencies and twenty-first century skills in the post-pandemic world. Educational Technology Research and Development, 71(1), 137–161. https://doi.org/10.1007/s11423-023-10203-6
O'Hagan, M. (2019). The Routledge Handbook of Translation and Technology. Routledge.
Redecker, C. (2017). European framework for the digital competence of educators: DigCompEdu. Publications Office of the European Union. https://doi.org/10.2760/159770
Tlili, A., Shehata, B., Adarkwah, M. A., Bozkurt, A., Hickey, D. T., Huang, R., & Agyemang, B. (2023). What if the devil is my guardian angel: ChatGPT as a case study of using chatbots in education. Smart Learning Environments, 10, 15. https://link.springer.com/article/10.1186/s40561-023-00237-x
Vuorikari, R., Kluzer, S., & Punie, Y. (2022). DigComp 2.2: The digital competence framework for citizens—With new examples of knowledge, skills and attitudes. Publications Office of the European Union. https://doi.org/10.2760/115376
Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education. International Journal of Educational Technology in Higher Education, 16, 39. https://doi.org/10.1186/s41239-019-0171-0
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Veronika Titarchuk, Liubomyra Iliichuk, Svitlana Vakulenko

This work is licensed under a Creative Commons Attribution 4.0 International License.












