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http://dx.doi.org/10.21796/jse.2021.45.1.105

An Analysis of Gender Differences in Primary, Middle and High School Students' Artificial Intelligence Ethics Awareness  

Kim, Gwisik (Gyeongin National University of Education)
Shin, Youngjoon (Gyeongin National University of Education)
Publication Information
Journal of Science Education / v.45, no.1, 2021 , pp. 105-117 More about this Journal
Abstract
The purpose of this study is to analyze the gender differences of elementary, junior high, and high school students in the artificial intelligence ethics awareness (hereinafter referred to as AIEA). This is a study to investigate whether there is a gender difference in the AIEA, and if so, when the gender difference will occur. This study was conducted with 198 elementary school students (98 female students, 100 male students), 265 middle school students (166 female students, 99 male students), and 114 high school students (58 female students and 56 male students) in I Metropolitan City. The results are as follows: First, a gender difference in the AIEA between all boys and girls was confirmed. Second, the gender difference in the AIEA tended to be solidified as the school age increased from elementary school to middle school and high school. Third, female students at all stages of elementary school, junior high school, and high school are not yet very reliable in artificial intelligence, and there is a greater concern about non-discrimination than boys. It turns out that they have a negative position on permission to enter the territory. Fourth, the interaction effects of school age and gender have been identified in 'stability and reliability,' and in 'permit and limit' categories. Taken together, these results show that an educational strategy that approaches the gender equality perspective of the educational program is necessary so that there will be no gender difference in the AIEA during artificial intelligence education activities.
Keywords
intelligence ethics awareness; gender difference; test for intelligence ethics awareness; cross-sectional study;
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1 Lee, E. J., & Lee, K. H. (2011). A study on the factors influencing gender differences changes of Korean students in PISA mathematics assessment. Journal of Educational Research in Mathematics, 21(4), 313-326.
2 Ministry of Science and ICT [MSIT]. (2020). Human-centered ethical standards for artificial intelligence. Press Release, Dec., 23. 2020,
3 Oh, T. W. (2020). EU policy for AI ethics from white paper on artificial intelligence. The Digital Ethics, 4(1), 22-31.
4 Barton, A. C. (1998). Feminist science education. Teachers College Press.
5 Kim, J. H. (2010). Are women more sensitive than men to the risk of cyber victimizations?: Health communication perspective. Health Communication Research, 2(2), 155-180.
6 Bang, J. M. (2021). The transition of regulatory governance on AI algorithms - Focused on US algorithmic regulations and AI ethical principles. Public Law, 49(3), 375-406.   DOI
7 Barrat, J. (2013). Our final invention: Artificial intelligence and the end of the human era. New York, NY: Thomas Dunne Books/St Martin's Press.
8 Byun, S. Y. (2019). A study on the ethics certification program based on the morality types of AI robots. Journal of Ethics, 126, 73-90.
9 Heo, E. S., Lee, Y. H., & Shin, J. W. (2020). Why ethics is: A landscape of modern AI ethics debate, Its features and limitations. Human Beings, Environment and Their Future, 24, 165-209.
10 Byun, S. Y. (2020). A study on the necessity of AI ethics education. The Journal of Korea elementary education, 31(3), 153-164.
11 Ji, H. A. (2020). A study on the application of roboethics to moral education (Doctoral Dissertation). Seoul National University.
12 Kim, G. S., & Shin, Y. J. (2021). Study on the development of test for artificial intelligence ethical awareness. Journal of The Korean Association of Artificial Intelligence Education, 2(1), 1-19.   DOI
13 Kim, H. G., & Kim, Y. S. (2020). Meta-analysis of gender difference in performance on the information subtest of the Korean-Wechsler intelligence scale. The Korean Journal of Rehabilitation Psychology, 27(4), 151-163.   DOI
14 Kim, J. M. (2020). Artificial intelligence algorithm regulation status, recent trends, and legal implications - Focusing on the issue of artificial intelligence bias. The Digital Ethics, 4(2), 27-42.
15 Lee, C. S., & Lee, H. J. (2019). Expectations and anxieties affecting attitudes toward artificial intelligence revolution. Journal of the Korea Contents Association, 19(9), 37-46.
16 Yoon, S. H. (2016). The shaping and changes of scientific discourses: Focusing on the discourses on sex/gender differences in neuroscience (Doctoral Dissertation). Seoul National University.
17 Kim, M. J. (2017). The necessity of artificial intelligence ethics and trends in Korea and abroad. Journal of The Korean Institute of Communication Sciences, 34(10), 45-54.
18 Kline, R. (2010). Cybernetics, automata studies, and the Dartmouth conference on artificial intelligence. IEEE Annals of the History of Computing, 33(4), 5-16.   DOI
19 Kurzweil, R. (2005). The singularity is near. New York, NY: Viking.
20 Lee, A. R., Lee, Y. J., & Yang, H. I. (2014). The effects of cyber bullying and bullied experience on upper elementary students' aggression and verbal aggression. Korea Journal of Counseling, 15(6), 2437-2450.   DOI
21 Lee, J. W. (2019). Can we impose responsibilities on artificial intelligence? To seek accountability- oriented ethics for artificial intelligence. Korean Journal for the Philosophy of Science, 22(2), 70-104.
22 Lee, K. S., & Park, I. Y. (2015). Characteristics on gender difference of Korean students in TIMSS mathematics assessment. The Journal of Curriculum and Evaluation, 18(1), 155-183.   DOI
23 Ministry of Education [MOE]. (2020). Educational policy directions and key tasks in the age of artificial intelligence. Press Release, Nov., 20. 2020,
24 Organisation for Economic Co-operation and Development [OECD]. (2019). Trends shaping education 2019. Paris: OECD Publishing, DOI: 10.1787/trends_edu-2019-en.
25 Park, A. C., & Woo, C. Y. (2008). The relationship among high school students' attachment for their parents and peers depending on gender, self-identity, and career decision-making levels. The Korean Journal of Educational Psychology, 22(1), 69-85.
26 Sung, S. J. (1999). Cyberface and gender difference ideology. Journal of Women Studies, 10, 101-116.
27 Park, C. J., Dong, H. K., & Shin, Y. J. (2007). An analysis of preferences for science and the role gender differences plays in determining preferences for It amongst elementary school students. Journal of Korean Elementary Science Education, 26(2), 216-225.
28 Park, J. Y. (2018). Trend analysis of artificial intelligence technology using patent information. Journal of the Korea society of computer and information, 23(4), 9-16.   DOI
29 Song, M. Y., Im, H. J., Rim, H. M., Park, H. Y., & Ku, J. O. (2015). Educational factors influencing the gender difference in PISA. The journal of Educational Studies, 46(4), 99-122.   DOI
30 Yoo, E. H. (2018). Online privacy, technologies, and perceived risk. The Journal of Social Science, 25(2), 82-100.   DOI
31 Yun, S. J. (2020). The allegory of AI and empathy in the movie Her. The Journal of Image and Cultural Contents, 19, 213-236.   DOI
32 Kim. D. H. (2016). Forming and indicating a christian theological discourse on AI. Theological Studies, 68, 35-60.