Student Readiness for AI in Business: Roles of Literacy, Attitude, and Practical Engagement
DOI:
https://doi.org/10.22441/jurnal_mix.2026.v16i2.001Keywords:
AI Literacy, Affective Attitude, Practical Engagement, Business Education, Student ReadinessAbstract
Purpose: This study investigates the determinants of student readiness for artificial intelligence (AI) integration in business education, emphasizing the roles of AI literacy, affective attitude, and practical engagement. The research addresses the increasing demand for future business professionals equipped to operate in AI-enhanced environments.
Methodology: A quantitative approach was employed using survey data from 177 undergraduate students in the Greater Jakarta area. Constructs were measured through validated instruments and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) to examine both direct and mediated relationships among variables.
Findings: The results show that AI literacy and affective attitude have significant direct effects on AI readiness. Practical engagement contributes to readiness indirectly through affective attitude. Affective attitude is the strongest direct predictor of readiness and significantly mediates the effects of AI literacy and practical engagement.
Conclusion: The findings suggest that enhancing AI readiness requires a holistic educational framework integrating conceptual knowledge, experiential exposure, and emotional receptivity. Curriculum designers should focus on affective learning strategies alongside technical training to foster a workforce capable of navigating AI-driven business contexts.
References
Bati, S., Rokaya, N. S., Sherpa, N. C., & Gupta, R. (2024). Factors influencing attitude towards adaptation of AI among business students. New Perspective: Journal of Business and Economics, 7(1), 21–32. https://doi.org/10.3126/npjbe.v7i1.70020
Chan, C. K. Y., & Hu, W. (2023). Students’ voices on generative AI: Perceptions, benefits, and challenges in higher education. International Journal of Educational Technology in Higher Education, 20, 43. https://doi.org/10.1186/s41239-023-00411-8
Chiu, T. K. F., Ahmad, Z., Ismailov, M., & Sanusi, I. T. (2024). What are artificial intelligence literacy and competency? A comprehensive framework to support them. Computers and Education Open, 6, 100171. https://doi.org/10.1016/j.caeo.2024.100171
Dai, Y., Chai, C., Lin, P. Y., Jong, M., Guo, Y., & Qin, J. (2020). Promoting students’ well-being by developing their readiness for the artificial intelligence age. Sustainability, 12(16), 6597. https://doi.org/10.3390/su12166597
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
DeVasto, D., & Palmer, Z. (2024). Building critical AI literacy in the business communication classroom. Business and Professional Communication Quarterly, 87(4), 557–572. https://doi.org/10.1177/23294906241253199
Falebita, O. S., & Kok, P. J. (2025). Artificial intelligence tools usage: A structural equation modeling of undergraduates’ technological readiness, self-efficacy and attitudes. Journal for STEM Education Research, 8, 257–282. https://doi.org/10.1007/s41979-024-00132-1
Ganguli, S. (2025). The why and how of generative AI application in business for career readiness. International Journal of Advanced Corporate Learning (IJAC), 18(1), 64–76. https://doi.org/10.3991/ijac.v18i1.51137
Kong, S.-C., Cheung, W. M.-Y., & Zhang, G. (2021). Evaluation of an artificial intelligence literacy course for university students with diverse study backgrounds. Computers and Education: Artificial Intelligence, 2, 100026. https://doi.org/10.1016/j.caeai.2021.100026
Laupichler, M. C., Aster, A., Schirch, J., & Raupach, T. (2022). Artificial intelligence literacy in higher and adult education: A scoping literature review. Computers and Education: Artificial Intelligence, 3, 100101. https://doi.org/10.1016/j.caeai.2022.100101
Li, W., Zhang, X., Li, J., Yang, X., Li, D., & Liu, Y. (2024). An explanatory study of factors influencing engagement in AI education at the K-12 level: An extension of the classic TAM model. Scientific Reports, 14(1), 13922. https://doi.org/10.1038/s41598-024-64363-3
Southworth, J. R., Migliaccio, K., Glover, J., Glover, J. M., Reed, D., McCarty, C., Brendemuhl, J., & Thomas, A. O. (2023). Developing a model for AI across the curriculum: Transforming the higher education landscape via innovation in AI literacy. Computers and Education: Artificial Intelligence, 4, 100127. https://doi.org/10.1016/j.caeai.2023.100127
Wu, X., & Qin, Y. (2025). On the relationship between music students’ negative emotions, artificial intelligence readiness, and their engagement. Acta Psychologica, 253, 104760. https://doi.org/10.1016/j.actpsy.2025.104760
Yalcinkaya, T., Ergin, E., & Yucel, S. C. (2024). Exploring nursing students’ attitudes and readiness for artificial intelligence: A cross-sectional study. Teaching and Learning in Nursing, 19(4), e722–e728. https://doi.org/10.1016/j.teln.2024.07.008
You, Z., Guo, M., Wainaina, G., Qi, K., & Guo, H. (2024). AI literacy and academic performance of Chinese university students: Mediating role of learning attitude. In EKI ’24: Proceedings of the 2nd International Conference on Educational Knowledge and Informatization (pp. 495–503). Association for Computing Machinery. https://doi.org/10.1145/3691720.3691805
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