Research on the Co-evolution Model of AI Education Ecosystem: The Perspective of Complex System

  • Yiling Hu ,
  • Zihong Zhao ,
  • Fang Wen
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  • Department of Education Information Technology, East China Normal University, Shanghai 200062

Accepted date: 2022-05-29

  Online published: 2022-08-24

Abstract

As the core technology that triggered the fourth technological revolution, artificial intelligence (AI) has brought about subversive changes and reshaping of various industries in the human social system, and the resulting industrial structure and human capital structure have undergone major changes. The education system is the main position for talent training and human capital output. In the face of the integration of AI technology and the transformation of social talent needs, the education system urgently needs to change its original development goals and structural forms to deal with the impact of AI technology on the education system shocks and challenges. Therefore, this research uses complex system science as the theoretical basis to construct a co-evolution model of the education ecosystem, and analyzes the subjects contained in the education system at different levels and the complexity between subjects from the three levels of macro, meso, and micro. Through the deconstruction of the system co-evolution model, the co-evolution dynamic mechanism driven by AI education reform is established, and the overall strategy of “incentive-guarantee-operating mechanism” is proposed to integrate the wisdom and power of multiple subjects in the education ecosystem and jointly promote the co-evolution of the education ecosystem, so as to promote the AI ??education reform of the education system.

Cite this article

Yiling Hu , Zihong Zhao , Fang Wen . Research on the Co-evolution Model of AI Education Ecosystem: The Perspective of Complex System[J]. Journal of East China Normal University(Educational Sciences), 2022 , 40(9) : 118 -126 . DOI: 10.16382/j.cnki.1000-5560.2022.09.011

References

1 蔡宁, 潘松挺. (2008). 网络关系强度与企业技术创新模式的耦合性及其协同演化——以海正药业技术创新网络为例. 中国工业经济, (04), 137- 144.
2 曹培杰. (2018). 智慧教育: 人工智能时代的教育变革. 教育研究, 39 (08), 121- 128.
3 董俊武, 黄江圳, 陈震红. (2004). 动态能力演化的知识模型与一个中国企业的案例分析. 管理世界, (04), 117- 127+156.
4 杜占元. (2018). 人工智能与未来教育变革. 中国国情国力, (01), 6- 8+5.
5 范国睿. (2004). 复杂科学与教育组织管理研究. 教育研究, (02), 52- 58.
6 顾小清. (2021). 当现实逼近想象: 人工智能时代预见未来教育研究. 开放教育研究, 27 (01), 4- 12.
7 何大韧, 刘宗华, 汪秉宏. (2009). 复杂系统与复杂网络. 北京: 高等教育出版社.
8 黄培伦, 尚航标, 王三木, 李海峰. (2008). 企业能力: 静态能力与动态能力理论界定及关系辨析. 科学学与科学技术管理, (07), 165- 169.
9 李世瑾, 胡艺龄, 顾小清. (2021). 如何走出人工智能教育风险的困局: 现象、成因及应对. 电化教育研究, 42 (07), 19- 25.
10 潘松挺. (2009). 网络关系强度与技术创新模式的耦合及其协同演化. 杭州: 浙江大学博士论文.
11 王振. (2018). 人工智能对产业发展的影响. 现代管理科学, (04), 58- 60.
12 薛霄. (2020). 复杂系统的计算实验方法——原理、模型与案例. 北京: 科学出版社. .
13 赵昌文, 陈春发, 唐英凯. (2009). 科技金融. 北京: 科学出版社.
14 张力. (2011). 产学研协同创新的战略意义和政策走向. 教育研究, 32 (07), 18- 21.
15 周文娟. (2018). “人工智能+”时代的教育变革路向研究. 郑州轻工业学院学报(社会科学版), 19 (06), 62- 70.
16 Burbules, N. C., Fan, G., & Repp, P. (2020). Five trends of education and technology in a sustainable future. Geography and Sustainability, 1 (2), 93- 97.
17 Corrigan, J. A. (2012). The implementation of e-tutoring in secondary schools: A diffusion study. Computers & Education, 59 (3), 925- 936.
18 Cannon, M. D., & Edmondson, A. C. (2005). Failing to learn and learning to fail (intelligently): How great organizations put failure to work to innovate and improve. Long Range Planning, 38 (3), 299- 319.
19 Etzkowitz, H. (2003). Innovation in innovation: the triple helix of university-industry-government relations. Social Science Information, 42 (3), 293- 337.
20 Gainer, J. (2012). Critical thinking: Foundational for digital literacies and democracy. Journal of Adolescent & Adult Literacy, 56 (1), 14- 17.
21 Hedberg R. (1981). How organizations learn and unlearn. In: Nystrom, P. C., Starbuck, W. H. ed. Handbook of Organizational Design. Oxford: Oxford University Press.
22 Herzberg, F. (1987). One more time: How do you motivate employees? Cambridge MA. : Harvard Business Review Press.
23 Herzberg, F. (2017). Motivation to work. New York: Routledge.
24 Hopfenbeck, T., Tolo, A., Florez, T., & El Masri, Y. (2013). Balancing trust and accountability? The assessment for learning programme. in Norway: A governing complex education systems case study, OECD Education Working Papers. Paris: OECD Publishing.
25 Jacobson, M. J., & Wilensky, U. (2006). Complex systems in education: Scientific and educational importance and implications for the learning sciences. The Journal of the learning sciences, 15 (1), 11- 34.
26 Kantabutra, S., & Avery, G. C. (2010). The power of vision: Statements that resonate. Journal of Business Strategy, 31 (1), 37- 45.
27 Kirkman, B. L., & Rosen, B. (1999). Beyond self-management: Antecedents and consequences of team empowerment. Academy of Management Journal, 42 (1), 58- 74.
28 Luckin, R. , Holmes, W. , Griffiths, M. & Forcier, L. B. (2016). Intelligence unleashed: An argument for AI in education. London: Pearson.
29 Miller, J. H., Page, S. E., & Page, S. (2009). Complex adaptive systems. Princeton: Princeton university press.
30 Ng, Y. M. M. (2020). Re-examining the innovation post-adoption process: the case of Twitter discontinuance. Computers in Human Behavior, 103, 48- 56.
31 Sahin, S. (2012). Pre-service teachers’ perspectives of the diffusion of information and communications technologies (ICTs) and the effect of case-based discussions (CBDs). Computers & Education, 59 (4), 1089- 1098.
32 Rogers, E. M. (2010). Diffusion of innovations. New Yord: Simon and Schuster.
33 Seldon, A., Abidoye, O., & Metcalf, T. (2020). The fourth education revolution reconsidered: Will artificial intelligence enrich or diminish humanity?. Northwood: Legend Press.
34 Senge, P. M. (2010). The fifth discipline: the art and practice of the learning organization. New York. Performance Improvement, 30 (5), 37- 37.
35 Tampoe, M. (1993). Motivating knowledge workers—The challenge for the 1990s. Long Range Planning, 26 (3), 49- 55.
36 Tampoe, M. (1994). Exploiting the core competences of your organization. Long Range Planning, 27 (4), 66- 77.
37 Teece, D. J., & Pisano, G. P. (1994). The dynamic capabilities of firms: an introduction. Industrial and Corporate Change, 3 (3), 537- 556.
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