Digital Literacy and Attitudes toward Artificial Intelligence as Predictors of Online Learning Preferences
Article Sidebar
Main Article Content
บทคัดย่อ
This study aimed to examine how digital literacy and attitudes toward artificial intelligence (AI) predict undergraduate management students’ online learning preferences at a public university in Thailand. Using a quantitative research design, data were collected from 177 participants through a structured questionnaire. The instruments included three scales measuring digital literacy, attitudes toward AI, and online learning preferences, each showing satisfactory reliability (α = .832-.866). Descriptive statistics, Pearson correlation, and multiple linear regression analyses were used. The results indicated that both digital literacy and attitudes toward AI significantly predicted online learning preferences (R² = .30, p < .001), with digital literacy serving as the stronger predictor (β = .423, p < .001). These findings suggest that students with stronger digital skills are better at adapting to and engaging in online learning. Simultaneously, positive attitudes toward AI also contribute to technology adoption. Although the model explained about 30% of the variance, it provides empirical evidence that incorporating digital and AI literacy is a crucial aspect of online learning readiness. Practically, the results suggest that universities should strengthen digital literacy by integrating embedded course modules and AI-awareness workshops. This study contributes to the global discourse on digital readiness and AI integration in higher education, offering insights for educators and policymakers seeking to enhance technology-mediated learning.
Article Details
อนุญาตภายใต้เงื่อนไข Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
เนื้อหาและข้อมูลในบทความที่ลงตีพิมพ์ในวารสารวิชาการมหาวิทยาลัยราชภัฏภูเก็ต ถือเป็นข้อคิดเห็นและความรับผิดชอบของผู้เขียนบทความโดยตรง ซึ่งกองบรรณาธิการวารสารฯ ไม่จำเป็นต้องเห็นด้วยหรือร่วมรับผิดชอบใด ๆ
บทความ ข้อมูล เนื้อหา รูปภาพ ฯลฯ ที่ได้รับการตีพิมพ์ในวารสารวิชาการมหาวิทยาลัยราชภัฏภูเก็ต ถือเป็นลิขสิทธิ์ของวารสารวิชาการมหาวิทยาลัยราชภัฏภูเก็ต หากบุคคลหรือหน่วยงานใดต้องการนำทั้งหมดหรือส่วนหนึ่งส่วนใดไปเผยแพร่ต่อหรือเพื่อกระทำการใด ๆ จะต้องได้รับอนุญาตเป็นลายลักษณ์อักษรจากวารสารวิชาการมหาวิทยาลัยราชภัฏภูเก็ตก่อนเท่านั้น
เอกสารอ้างอิง
Acosta-Enriquez, B. G., Arbulú Pérez Vargas, C. G., Huamaní Jordan, O., Arbulú Ballesteros, M. A., & Paredes Morales, A. E. (2024). Exploring attitudes toward ChatGPT among college students: An empirical analysis of cognitive, affective, and behavioral components
using path analysis. Computers and Education: Artificial Intelligence, 7, 100320. https://doi.org/10.1016/j.caeai.2024.100320
Aktay, S., Gök, S., & Yıldırım, A. (2024). Artificial intelligence attitude scale. International Technology and Education Journal, 8(2), 14–24. https://itejournal.com/articles/artificial-intelligence-attitude-scale.pdf
Alanoglu, M., Karabatak, S., & Yang, H. (2026). Understanding university students’ self-directed online learning in the context of emergency remote teaching: The role of online learning readiness and digital literacy. Journal of Computing in Higher Education, 38, 651–677. https://doi.org/10.1007/s12528-025-09458-0
American Psychological Association. (2020). Publication manual of the American Psychological Association (7th ed.). American Psychological Association.
Anderson, T. D., Ogruk-Maz, G., & Bell, T. J., III. (2024). Enhancing digital literacy in higher education: A comprehensive analysis of digital skill development among college students. Journal of Higher Education Theory and Practice, 25(3), 63–81. https://doi.org/10.33423/jhetp.v25i3.7631
Best, J. W. (1977). Research in education (3rd ed.). Prentice-Hall.
British Educational Research Association. (2018). Ethical guidelines for educational research (4th ed.). https://www.bera.ac.uk/publication/ethical-guidelines-for-educational-research-2018-online
Chen, F. (2025). The relationship between digital literacy and college students’ academic achievement: The chain mediating role of learning adaptation and online self-regulated learning. Frontiers in Psychology, 16, 1590649. https://doi.org/10.3389/fpsyg.2025.1590649
Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Lawrence Erlbaum Associates.
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008
Deci, E. L., & Ryan, R. M. (2000). The “what” and “why” of goal pursuits: Human needs and the self-determination of behavior. Psychological Inquiry, 11(4), 227–268. https://doi.org/10.1207/S15327965PLI1104_01
Demirel, S. (2022). Developing students’ attitude scale for the online education. International Journal of Modern Education Studies, 6(2), 448–469. https://doi.org/10.51383/ijonmes.2022.239
El-Shara’, I. A., Saeed, A. S., & Arouri, Y. M. (2025). University students’ awareness and attitudes toward the use of artificial intelligence applications (AIAs) in learning: A descriptive study. International Journal of Information and Education Technology,
(3), 539–548. https://doi.org/10.18178/ijiet.2025.15.3.2264
Erdfelder, E., Faul, F., & Buchner, A. (1996). G-POWER: A general power analysis program. Behavior Research Methods, Instruments, & Computers, 28(1), 1–11. https://doi.org/10.3758/BF03203630
Etikan, I., & Bala, K. (2017). Sampling and sampling methods. Biometrics & Biostatistics International Journal, 5(6), 00149. https://doi.org/10.15406/bbij.2017.05.00149
Gautam, P. (2022, October 10). Advantages and disadvantages of online learning. eLearning Industry. https://elearningindustry.com/advantages-and-disadvantages-online-learning
Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2019). Multivariate data analysis (8th ed.). Cengage Learning.
Hermosura, L. (2025). Analysis of college students' attitude towards using AI in their academic works. World Journal of Advanced Research and Reviews, 26(1), 3634–3639. https://doi.org/10.30574/wjarr.2025.26.1.1527
Irpan, R. M., Roesminingsih, M. V., & Jacky, M. (2023). The influence of digital literacy in online learning on student learning outcomes. Studies in Philosophy of Science and Education, 4(2), 88–93. https://doi.org/10.46627/sipose.v4i2.285
Katsantonis, A., & Katsantonis, I. G. (2024). University students’ attitudes toward artificial intelligence: An exploratory study of the cognitive, emotional, and behavioural dimensions of AI attitudes. Education Sciences, 14(9), 988. https://doi.org/10.3390/educsci14090988
Kayyali, M. (2024). Digital literacy in higher education: Preparing students for the workforce of the future. International Journal of Information Science and Computing, 11(1), 53–73. https://doi.org/10.30954/2348-7437.1.2024.6
Liu, X., Wu, J., Li, B., Guo, L., & Ye, B. (2024). Digital literacy and online learning satisfaction among junior high school students: A moderated mediation model. South African Journal of Education, 44(4), 1–10. https://doi.org/10.15700/saje.v44n4a2562
Ng, W. (2012). Can we teach digital natives digital literacy? Computers & Education, 59(3), 1065–1078. https://doi.org/10.1016/j.compedu.2012.04.016
Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric theory (3rd ed.). McGraw-Hill.
Nurteti, L., Pardosi, V. B. A., Maq, M. M., Hanim, S., & Tampubolon, J. (2024). Analysis of factors that influence student preferences for online learning. Journal Emerging Technologies in Education, 2(1), 61–71. https://doi.org/10.70177/jete.v2i1.745
Padohinog, E., & Balsicas, N. (2023). College students’ preferences and perceptions of online learning activities in a private school in Cavite. Journal of Science and Education (JSE), 3(3), 264–273. https://doi.org/10.56003/jse.v3i3.233
Promsri, C. (2019). The association between digital literacy and social intelligence. International Journal of English, Literature and Social Science (IJELS), 4(8), 1674–1678. https://ijels.com/detail/the-association-between-digital-literacy-and-social-intelligence
Rovinelli, R. J., & Hambleton, R. K. (1977). On the use of content specialists in the assessment of criterion-referenced test item validity. Dutch Journal of Educational Research, 2(2), 49–60.
Sun, A., & Chen, X. (2016). Online education and its effective practice: A research review. Journal of Information Technology Education: Research, 15, 157–190. https://doi.org/10.28945/3502
Tabachnick, B. G., & Fidell, L. S. (2019). Using multivariate statistics (7th ed.). Pearson.
UNESCO Institute for Statistics. (2021). Digital literacy (Glossary definition). https://uis.unesco.org/en/glossary-term/digital-literacy
Vaněček, D., Dobrovská, D., & Yorulmaz, I. (2024). Students' attitudes towards AI in teaching and learning. International Journal of Engineering Pedagogy, 14(8), 52731. https://doi.org/10.3991/ijep.v14i8.52731
Vaszkun, B., & Szakács, K. M. (2025). Looking for student success factors outside of the educators’ scope: The effect of digital literacy, personal skills, and learning habits and conditions on self-evaluated online learning effectiveness in management education. International Journal of Management Education, 23(2), 101188. https://doi.org/10.1016/j.ijme.2025.101188
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478. https://doi.org/10.2307/30036540
Vieriu, A. M., & Petrea, G. (2025). The impact of artificial intelligence (AI) on students’ academic development. Education Sciences, 15(3), 343. https://doi.org/10.3390/educsci15030343
Wang, H., & Song, Y. (2024). Portrait of college students’ online learning behavior based on artificial intelligence technology. IEEE Access, 12, 6318–6328. https://doi.org/10.1109/access.2024.3349448
Xu, Y., Chen, C., Feng, D., & Luo, Z. (2022). A survey of college students on the preference for online teaching videos of variable durations in online flipped classroom. Frontiers in Public Health, 10, 838106. https://doi.org/10.3389/fpubh.2022.838106
Zimmerman, B. J. (2002). Becoming a self-regulated learner: An overview. Theory Into Practice, 41(2), 64–70. https://doi.org/10.1207/s15430421tip4102_2
