AI LITERACY AND RISK TOLERANCE AMONG UNIVERSITY STUDENTS
DOI:
https://doi.org/10.38076/y0myhg56Kata Kunci:
AI literacy, risk tolerance, perceived ease of use, university studentsAbstrak
This study investigated the relationship between artificial intelligence (AI) literacy and risk tolerance among Indonesian university students. As generative AI tools became deeply embedded in students' academic and personal decision-making, understanding how different dimensions of AI literacy related to risk-taking dispositions became increasingly important. A correlational quantitative design was employed, involving 156 university students recruited through purposive sampling. AI literacy was measured using the seven-dimensional AI Literacy Scale developed by Nong et al. (2024), comprising application ability, morality, critical thinking, self-efficacy, cognitive ability, perceived ease of use, and perceived usefulness. Risk tolerance was assessed using the Financial Risk Tolerance Scale developed by Grable and Lytton (1999). Spearman's rho correlation analysis was used to examine the relationship between variables. The results revealed that only the perceived ease of use dimension was significantly and negatively correlated with risk tolerance (r = -0.326, p < 0.01), whereas the other six dimensions of AI literacy showed no significant correlation. These findings indicated that students who perceived AI tools as easier to use tended to display lower risk tolerance, suggesting that effortless interaction with AI may foster cognitive comfort that reduces willingness to engage with uncertainty. The study contributed to the AI literacy literature by demonstrating dimension-specific associations between AI literacy and a key psychological disposition.
Penelitian ini menyelidiki hubungan antara literasi kecerdasan buatan (AI) dan toleransi risiko pada mahasiswa di Indonesia. Seiring dengan semakin tertanamnya alat AI generatif dalam pengambilan keputusan akademik dan personal mahasiswa, pemahaman tentang bagaimana berbagai dimensi literasi AI berkaitan dengan disposisi pengambilan risiko menjadi semakin penting. Penelitian ini menggunakan desain kuantitatif korelasional dengan melibatkan 156 mahasiswa yang dipilih melalui purposive sampling. Literasi AI diukur menggunakan AI Literacy Scale tujuh dimensi yang dikembangkan oleh Nong et al. (2024), terdiri dari application ability, morality, critical thinking, self-efficacy, cognitive ability, perceived ease of use, dan perceived usefulness. Toleransi risiko diukur menggunakan Financial Risk Tolerance Scale yang dikembangkan oleh Grable dan Lytton (1999). Analisis korelasi Spearman digunakan untuk menguji hubungan antar variabel. Hasil penelitian menunjukkan bahwa hanya dimensi perceived ease of use yang berkorelasi negatif signifikan dengan toleransi risiko (r = -0,234, p < 0,01), sedangkan enam dimensi literasi AI lainnya tidak berkorelasi signifikan. Temuan ini mengindikasikan bahwa mahasiswa yang merasa alat AI lebih mudah digunakan cenderung memiliki toleransi risiko yang lebih rendah, menunjukkan bahwa interaksi yang mudah dengan AI dapat menumbuhkan kenyamanan kognitif yang menurunkan kesediaan menghadapi ketidakpastian.
Referensi
APJII. (2024). Survei penetrasi internet Indonesia 2024. Asosiasi Penyelenggara Jasa Internet Indonesia.
Bandura, A. (1997). Self-efficacy: The exercise of control. W. H. Freeman.
Cardona, M. A., Rodríguez, R. J., & Ishmael, K. (2023). Artificial intelligence and the future of teaching and learning. U.S. Department of Education, Office of Educational Technology.
Chiu, T. K. F., Xia, Q., Zhou, X., Chai, C. S., & Cheng, M. (2023). Systematic literature review on opportunities, challenges, and future research recommendations of artificial intelligence in education. Computers and Education: Artificial Intelligence, 4, 100118. https://doi.org/10.1016/j.caeai.2022.100118
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
Ellsberg, D. (1961). Risk, ambiguity, and the savage axioms. The Quarterly Journal of Economics, 75(4), 643–669. https://doi.org/10.2307/1884324
Forbes, J., & Kara, S. M. (2010). Confidence mediates how investment knowledge influences investing self-efficacy. Journal of Economic Psychology, 31(3), 435–443. https://doi.org/10.1016/j.joep.2010.01.012
Grable, J. E., & Lytton, R. H. (1999). Financial risk tolerance revisited: The development of a risk assessment instrument. Financial Services Review, 8(3), 163–181. https://doi.org/10.1016/S1057-0810(99)00041-4
Hallahan, T. A., Faff, R. W., & McKenzie, M. D. (2004). An empirical investigation of personal financial risk tolerance. Financial Services Review, 13(1), 57–78. https://doi.org/10.61190/fsr.v13i1.4782
Heath, C., & Tversky, A. (1991). Preference and belief: Ambiguity and competence in choice under uncertainty. Journal of Risk and Uncertainty, 4(1), 5–28. https://doi.org/10.1007/BF00057884
Kahneman, D. (2011). Thinking, fast and slow. Farrar, Straus and Giroux.
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
Long, D., & Magerko, B. (2020). What is AI literacy? Competencies and design considerations. Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, 1–16. https://doi.org/10.1145/3313831.3376727
Lusardi, A., & Mitchell, O. S. (2014). The economic importance of financial literacy: Theory and evidence. Journal of Economic Literature, 52(1), 5–44. https://doi.org/10.1257/jel.52.1.5
Ng, D. T. K., Leung, J. K. L., Chu, S. K. W., & Qiao, M. S. (2021). Conceptualizing AI literacy: An exploratory review. Computers and Education: Artificial Intelligence, 2, 100041. https://doi.org/10.1016/j.caeai.2021.100041
Nobre, L. H. N., & Grable, J. E. (2015). The role of risk profiles and risk tolerance in shaping client investment decisions. Journal of Financial Service Professionals, 69(3), 18–21.
Nong, Y., Cui, J., He, Y., Zhang, P., & Zhang, T. (2024). Development and validation of an AI literacy scale. Journal of Artificial Intelligence Research, 1(1), 17–26. https://doi.org/10.70891/JAIR.2024.100029
Otoritas Jasa Keuangan. (2024). Survei nasional literasi dan inklusi keuangan (SNLIK) 2024. Retrieved from https://ojk.go.id/id/berita-dan-kegiatan/publikasi/Pages/Survei-Nasional-Literasi-dan-Inklusi-Keuangan-(SNLIK)-2024.aspx
Roszkowski, M. J., & Davey, G. (2010). Risk perception and risk tolerance changes attributable to the 2008 economic crisis: A subtle but critical difference. Journal of Financial Service Professionals, 64(4), 42–53.
Ryack, K. (2011). The impact of family relationships and financial education on financial risk tolerance. Financial Services Review, 20(3), 181–193. https://doi.org/10.61190/fsr.v20i3.4702
Sahi, S. K. (2013). Demographic and socio-economic determinants of financial satisfaction. International Journal of Social Economics, 40(2), 127–150. https://doi.org/10.1108/03068291311283607
Tversky, A., & Kahneman, D. (1992). Advances in prospect theory: Cumulative representation of uncertainty. Journal of Risk and Uncertainty, 5(4), 297–323. https://doi.org/10.1007/BF00122574
Vaccaro, M., Almaatouq, A., & Malone, T. (2024). When combinations of humans and AI are useful: A syste-matic review and meta-analysis. Nature Human Behaviour, 8(11), 2293–2303. https://doi.org/10.1038/s41562-024-02024-1
Van Rooij, M., Lusardi, A., & Alessie, R. (2011). Financial literacy and stock market participation. Journal of Financial Economics, 101(2), 449–472. https://doi.org/10.1016/j.jfineco.2011.03.006
Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the Technology Acceptance Model: Four longitudinal field studies. Management Science, 46(2), 186–204. https://doi.org/10.1287/mnsc.46.2.186.11926
Wang, B., Rau, P. L. P., & Yuan, T. (2023). Measuring user competence in using artificial intelligence: Validity and reliability of artificial intelligence literacy scale. Behaviour & Information Technology, 42(9), 1324–1337. https://doi.org/10.1080/0144929X.2022.2072768
Yao, R., & Hanna, S. D. (2005). The effect of gender and marital status on financial risk tolerance. Journal of Personal Finance, 4(1), 66–85.
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