Beyond Digital Access: Generative Artificial Intelligence and Digital Literacy Predict Vocational Students’ Academic Writing
DOI:
https://doi.org/10.59211/mjpjetl.v4i1.392Keywords:
academic writing skills, digital literacy, generative artificial intelligence, vocational education, vocational studentsAbstract
This study examined the partial and simultaneous statistical effects of digital literacy and responsible generative artificial intelligence utilization on vocational students’ academic writing skills. A quantitative explanatory-correlational approach with a cross-sectional survey design was employed. The sample comprised 100 students from five vocational study programs selected through proportionate stratified random sampling. Data were collected using two 10-item Likert questionnaires measuring digital literacy and generative artificial intelligence utilization, together with a source-based academic essay performance test assessed using an analytic rubric. Pearson correlation and multiple linear regression were applied. Digital literacy had a positive and significant statistical effect on academic writing skills (B = 6.413; β = 0.334; p < 0.001). Generative artificial intelligence utilization also had a positive and significant effect and provided a stronger relative contribution (B = 9.097; β = 0.494; p < 0.001). Simultaneously, both predictors produced F(2,97) = 42.013; p < 0.001 and explained 46.4% of the variance. These findings indicated that vocational students’ academic writing development required the integration of digital literacy and critical, ethical, and responsible artificial intelligence practices in writing instruction and institutional policy.
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[1] D. Ravšelj, D. Keržič, N. Tomaževič, L. Umek, N. Brezovar, et al., “Higher education students’ perceptions of ChatGPT: A global study of early reactions,” PLOS ONE, vol. 20, no. 2, Art. no. e0315011, 2025, doi: 10.1371/journal.pone.0315011.
[2] Y. Zhao, A. M. Pinto Llorente, and M. C. Sánchez Gómez, “Digital competence in higher education research: A systematic literature review,” Computers & Education, vol. 168, Art. no. 104212, 2021, doi: 10.1016/j.compedu.2021.104212.
[3] S. Nabhan and A. Habók, “The Digital Literacy Academic Writing Scale: Exploratory factor analysis,” SAGE Open, vol. 15, no. 1, pp. 1-13, 2025, doi: 10.1177/21582440241311709.
[4] V. Indriyani, F. O. Zuve, H. Triana, and A. Rachman, “The correlation between collaboration and digital literacy skill on students’ academic writing skills,” The Journal of Educators Online, vol. 22, no. 4, 2025, doi: 10.9743/JEO.2025.22.4.6.
[5] Suharno, N. A. Pambudi, and B. Harjanto, “Vocational education in Indonesia: History, development, opportunities, and challenges,” Children and Youth Services Review, vol. 115, Art. no. 105092, 2020, doi: 10.1016/j.childyouth.2020.105092.
[6] S. Salsabila and N. S. Lengkanawati, “Digital literacy and academic writing in the age of artificial intelligence: Evidence from undergraduate students,” International Journal of Language Teaching and Education, vol. 10, no. 1, pp. 401-416, 2026.
[7] Marzuki, U. Widiati, D. Rusdin, Darwin, and Indrawati, “The impact of artificial intelligence writing tools on the content and organization of students’ writing: English as a Foreign Language teachers’ perspective,” Cogent Education, vol. 10, no. 2, Art. no. 2236469, 2023, doi: 10.1080/2331186X.2023.2236469.
[8] S. Mahapatra, “Impact of ChatGPT on English as a Second Language students’ academic writing skills: A mixed methods intervention study,” Smart Learning Environments, vol. 11, Art. no. 9, 2024, doi: 10.1186/s40561-024-00295-9.
[9] A. Nguyen, Y. Hong, B. Dang, and X. Huang, “Human-artificial intelligence collaboration patterns in artificial intelligence-assisted academic writing,” Studies in Higher Education, vol. 49, no. 5, pp. 847-864, 2024, doi: 10.1080/03075079.2024.2323593.
[10] C. K. Y. Chan and W. Hu, “Students’ voices on generative artificial intelligence: Perceptions, benefits, and challenges in higher education,” International Journal of Educational Technology in Higher Education, vol. 20, Art. no. 43, 2023, doi: 10.1186/s41239-023-00411-8.
[11] D. Lakens, “Sample size justification,” Collabra: Psychology, vol. 8, no. 1, Art. no. 33267, 2022, doi: 10.1525/collabra.33267.
[12] D. T. K. Ng, W. Wu, J. K. L. Leung, T. K. F. Chiu, and S. K. W. Chu, “Design and validation of the artificial intelligence literacy questionnaire: The affective, behavioural, cognitive and ethical approach,” British Journal of Educational Technology, vol. 55, no. 3, pp. 1082-1104, 2024, doi: 10.1111/bjet.13411.
[13] L. S. Lambert and D. A. Newman, “Construct development and validation in three practical steps: Recommendations for reviewers, editors, and authors,” Organizational Research Methods, vol. 26, no. 4, pp. 574-607, 2023, doi: 10.1177/10944281221115374.
[14] A. F. Hayes and J. J. Coutts, “Use omega rather than Cronbach’s alpha for estimating reliability. But…,” Communication Methods and Measures, vol. 14, no. 1, pp. 1-24, 2020, doi: 10.1080/19312458.2020.1718629.
[15] D. Tumin, M. Hayney, and R. P. Winsett, “Describing and presenting multivariable regression models,” Progress in Transplantation, vol. 30, no. 4, pp. 303-305, 2020, doi: 10.1177/1526924820959774.
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Copyright (c) 2026 Ria Ria Candra Dewi, Aziz Azindani, Lily Budinurani, Novita Wulandari
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