Kesetaraan Gender dan Sikap terhadap AI dalam Rangka Bela Negara Pada Generasi Z
DOI:
https://doi.org/10.33557/v25cn123Keywords:
Gender Equality, AI, Generation Z, Digital NationalismAbstract
This study examines the influence of gender equality on Generation Z's attitudes toward artificial intelligence (AI) in the context of digital national defense. A total of 171 university students aged 18–25 years old were involved as respondents, consisting of 95 women and 76 men. Analysis using the Mann-Whitney test showed a significant difference in attitudes toward AI between men and women, where women scored higher (mean rank = 93.10) than men (mean rank = 77.13). However, no significant difference was found in the level of gender equality between the two. The results of the correlation test showed a strong positive relationship between gender equality and attitudes toward AI, with a correlation value of 0.689. In addition, the results of the regression analysis showed that gender equality contributed 48% to the formation of Generation Z's attitudes toward AI. This means that the higher the level of gender equality, the more positive their attitudes toward the use of artificial intelligence-based technology. Other factors outside the gender equality variable were not examined in this study. These findings confirm that gender equality plays a significant role in shaping Generation Z's readiness and participation in digital national defense. The results of this study are expected to form the basis for the development of inclusive digital literacy and gender-equitable technology policies in Indonesia.
References
Akbar, R. S., Hutasuhut, M. A., & Rifansyah, M. A. A. (2024). Bela Negara Di Era Digital Tantangan Dan Strategi Memperkokoh Nilai-Nilai. 4, 8418–8428.
Bennett, S., Maton, K., & Kervin, L. (2008). The “digital natives” debate: A critical review of the evidence. British Journal of Educational Technology, 39(5). https://doi.org/10.1111/j.1467-8535.2007.00793.x
Creswell, J., & Creswell, Jd. (2018). Research Design: Qualitative, Quantitative adn Mixed Methods Approaches. In Journal of Chemical Information and Modeling (Vol. 53, Issue 9).
Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate Data Analysis. Seventh Edition. Prentice Hall. In Exploratory Data Analysis in Business and Economics.
Kasneci, E., Sessler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., Gasser, U., Groh, G., Günnemann, S., Hüllermeier, E., Krusche, S., Kutyniok, G., Michaeli, T., Nerdel, C., Pfeffer, J., Poquet, O., Sailer, M., Schmidt, A., Seidel, T., … Kasneci, G. (2023). ChatGPT for good? On opportunities and challenges of large language models for education. In Learning and Individual Differences (Vol. 103). https://doi.org/10.1016/j.lindif.2023.102274
Kemhan_RI. (2015). Buku Putih Pertahanan Indonesia 2015.
Kline, R. B. (1999). Book Review: Psychometric theory (3rd ed.). Journal of Psychoeducational Assessment, 17(3), 275–280. https://doi.org/10.1177/073428299901700307
Lestari, P. A., & Elfattah, H. Y. A. (2025). Media, Gender, and Identity: Challenges and Strategies for Equitable Representation. Sinergi International Journal of Communication Sciences, 3(2), 73–86. https://doi.org/10.61194/ijcs.v3i2.652
Mandeville, G. K., & Roscoe, J. T. (1971). Fundamental Research Statistics for the Behavioral Sciences. Journal of the American Statistical Association, 66(333), 224. https://doi.org/10.2307/2284880
Novotny, M., Weber, W., Kern, C., & Kreuter, F. (2025). Measuring public opinion towards artificial intelligence: development and validation of a general AI attitude short scale. In AI and Society (Issue Zhang 2023). Springer London. https://doi.org/10.1007/s00146-025-02478-5
Prensky, M. (2001). Digital Natives, Digital
Immigrants Part 1. On the Horizon, 9(5), 1–6. https://doi.org/10.1108/10748120110424816
Sadia, H., & Khurshid, F. (2025). Vol. 03 No. 01. January-March 2025 Advance Social Science Archives Journal. Advance Social Science Archives Journal, 03(01), 1802–1809.
Sey, A. (2021). Gender Digital Equality Across ASEAN. ERIA Discussion Paper Series, 358(358).
Tunjungbiru, A. D., Pranggono, B., Sari, R. F., Sanchez-Velazquez, E., Purnamasari, P. D., Liliana, D. Y., & Andryani, N. A. C. (2025). AI Literacy and Gender Bias: Comparative Perspectives from the UK and Indonesia. Education Sciences, 15(9), 1–23. https://doi.org/10.3390/educsci15091143
West, M., Kraut, R., & Ei Chew, H. (2019). I’d blush if I could : closing gender divides in digital skills through education. Ministerio De Educación, 306.
West, S. M., Whittaker, M., & Crawford, K. (2019). Discriminating systems. In AI Now Institute (Issue April). https://ainowinstitute.org/wp-content/uploads/2023/04/discriminatingsystems.pdf%0Ahttps://ainowinstitute.org/discriminatingsystems.pdf
Whatley, M. a. (2008). The dimensionality of the 15 items Attitudes Toward Women Scale. Race, Gender & Class, 15(1–2), 265–273.
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