The advent of the era of artificial intelligence has brought about great changes to contemporary education and teaching, not only in the teaching work of teachers but also in the learning content and learning mode of students. These issues have become the focus of discussion in the education industry. Due to the large differences in the level of education and teaching in different regions, there have always been different concepts in the development of artificial intelligence education in different regions, and thus there has been no consensus on the cultivation of youth artificial intelligence literacy, which is not conducive to the development of youth artificial intelligence literacy cultivation. Therefore, it is important to explore the process and factors of consensus formation. This study aims to explore how to reach a consensus on the cultivation of youth artificial intelligence literacy from the perspective of social psychology. The analysis of the forum on youth artificial intelligence literacy shows that the factors affecting the achievement of consensus are mainly divided into three parts: shared reality, intersubjective consensus, and social representation.

Zhuoying Li, Linxin Liu. Cultivation of Youth Artificial Intelligence Literacy from the Perspective of Social Consensus: Analysis of Roundtable Forum. International Journal of New Developments in Education (2024), Vol. 6, Issue 9: 125-134. https://doi.org/10.25236/IJNDE.2024.060919.

[1] Feng, C. D. (2017). Competency-based education: Connotation, model, principles and challenges. Educational Science Research, (04), 30–34, 40.

[2] Lu, D. K., & Li, S. T. (2024). Is it a “mythical creature” or a “gray rhino”: The multi-dimensional impacts of ChatGPT and other large language models on education and countermeasures. Journal of Xinjiang Normal University (Philosophy and Social Sciences Edition), (2), 106–124.

[3] Li, Q. X., & Liang, Z. (2022). Exploration of teachers' professional development paths in the era of artificial intelligence. Theory and Practice of Education, 42(34), 54–58.

[4] Zhang, Y. R., & Zuo, B. (2006). Social identity theory and its development. Advances in Psychological Science, (03), 475–480.

[5] Wu, Y., & Yang, Y. Y. (2013). The “mutual constitution” of society and individuals in the formation process of social mentality: The enlightenment of the “consensus” theory in social psychology on the study of social mentality. Social Science Front, (02), 159–166.

[6] Liu, F., & Zuo, B. (2010). Intergroup emotion theory and its research. Advances in Psychological Science, (06), 940–947.

[7] Teo, T. W. (2019). STEM education landscape: The case of Singapore. Journal of Physics: Conference Series, 1340(1), 012002. doi:10.1088/1742-6596/1340/1/012002

[8] Wang, H. Y., & Tian, Y. H. (2016). UK: Programming education enters the national curriculum. Shanghai Education, (02), 20–23.

[9] Qian, S. L. (2023). The current situation and enlightenment of computer science education in American primary and secondary schools. Primary and Secondary School Information Technology Education, (09), 91–94.

[10] Yuan, H. (2018). Interpretation of the "New Generation of Artificial Intelligence Development Plan". Technology Wind, (31), 37.

[11] Xu, W., & Ouyang, F. (2022). The application of AI technologies in STEM education: A systematic review from 2011 to 2021. International Journal of STEM Education, 9(1), 59. doi:10.1186/s40594-022-00348-1

[12] Han, Q. Q. (2020). A comparative study of artificial intelligence education in primary and secondary schools in China and the United States [Master's thesis]. Zhejiang Normal University.

[13] Ding, M. R., & Wang, T. J. (2021). Design and application of the "knowledge construction, STEM, and maker" trinity teaching model in artificial intelligence teaching. E-Education Research, (04), 108–114.

[14] Tang, Y. W., Guo, L. T., Xie, Y. G., & Zhong, S. C. (2017). Research on the interdisciplinary integration model of STEM supported by educational artificial intelligence. China Educational Technology, (08), 46–52.

[15] Kenny, D. A., Albright, L., Malloy, T. E., & Kashy, D. A. (1994). Consensus in interpersonal perception: Acquaintance and the big five. Psychological Bulletin, 116(2), 245. doi:10.1037/ 0033-2909.116.2.245

[16] Clement, R. W., & Krueger, J. (2000). The primacy of self-referent information in perceptions of social consensus. British Journal of Social Psychology, 39(2), 279–299. doi:10.1348/ 014466600164680

[17] De Vreede, T., Reiter-Palmon, R., & de Vreede, G. J. (2013, January). The effect of shared mental models on consensus. In 2013 46th Hawaii International Conference on System Sciences (pp. 263–272). IEEE. doi:10.1109/HICSS.2013.117

[18] Briggs, R. O., Kolfschoten, G. L., & Vreede, G. J. D. (2005). Toward a theoretical model of consensus building. AMCIS 2005 Proceedings, 12.

[19] Zollman, K. J. (2012). Social network structure and the achievement of consensus. Politics, Philosophy & Economics, 11(1), 26–44. doi:10.1177/1470594X11414884

[20] Oskamp, S. (1965). Overconfidence in case-study judgments. Journal of Consulting Psychology, 29(3), 261–265. doi:10.1037/h0022062

[21] Tversky, A., Kahneman, D., & Slovic, P. (1982). Judgment under uncertainty: Heuristics and biases. Cambridge University Press.

[22] Echterhoff, G., Higgins, E. T., Kopietz, R., & Groll, S. (2008). How communication goals determine when audience tuning biases memory. Journal of Experimental Psychology: General, 137(1), 3–21. doi:10.1037/0096-3445.137.1.3

[23] Bi, Y. L., Liu, Z., & Li, S. (2008). A comparative study of overconfidence in group decision-making and individual decision-making. Chinese Journal of Ergonomics, (04), 49–52, 77.

[24] Cui, Z. Q., Zhang, H., & Liu, X. P. (2025). The “saying is believing” effect: An explanation based on shared reality. Psychological Development and Education, (01), 145–152.

[25] Cresswell, K. M., Slee, A., Coleman, J., Williams, R., Bates, D. W., & Sheikh, A. (2013). Qualitative analysis of round-table discussions on the business case and procurement challenges for hospital electronic prescribing systems. PLoS One, 8(11), e79394. doi:10.1371/journal.pone.0079394.