ปัจจัยเชิงสาเหตุของความฉลาดรู้ทางปัญญาประดิษฐ์และผลต่อประสิทธิภาพการเรียนรู้ของนักศึกษาระดับอุดมศึกษา

Thapanee Phengsuk; Panithan Mekkamol; Sawitree Boonmee

Authors

Thapanee Phengsuk Faculty of Management Science, Udon Thani Rajabhat University
Panithan Mekkamol Faculty of Management Science, Udon Thani Rajabhat University
Sawitree Boonmee Faculty of Management Science, Udon Thani Rajabhat University

Keywords:

Artificial Intelligence Literacy, AI Usage Experience, AI Ethics Awareness, Digital Capability, Learning Performance

Abstract

Objectives of the research article were: 1. examine the causal factors influencing artificial intelligence literacy among higher education students and 2. investigate the effect of AI literacy on students’ learning performance. The antecedent factors considered included digital capability, AI usage experience, AI ethics awareness, and institutional support. A quantitative explanatory research design was utilized. The study included 470 higher-education students from Mueang District, Udon Thani Province, Thailand. Data was collected through a questionnaire and analyzed using descriptive statistics, confirmatory factor analysis, and structural equation modeling (SEM).

The proposed model demonstrated an acceptable fit with the empirical data (χ²/df = 2.372, CFI = 0.976, RMSEA = 0.054). AI usage experience had the strongest positive effect on AI literacy (β = 0.802, p < .001), followed by AI ethics awareness (β = 0.240, p < .01). Institutional support exhibited a significant negative effect (β = −0.104, p < .05), whereas digital capability did not have a statistically significant effect. Additionally, AI literacy had a significant positive effect on learning performance (β = 0.690, p < .001). These results indicate that experiential interaction with AI tools is essential for promoting AI literacy and that both practical and ethical approaches are important for enhancing learning performance in higher education.

References

Baumgartner, H. & Homburg, C. (1996). Applications of Structural Equation Modeling in Marketing and Consumer Research: A Review. International Journal of Research in Marketing, 13(2), 139–161.

Bentler, P. M. & Bonett, D. G. (1980). Significance tests and goodness of fit in the analysis of covariance structures. Psychological Bulletin, 88(3), 588–606.

Bewersdorff, A. et al. (2025). Taking The Next Step with Generative Artificial Intelligence: The Transformative Role of Multimodal Large Language Models in Science Education. Learning and Individual Differences, 118, 102601.

Chun, Z. et al. (2025). Exploring The Interplay Among Artificial Intelligence Literacy, Creativity, Self-Efficacy, and Academic Achievement in College Students. Education and Information Technologies, 30, 1–34.

Cortina, J. M. (1993). What is coefficient alpha? An examination of theory and applications. Journal of Applied Psychology, 78(1), 98–104.

Fornell, C. & Larcker, D. F. (1981). Structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50.

Hair, J. F. et al. (2019). Multivariate data analysis. Hampshire: Cengage Learning.

Hu, L. T. & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis. Structural Equation Modeling, 6(1), 1–55.

Kline, R. B. (2023). Principles and practice of structural equation modeling. New York: Guilford Publications.

Kohnke, L. et al. (2025). Preparing future educators for AI-enhanced classrooms: Insights into AI literacy and integration. Computers and Education: Artificial Intelligence, 8, 100398.

Long, D. & Magerko, B. (2020). What is AI literacy? Competencies and design considerations. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems. (pp. 1–16). New York: Association for Computing Machinery.

MacCallum, R. C. et al. (1996). Power analysis and determination of sample size for covariance structure modeling. Psychological Methods, 1(2), 130–149.

Ngoveni, M. (2025). Bridging the AI knowledge gap: The urgent need for AI literacy and institutional support. The International Journal of Technologies in Learning, 32(2), 83.

Ng, D. T. K. et al. (2021). Conceptualizing AI literacy. Computers and Education: Artificial Intelligence, 2, 100041.

Nunnally, J. C. & Bernstein, I. H. (1994). Psychometric theory (3rd ed.). New York: McGraw-Hill.

Scherer, R. et al. (2021). Profiling teachers’ readiness for online teaching and learning in higher education: Who’s ready?. Computers in Human Behavior, 118, 106675.

Schermelleh-Engel, K. et al. (2003). Evaluating the fit of structural equation models. Methods of Psychological Research Online, 8(2), 23–74.

Singh, E. et al. (2025). AI-enhanced education: Exploring the impact of AI literacy on Generation Z’s academic performance in Northern India. Quality Assurance in Education, 33(2), 185–202.

Sun, L. & Zhou, L. (2024). Does generative artificial intelligence improve the academic achievement of college students?. Journal of Educational Computing Research, 62(7), 1676–1713.

Techasermwattanakul, P. & Suwannatthachote, P. (2025). The application of generative AI in educational research: A systematic literature review. Journal of Education and Innovation, 27(1), 160–174.

Tinmaz, H. et al. (2022). A systematic review on digital literacy. Smart Learning Environments, 9(1), 1-18.

Tongchai, A. & Malakul, S. (2025). Thai Teachers' Perceptions of Integrating Generative AI in K-12 Education: Opportunities and Challenges. TechTrends, 1-13.

UNESCO. (2018). A global framework of reference on digital literacy skills for indicator 4.4.2. Paris: United Nations Educational, Scientific and Cultural Organization.

UNESCO. (2022). Recommendation on the ethics of artificial intelligence. Paris: United Nations Educational, Scientific and Cultural Organization.

Wang, C. et al. (2025). Factors influencing university students’ behavioral intention to use generative artificial intelligence: Integrating the theory of planned behavior and AI literacy. International Journal of Human–Computer Interaction, 41(11), 6649–6671.

Yamane, T. (1973). Statistics: An introductory analysis (3rd ed.). New York: Harper & Row.

Yang, J. et al. (2025). A framework for AI ethics literacy: Development, validation, and its role in fostering students' self-rated learning competence. Scientific Reports, 15(1), 38030.

Yang, Y. et al. (2025). Navigating the landscape of AI literacy education: Insights from a decade of research (2014–2024). Humanities and Social Sciences Communications, 12, 1-12.