AI-DTCEM: A Capability Ecology Framework for Dual-Qualified Teacher Team Construction
Source abstract
Abstract
Addressing Artificial Intelligence (AI) faculty deficiencies in higher education, this paper develops the AI+ Dual-qualified Teacher Capability Ecology Model (AI-DTCEM) based on Capability Ecology Theory. The model is developed after a thorough analysis of the current state of new engineering talent cultivation in universities and the innovative practical abilities required in the AI+ environment. This paper proposes an implementation framework characterized by “three-dimensional collaboration, four-tier progression, and five-element drive.” Additionally, it uses the collaborative education project involving Hangzhou Normal University, Zhejiang University, and Hangzhou Ruishu Technology Co., Ltd. as a backdrop to introduce a deep collaborative education model, showcasing the theoretical and practical achievements of this project. Using NetLogo as the simulation platform, this paper designs a 96-month system dynamics experiment to compare and analyze the outcomes of four scenarios: the baseline experiment, the AI-enhanced experiment, the policy-driven experiment, and the comprehensive optimization experiment. This study reveals the following findings: (1) Policy-driven initiatives are crucial for the successful construction of dual-qualified teacher teams, with the policy-driven scenario achieving the highest overall skill level (9.332). (2) The application of AI technology significantly enhances teacher skill development, resulting in AI skill improvements ranging from 116.6% to 163.4%. (3) The comprehensive optimization scenario (utilizing a collaborative mechanism) achieves systemic advantages, realizing a 100% dual-qualified teacher ratio. However, this comes with diminishing marginal returns on investment. This research provides a theoretical foundation, quantitative analysis, and practical pathways for developing dual-qualified teacher teams in the AI+ era.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Innovative Teaching and Learning Methods · Software Engineering Techniques and Practices · Competency Development and Evaluation
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
International College, Zhejiang University, No. 866, Yuhangtang Road, Hangzhou 310058, China
School of Information Science and Technology, Hangzhou Normal University, No. 2318, Yuhangtang Road, Hangzhou 311121, China
Hangzhou Ruishu Technology Co., Ltd., Cangqian Street, Hangzhou 311121, China
Funding
Zhejiang Provincial Leading (Lingyan) R&D Program · Zhejiang Provincial Philosophy and Social Sciences Planning Project · Zhejiang Provincial Natural Science Foundation of China
References (30)
Show source-provided references
1. Ministry of Education (2018). Notice on printing and distributing the “Action Plan for Innovation in Artificial Intelligence in Higher Education”, Bulletin of the Ministry of Education of the People’s Republic of China.
2. Research on the training mechanism of AI talent in universities based on industry-university integration in the 14th Five-Year Plan
3. Zhang, F. (2024, August 05). Artificial Intelligence Education Should Be Connected with Traditional Industries. Available online: http://m.jyb.cn/rmtzcg/xwy/wzxw/201903/t20190304_215411_wap.html.
4. Research on the evaluation standard of “double-qualified” teachers in information-related majors of local undergraduate colleges
5. Li, S., Yuan, C., and Zhong, J. (2021). Software Engineering “Double-Qualified” Teacher Team Construction Research. Heilongjiang Educ. (High. Educ. Res. Eval.), 27–29.
6. Darling-Hammond, L., Hyler, M.E., and Gardner, M. (2017). Effective Teacher Professional Development, Learning Policy Institute.10.54300/122.311 ↗
7. Rethinking the connections between campus courses and field experiences in college-and university-based teacher education10.1177/0022487109347671 ↗
8. Leoste, J., Tammets, K., and Ley, T. (2023). Integrating AI tools in teacher professional learning: A conceptual model and illustrative case. Front. Artif. Intell., 6.10.3389/frai.2023.1255089 ↗
9. The promises and challenges of artificial intelligence for teachers: A systematic review of research10.1007/s11528-022-00715-y ↗
10. Reshaping curriculum adaptation in the age of artificial intelligence: Mapping teachers’ AI-driven curriculum adaptation patterns10.1002/berj.4068 ↗
11. Artificial intelligence in teaching and teacher professional development: A systematic review10.1016/j.caeai.2024.100355 ↗
12. Do teachers’ professional vision and teaching experience always go hand in hand? Examining knowledge-based reasoning of Finnish Grade 1 teachers10.1016/j.tate.2021.103458 ↗
13. How the Bronfenbrenner Bio-ecological System Theory Explains the Development of Students’ Sense of Belonging to School?
14. Spencer, L.M., and Spencer, S.M. (1993). Competence at Work: Models for Superior Performance, John Wiley & Sons.
15. Gegenfurtner, A., Lewalter, D., Lehtinen, E., Schmidt, M., and Gruber, H. (2020). Teacher expertise and professional vision: Examining knowledge-based reasoning of pre-service teachers, in-service teachers, and school principals. Front. Educ., 5.10.3389/feduc.2020.00059 ↗
16. Collaborative Perspective on the Development of Innovative Talents in Universities
17. Research on the training mode of computer specialty innovative talents under artificial intelligence
18. Reform and Practice of Talent Training Mode for Innovative Talents in ICT under the Background of Industry-Education Integration
19. A Study on the Teachers’ Professional Knowledge and Competence in Environmental Education
20. Exploration of Collaborative Training Mode of Industry and Education in the Context of New Engineering
21. Exploration of the Path of “Engineering Practice” Teaching System Construction under the Background of New Engineering
22. Exploration of Industry-University Collaborative Training Mode for Computer Majors
23. (2025, October 15). NetLogo User Manual Version 6.0.3. Available online: http://ccl.northwestern.edu/netlogo/6.0.3/docs/headings.html.
24. Ministry of Education Department of Teachers (2023). Report on the Construction of Teachers in Vocational Colleges Nationwide, Technical Report.
25. Learning curve theory in professional development
26. AI-assisted teaching effectiveness in vocational education: A meta-analysis
27. Ministry of Education Department of Finance (2022). Statistical Announcement on the Implementation of National Education Funds, Technical Report.
28. Ministry of Industry and Information Technology (2023). White Paper on the Development of the Artificial Intelligence Industry, Technical Report.
29. Research on the Quantitative Assessment Method of Educational Policy Intensity
30. State Council (2019). National Vocational Education Reform Implementation Plan.