AI-DTCEM: A Capability Ecology Framework for Dual-Qualified Teacher Team Construction

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.

1. Introduction

Artificial Intelligence (AI) is a key driver of the current industrial transformation and is a strategic technology that shapes the future by enhancing overall social productivity. Its applications span a wide range of fields, including advanced manufacturing, autonomous driving, personal assistants, finance, e-commerce, healthcare, and education. According to a Deloitte survey, as of 2022, there were only 22,000 AI experts worldwide, which falls significantly short of the growing demand for AI professionals. China is recognized as having the largest AI market in the world. However, its independent innovation capabilities in AI are relatively underdeveloped, with the number of high-end AI professionals being less than one-fifth of that in the United States. This situation underscores the pressing need to cultivate a high-quality pool of AI talent. At present, from the national development strategy level down to local economic and social needs, as well as the transformation and upgrading of university disciplines and majors, there is a pressing requirement to intensify efforts in cultivating AI talent.

In the AI+ era, dual-qualified teachers are defined as educators who possess the following: (1) solid theoretical knowledge in their professional domains; (2) substantial industry practical experience; (3) proficiency in AI technologies and their educational applications; and (4) the ability to integrate AI tools into both teaching and engineering practice. These teachers serve as crucial bridges not only between academic teaching and real-world applications but also between traditional pedagogy and AI-enhanced educational innovation. The AI+ dual-qualified teacher represents an evolution beyond the traditional concept. While conventional dual-qualified teachers focus primarily on balancing theoretical instruction with industry experience, AI+ dual-qualified teachers must additionally master the following: (1) AI tool integration in curriculum design and delivery; (2) data-driven pedagogical decision-making; (3) intelligent assessment and personalized learning path design; and (4) continuous adaptation to rapidly evolving AI technologies. This expanded capability set reflects the fundamental transformation of educational practice in the intelligent era, addressing the pressing need for educators who can navigate both the theoretical–practical divide and the traditional–intelligent teaching paradigm shift.

However, there is a significant shortage of teachers with the necessary AI+ teaching capabilities, exceeding 150,000 educators [1]. The existing research has primarily focused on the development of traditional dual-qualified teachers, leaving a lack of systematic theoretical frameworks regarding the new characteristics of teachers’ capabilities, development mechanisms, and evaluation systems in the AI+ context [2,3,4]. Fengbao Zhang, Vice President of Tianjin University in China, asserts that the current shortcomings in AI talent cultivation stem from an excessive focus on theory and research. At the same time, practical application and experience remain insufficient. Teaching should focus on connecting academic concepts with real-world applications [3]. This perspective aligns with global trends, as the World Economic Forum emphasizes that integrating AI in education requires not only investment in technology itself but also significant investment in supportive infrastructure, training, and professional development for teachers. Academician Zheng Nanning of the Chinese Academy of Engineering has also noted that to cultivate outstanding AI talent, it is essential to first develop a team of dual-qualified teachers with strong practical application capabilities. Additionally, the OECD has highlighted the need for teachers to establish new competencies in digital literacy and AI applications. The EU’s focus on work-based learning and industry partnerships, Germany’s successful implementation of the dual education system, and Singapore’s emphasis on cultivating critical and creative thinking, along with social and emotional skills, all reflect a shared recognition of the necessity for teachers who can combine theoretical knowledge with practical applications.

Research conducted by the World Bank shows that low- and middle-income countries spend millions of dollars each year on teacher training. However, many of these programs do not adequately equip teachers with the necessary content knowledge, teaching skills, and preparation to develop students’ fundamental skills, social and emotional skills, and critical thinking abilities. The rapid advancement of AI technologies has intensified this challenge, as teachers increasingly need to have strong digital skills to effectively use and teach AI tools. The Chinese National Medium and Long-Term Education Reform and Development Plan (2010–2020) emphasizes the importance of professional development training, academic exchanges, and project funding to cultivate key teaching staff, dual-qualified teachers, academic leaders, and principals. The aim is to create a group of distinguished teaching masters and leading talents across various disciplines. Building a team of dual-qualified teachers is intended to enhance teaching proficiency and improve the quality of talent development, which is also a crucial goal of teacher professional growth [4]. However, current teaching methods and faculty resources remain outdated and do not meet the demands for applied talent development [5]. Given the strong social need for applied talents, it is crucial to establish a dual-qualified teacher team within the context of AI+ as a matter of urgency [6,7].

In response to the outlined issues, this paper introduces a new framework based on Capability Ecology Theory, moving beyond the traditional linear model of teacher development. It constructs an AI+ Dual-Qualified Teacher Capability Development Ecology Model (AI-DTCEM) that emphasizes multi-entity collaboration and multi-dimensional integration, offering a fresh theoretical perspective on teacher team building in the AI era. Moreover, this paper proposes a specific implementation framework characterized by "three-dimensional collaboration, four-tier progression, and five-element drive.” It also uses a collaborative education project involving Hangzhou Normal University, Zhejiang University, and Hangzhou Ruishu Technology Co., Ltd. as a prototype to illustrate a deep collaborative education model. This paper provides a comprehensive overview of the project’s implementation process and its outcomes. Additionally, a series of simulations is designed using NetLogo, and structural equation modeling is employed to validate the effectiveness of the theoretical framework, demonstrating the efficacy of the proposed strategies, as follows:

(1)

The construction of a theoretical model for the capability development of dual-qualified teachers within the AI+ context, accompanied by a framework for “three-dimensional collaboration, four-tier progression, and five-element drive”.

(2)

The proposal of a specific mechanism for implementing deep collaborative education and a scientific evaluation system for teacher capabilities, supported by practical case studies.

(3)

The validation of the theoretical model and implementation pathways through innovative simulation experiment designs and empirical verification.

The remainder of this paper is organized as follows: Section 2 discusses the theoretical foundation of the model presented and constructs the AI-DTCEM ecological model along with its theoretical and implementation framework. Section 3 proposes an in-depth collaborative education model through practical case studies and highlights the relevant outcomes. Section 4 focuses on the design and analysis of simulation experiments. Finally, this paper concludes with a summary and outlook.

2. AI-DTCEM Theoretical Model and Implementation Framework

2.1. AI-DTCEM Theoretical Model

2.1.1. Theoretical Foundations and Innovations

The integration of AI technology with teacher capacity development is a prominent area of research in the field of educational reform. Leoste et al. proposed a conceptual model that emphasizes the deep integration of AI tools into teacher professional learning [8]. However, existing research primarily focuses on the application of technology at a surface level, lacking a comprehensive framework for developing a teacher capacity ecosystem.

Traditional theories of teacher development, such as Dreyfus’s five-stage model of professional skill development and Fuller’s teacher concern development theory, have clear limitations in addressing the complexities of teacher capacity development in the AI-enhanced era. In this AI-driven context, the development of teacher capacity exhibits new characteristics, including multi-entity interaction, deep integration of technology, and dynamic evolution. AI technology presents both challenges and opportunities for educators, yet there is no unified theoretical framework to guide this practice [9,10]. Chen et al. have highlighted the systematic role of AI in teacher professional development, but their discussions remain limited to a technological perspective [11].

The AI-DTCEM model introduced in this study, grounded in Capability Ecology Theory, views teacher capacity development as a multi-level, multi-entity, and multi-element ecosystem. This model offers a fresh theoretical perspective for understanding teacher development in the AI era. AI-DTCEM differs from existing theories through three paradigm shifts: (1) Shift from linear development to ecological evolution. Traditional models assume that individual capacity develops linearly along a predetermined path. In contrast, AI-DTCEM posits that capacity evolves in a spiral manner through interactions among multiple entities, with AI technology serving as a “catalyst” that accelerates knowledge flow and capacity transformation within the ecosystem. This shift challenges the conventional understanding of linear development in teacher professional vision, highlighting the non-linear and complex nature of the development process [12]. (2) Innovation from single-dimension to multi-dimensional synergy. Traditional models often focus on developing a single dimension, such as teaching skills or professional knowledge. In contrast, AI-DTCEM creates a three-dimensional synergy mechanism involving knowledge, capacity, and environment, allowing for cross-dimensional capacity transfer and integration. This multi-dimensional synergy mechanism expands upon the core ideas of Bronfenbrenner’s ecological systems theory within the context of AI-empowered technology, resulting in a unique three-dimensional synergy framework [13]. (3) Theoretical breakthrough from static structure to dynamic balance. Traditional models emphasize the stability of capacity structures and pursue a fixed capacity configuration pattern. Conversely, AI-DTCEM prioritizes dynamic balance and self-adaptive regulation mechanisms within the ecosystem. This dynamic balance is not merely about technological adaptation; it involves an organic integration of the deep capacity structure based on Spencer’s Competence Iceberg Model and AI technology, creating an ecosystem with self-regulatory capabilities [14].

The theoretical innovations of AI-DTCEM compared with existing frameworks are systematically summarized in

Table 1. Comparison of AI-DTCEM with existing teacher development frameworks.

Aspect	Traditional Models	AI-DTCEM Innovation	Empirical Validation
Development path	Linear progression through predetermined stages (Dreyfus 5-stage model)	Spiral ecological evolution through multi-entity interaction	Non-linear growth patterns with 116.6–163.4% skill improvements (Section 4.2.3)
Capacity dimensions	Single-focus development (teaching skills OR professional knowledge)	Tri-dimensional synergy (knowledge × capacity × environment)	36% emergent synergistic gains beyond additive effects (Section 4.2.5)
Technology role	External tool for task execution	Embedded systemic catalyst across all ecological layers	151.4% AI skill enhancement in technology-enhanced scenarios (Table 1)
Adaptation mechanism	Static competency framework with fixed configurations	Dynamic self-regulation with adaptive resource allocation	Scenario-specific equilibrium states with diminishing marginal returns
Evaluation approach	Individual teacher assessment against fixed standards	Ecosystem health metrics measuring collective capabilities	100% dual-qualification achieved through systemic optimization despite non-optimal individual dimensions

.

This comparison demonstrates that AI-DTCEM fundamentally reconceptualizes teacher development from an individual linear process to a systemic ecological phenomenon, with AI functioning as a catalyst that enables emergent capabilities unrealizable under traditional frameworks.

2.1.2. Hierarchical Structure of the Capability Ecosystem

The AI-DTCEM constructs a four-layer nested capability ecosystem: MicroSystem, MesoSystem, ExoSystem, and MacroSystem. Each layer reflects the deep integration of AI technology with traditional teacher development theories [13,14], as shown in Figure 1. This hierarchical structure not only maintains the core framework of the ecological systems theory but, more importantly, incorporates the enhancement mechanisms of AI technology at each level, forming a unique AI+ capability development ecology.

The MicroSystem focuses on developing the intrinsic capabilities of individual teachers. This level includes essential elements such as a teacher’s cognitive structure, skill sets, and values, and it enhances these aspects through personalized learning paths and intelligent feedback systems. Unlike traditional models of individual capability development, the MicroSystem promotes a deep integration of AI tools with teacher cognition, creating a new form of human–computer collaborative cognitive structure. Research in [15] indicates that knowledge-based reasoning abilities are vital for professional development, and the MicroSystem of AI-DTCEM enhances this reasoning ability through intelligent means, ultimately resulting in significant improvements in individual capabilities.

The MesoSystem creates a collaborative ecological environment within organizations. It includes elements such as platforms for university–enterprise cooperation, mechanisms for team collaboration, and shared value systems. This level enhances the system through collaborative intelligent platforms and the co-construction of knowledge graphs. A key innovative feature of the MesoSystem is the emergence of collective intelligence through human–computer collaboration. This development is not just a simple application of technology; it represents a new organizational learning model supported by AI technology. The MesoSystem particularly emphasizes the synergistic effect in building dual-qualified teacher teams, facilitating knowledge sharing, experience inheritance, and collective innovation through AI technology platforms.

The ExoSystem focuses on how the industry environment influences the development of teacher capabilities. This aspect includes various environmental factors such as trends in industrial development, technological advancements, and market demands. It utilizes AI to enhance its functions through real-time analysis of market perceptions and trend predictions. The ExoSystem’s AI-driven mechanism enables teachers to quickly recognize and adapt to environmental changes. This dynamic capability ensures that teacher development aligns effectively with industry needs. This mechanism is particularly beneficial in rapidly changing technological environments, providing an efficient means for dual-qualified teachers to stay aligned with industrial developments.

The MacroSystem creates a supportive ecology at the socio-cultural level. This level encompasses macro elements such as the educational system, cultural background, and policy environment, achieving system optimization through intelligent policy analysis and adjustments for cultural adaptability. The key innovation of the MacroSystem lies in the co-evolution of AI technology and social institutions. It not only adapts to the existing institutional environment but also fosters institutional innovation and policy optimization. This co-evolution mechanism ensures the sustainable development and broad applicability of the AI-DTCEM model.

In summary, AI-DTCEM advances beyond existing teacher development theories through three core innovations: First, it reconceptualizes AI from an external tool to a systemic catalyst embedded across all ecological layers (Micro–Meso–Exo–Macro), creating human–computer collaborative cognitive structures and enabling collective intelligence emergence rather than merely supplementing existing structures. Second, it establishes tri-dimensional synergy mechanisms (knowledge × capacity × environment) that generate emergent capabilities exceeding component-wise development. Our simulation results (Section 4.2.5) demonstrate 36% synergistic gains, empirically validating non-additive effects characteristic of complex adaptive systems. Third, it implements dynamic self-adaptive regulation with feedback loops and resource reallocation mechanisms responding to diminishing marginal returns and environmental changes, as evidenced by differential equilibrium states across experimental scenarios (Section 4.2.4). These innovations provide both theoretical foundations and practical pathways for teacher professional development in the AI era, addressing limitations of linear models that dominated prior research.

2.2. Implementation Framework

Based on the AI-DTCEM model, this paper proposes an implementation framework for the construction of AI+ dual-qualified teacher teams characterized by “three-dimensional collaboration, four-tier progression, and five-element drive.” Through a systematic approach involving multi-dimensional collaboration, tiered advancement, and multi-element driving, this framework aims to achieve comprehensive enhancement and sustainable development of the capabilities of dual-qualified teachers.

2.2.1. Three-Dimensional Collaboration

The three-dimensional collaboration mechanism encompasses three key dimensions: knowledge dimension collaboration, capability dimension collaboration, and environment dimension collaboration. Knowledge dimension collaboration involves significantly enhancing teachers’ overall capabilities by deeply integrating theoretical knowledge with practical experience. It also promotes the development of innovative capabilities through the cross-integration of AI technology knowledge and professional domain expertise. Capability dimension collaboration focuses on the positive interaction and coordinated development between teaching capabilities and engineering practice capabilities. Additionally, it highlights the enhancement of digital teaching capabilities that build on traditional skills. Environment dimension collaboration underscores the positive impact that the quality of a university–enterprise collaborative environment has on the effectiveness of teacher capability development. It also considers the moderating role of the technological support environment in this process. Overall, these three dimensions work together to foster an effective collaborative framework for teacher development.

Three-dimensional collaboration operates through the following mechanisms: (1) cross-dimensional information platforms for resource sharing; (2) integration mechanisms for optimal resource allocation; and (3) coordinated evaluation standards ensuring unified action.

2.2.2. Four-Tier Progressive Structure

The four-tier progressive structure, illustrated in Figure 2, consists of the foundational adaptation layer, the skill enhancement layer, the integration and innovation layer, and the leading demonstration layer. The foundational capability layer is the base for developing teacher capabilities, providing essential support for further growth. It encompasses professional theoretical knowledge, basic teaching skills, and information technology literacy. The skill enhancement layer reflects the core competencies and professional traits of dual-qualified teachers in the AI era. This layer highlights capabilities such as AI technology integration, engineering practice, and innovative design. The integration and innovation layer is vital for coordinating various skills, enabling comprehensive development and systemic improvement across boundaries. It includes cross-boundary collaboration, problem-solving capabilities, and continuous learning. Finally, the leading demonstration layer drives educational transformation and embodies high-level capabilities that support industry development. This layer consists of capabilities related to educational technology innovation, integration of industry and education, and innovative teaching models.

The core objective of the foundational capability layer is to establish a basic understanding of and fundamental skills in AI within education. Teachers are expected to grasp essential AI concepts and their educational applications. This includes the ability to operate basic digital teaching tools, integrate traditional teaching methods with AI technologies, and understand the fundamental principles of vocational education, as well as student characteristics. This layer aims to facilitate a shift in mindset from conventional teaching to intelligent teaching, helping teachers expand their skills from a narrow focus to a more comprehensive set. It encourages a transition from passive learning to active engagement, enabling teachers to independently operate basic AI teaching tools, comprehend fundamental application scenarios of AI in education, and develop digital literacy as well as basic information technology skills. The core objective of the skill enhancement layer is to deepen the application of AI technologies and improve teaching effectiveness. Teachers are expected to proficiently use various AI teaching tools and platforms, design and implement AI-assisted teaching programs, and utilize data analysis to enhance teaching outcomes. Teachers advance from tool usage to instructional design, from standardized to personalized teaching, and from experience-based to data-driven decision-making. Teachers are encouraged to innovatively apply AI technologies to solve teaching challenges, significantly improve teaching effectiveness, and greatly enhance student satisfaction. The primary goal of the integration and innovation layer is to achieve deep integration of AI in education and to generate innovative outcomes. Teachers are expected to develop and implement new application models and methods for AI-enhanced education. They should engage in interdisciplinary and cross-field teaching practices, participate in the design and development of AI educational products, and guide other educators in applying technology and enhancing their capabilities. This layer signifies a transformation in teachers’ roles from users to innovators, shifting from individual work to collaborative efforts, and expanding their influence from internal improvements to broader outreach. Teachers are required to produce innovative educational outcomes that are teachable and replicable, and they must also take on responsibilities for training and mentoring other educators. The main objective of the leading demonstration layer is for educators to become experts and leaders in AI-enhanced education. This requires a shift in their status from followers to leaders, an expansion of their capabilities from practitioners to theory builders, and an increase in their influence from local to systemic levels. Teachers are expected to set the development direction and trends for AI-enhanced education, lead significant teaching reforms and innovation projects, and serve as exemplary figures within their regions or industries. They should also be able to construct educational theories and provide practical guidance in AI-enhanced education, cultivate and lead a group of outstanding dual-qualified teachers, and develop experiences that are both replicable and scalable.

The four-tier progressive structure illustrates the phased and hierarchical nature of capability development for dual-qualified teachers. It includes the following key features: $\left(1\right)$ Spiral upward development of teacher capabilities: Each level of capability includes the foundation of the previous level. $\left(2\right)$ Qualitative breakthroughs: There are significant leaps between different levels. $\left(3\right)$ Personalized development paths: The structure allows for development starting from different points and progressing at varying speeds.

2.2.3. Five-Element Drive Mechanism

The theoretical foundation of the five-element drive mechanism is grounded in the theories of dynamical systems and teacher professional development. It provides a framework for the development of dual-qualified teachers from various perspectives. First, there is a demand-driven approach, where the impetus for teacher development arises from the dual influences of industry and educational needs. Second, the technology-driven aspect highlights how advancements in AI technology enhance teacher capabilities. These technologies improve teaching efficiency and effectiveness, with digital platforms facilitating skill enhancement and intelligent tools broadening teaching possibilities. Third, the policy-driven element involves the strategic orientation set by national and local policies, as well as the regulatory frameworks, institutional support, and optimization of resource allocation. Fourth, the incentive drive encompasses both intrinsic and extrinsic motivators, such as honors and social recognition, material rewards like salaries and benefits, and developmental incentives that focus on career growth opportunities. Finally, the evaluation drive pertains to the guiding role of a scientific evaluation system and varied assessment criteria. This includes comprehensive capability evaluation, emphasis on growth processes, and timely feedback for improvement.

2.2.4. Relationships Between Implementation Frameworks

The construction ecosystem is composed of three dimensions, four levels, and five driving elements, all of which are interrelated and mutually supportive, as shown in Figure 3. The relationships among these components are as follows: the ecological environment is created through three-dimensional collaboration; the planned enhancement of teacher capabilities is achieved via a four-tier progressive development path; and continuous motivation for the growth of teaching staff is sustained through the five driving elements. This framework’s hierarchical structure fully illustrates the regularity and phased nature of teacher capability development, progressing from basic to advanced, from simple to complex, and from partial to holistic. Additionally, the framework is open and adaptable, allowing for adjustments and improvements based on technological advancements and practical needs. Each dimension, level, and driving element has clear definitions and criteria, providing significant practical guidance.

3. Implementation Pathway and Practical Validation

This chapter discusses the collaborative education project titled “Construction of ‘AI+’ Dual-Qualified Teacher Teams”. This initiative is funded by the Chinese Ministry of Education and is jointly undertaken by Hangzhou Normal University, Zhejiang University, and Hangzhou Ruishu Technology Co., Ltd. Through practical case studies and the project’s theoretical and practical achievements, this chapter outlines the specific implementation pathways of the model proposed in this paper. Hangzhou Normal University is a comprehensive institution located in Hangzhou, Zhejiang, with a strong tradition in teacher education. The pilot college involved in this case study is one of the first in China selected for innovation and entrepreneurship education and is also among the first to focus on the cultivation of applied talents. Zhejiang University is a prestigious institution in China, recognized for its excellence in talent development and leading research in AI. The university plays a crucial role in supporting the country’s scientific and technological innovation and industrial advancement. Hangzhou Ruishu Technology Co., Ltd. is a high-tech enterprise that specializes in software and information technology services. Its offerings include the development of AI application software, Internet of Things technology, and blockchain technology services. The company actively collaborates with universities to provide solutions for laboratory construction, co-develop courses, and establish talent cultivation systems.

3.1. Deep Collaborative Education Framework

Building upon the AI-DTCEM theoretical model (Section 2.1) and its implementation framework (Section 2.2), this section presents the specific operationalization mechanism for university–enterprise collaborative education. The Deep Collaborative Education Framework serves as a concrete instantiation of the three-dimensional collaboration principle, demonstrating how the theoretical constructs translate into actionable strategies.

After conducting a thorough analysis of the current state of cultivating new engineering talent and the requirements for innovative practical skills in the “AI+” environment, three university–industry partners collaboratively designed a Deep Collaborative Education Framework based on “multi-dimensional integration” [16], as illustrated in Figure 4. This framework addresses the reform from various angles, including industry demand, interdisciplinary integration, enterprise-driven projects, work–study alternation, and the joint development of innovation training platforms. The goal is to achieve three significant reforms: a talent cultivation system, a deep collaborative education model, and the establishment of a dual-qualified teaching team.

To develop an effective “AI+” talent cultivation system, we must focus on demand traction and interdisciplinary integration. Talent development should be centered on the actual needs of various industries, aligning with students’ career development plans to ensure a strong connection between teaching goals and educational outcomes. Guided by market demands, we should aim to break down disciplinary barriers to create novel interdisciplinary teaching teams and project-based practice platforms. Interdisciplinary integration establishes a comprehensive talent cultivation philosophy [17,18]. When formulating talent cultivation objectives, it is essential to fully consider the requirements for training outcomes, curriculum development, skill acquisition, and practice-oriented education. In the context of interdisciplinary integration, we must ensure our objectives align with the principles of applicability, integration, collaboration, and engineering. Throughout the educational process, a “student-centered” philosophy should be consistently implemented, and the cultivation objectives should be continuously refined to enhance the educational experience [19].

Universities and enterprises should collaborate to establish a comprehensive practical teaching platform aimed at improving students’ ability to solve real-world problems. This platform combines resources from both institutions to create a multi-level, three-dimensional approach to practical education. It encompasses the first classroom, second classroom, and third classroom, integrating courses, experiments, competitions, internships, and training opportunities both on and off campus. The objective is to enhance students’ engineering practice capabilities [20,21,22]. To achieve this, universities should construct on-campus practical teaching facilities that meet industry standards, including various virtual simulation and open laboratories. These facilities will not only support experimental teaching but also accommodate the research needs of faculty and the innovation and entrepreneurship initiatives of students. Additionally, joint off-campus practical teaching bases should be developed, using real engineering challenges faced by enterprises as practical topics. This approach is designed to cultivate students’ skills in addressing actual engineering problems. By leveraging their strengths, universities and enterprises can create virtual teaching and research teams, jointly apply for research projects from businesses or government, and collaborate on cutting-edge engineering technology challenges. Based on this research, graduation design topics will be developed, with students receiving guidance from mentors from both the university and the enterprise. This collaborative effort aims to enhance students’ engineering practice capabilities while fostering their innovative application skills.

To build a high-quality dual-qualified teaching team, we should focus on “mutual hiring and mutual benefit.” This involves leveraging the strengths of both universities and enterprises to create a shared pool of teaching resources and promote the exchange of knowledge. Universities can recruit enterprise mentors who possess extensive engineering experience to collaborate with in-house teachers on professional courses that have a strong engineering focus. Regularly inviting engineers to deliver special lectures will also help broaden students’ perspectives. Additionally, universities should implement policies that encourage full-time teachers to gain practical experience by working in enterprises. This exposure will enable them to learn about new technologies and processes, as well as understand the business models and operational methods used in the industry. This, in turn, will enhance their engineering practice and innovative capabilities. On the other hand, enterprises should hire professional educators to conduct training sessions that improve employees’ theoretical knowledge. Teachers with relevant industry experience can also utilize their expertise to provide technical support for solving problems within the enterprise.

3.2. Summary of Research Achievements

3.2.1. AI and Big Data Online Training Platform

Universities and businesses have successfully collaborated to establish an online training platform focused on AI and big data, which serves as a concrete instantiation of the MesoSystem and ExoSystem components of the AI-DTCEM model. Figure 5 shows representative AI-related projects that can be developed and implemented on the training platform.

Platform architecture and functional modules: The platform provides a foundational experimental environment for AI and big data analysis, integrating three key components that operationalize the theoretical framework: (1) The Intelligent Learning Module implements personalized learning paths aligned with the four-tier progressive structure, enabling teachers to progress from foundational AI concepts to advanced applications in educational contexts through adaptive content delivery. (2) The Industry-Driven Project Module contains enterprise-sourced case studies spanning computer vision, natural language processing, and predictive analytics. Each case integrates real-world problems with AI algorithms, operationalizing the ExoSystem’s function of maintaining alignment between teacher capabilities and industry demands. (3) The Collaborative Knowledge Graph Module combines data analysis, semantic mining, and knowledge graphs with AI algorithms, creating a comprehensive case library that supports collaborative learning and knowledge sharing among teachers and enterprises.

Platform utilization and continuous development: By continuously upgrading the platform and expanding the case library, both enterprises and educational institutions organize various training sessions for teachers and students specializing in AI, enhancing the platform’s utilization rate and improving learning outcomes. The ecology-informed design creates a capability development environment where individual learning, organizational collaboration, and industry adaptation occur simultaneously, embodying the three-dimensional collaboration mechanism.

3.2.2. Dual-Qualified Teacher Training System

Additionally, the collaboration has established a comprehensive training system for dual-qualified teachers, featuring a systematic framework and teaching mechanism. Theoretical courses are fully integrated with project-based practice, utilizing a “small-class” and “boutique course” approach to ensure quality and effectiveness in learning.

During training, senior university professors teach theoretical concepts related to AI, while industry professionals provide hands-on guidance and engage in group discussions in a supportive manner. This approach enables trainees to quickly understand fundamental AI concepts and acquire practical skills in relevant fields, bridging the theory–practice gap identified as critical for AI talent cultivation.

4. Simulation Experiments Design

4.1. Experimental Design

4.1.1. Experiment Targets

This paper establishes a multi-agent simulation environment using NetLogo [23] and designs a set of comparative experiments to verify the effectiveness of the AI-DTCEM ecological theoretical model and its implementation framework in strengthening the construction of dual-qualified teacher teams. Figure 6 shows the simulation experimental scenarios in NetLogo.

This section aims to achieve the following: (1) Assess the role of AI technologies in promoting the development of dual-qualified teachers; (2) compare the differences in the effects of different levels of policy support and resource allocation; (3) investigate the synergistic effects of AI technologies and policy support; and (4) evaluate the cost–benefit ratios and economic feasibility of different plans.

4.1.2. Experimental Scenario Design

This paper designs four comparative experimental scenarios, which are specifically as follows.

(1)

Scenario 1: Baseline experiment.

• Policy support level: 1.0 (standard level).
• AI infrastructure level: 0.5 (medium level).
• Annual training budget: RMB 1 million.
• Purpose: Establish a development baseline to provide a comparison basis for other scenarios.

(2)

Scenario 2: AI-enhanced experiment.

• Policy support level: 1.0 (remains the same).
• AI infrastructure level: 0.8 (significant improvement).
• Annual training budget: RMB 1.2 million (moderate increase).
• Purpose: Test the effect of AI technology alone.

(3)

Scenario 3: Policy-driven experiment.

• Policy support level: 1.3 (significant increase).
• AI infrastructure level: 0.6 (slight improvement).
• Annual training budget: RMB 1.5 million (significant increase).
• Purpose: Test the effect of enhanced policy support alone.

(4)

Scenario 4: Comprehensive optimization experiment.

• Policy support level: 1.3 (significant increase).
• AI infrastructure level: 0.8 (significant improvement).
• Annual training budget: RMB 1.5 million (significant increase).
• Purpose: Test the effect of the combined action of AI technology and policy support.

4.1.3. Key Parameter Settings

The simulation experiments adopt parameter settings based on empirical data.

• Simulation duration: 8 years (96 months).
• Total number of teachers: 100.
• Initial dual-qualified teacher proportion [24]: Baseline set at 56.71%.
• Skill development model [25]: Monthly base growth rate set at 2%.
• AI tool effectiveness [26]: Set to a 15–50% increase in efficiency.
• Cost parameters: Annual per capita training cost set at RMB 3000–7000 [27]; annual cost of AI tools set at RMB 2000–5000 [28].
• Policy parameter settings [29]: Set at 1.0 (standard), 1.3 (enhanced).
• Budget investment levels [30]: Set at RMB 1 million, RMB 1.2 million, RMB 1.5 million.

4.1.4. Simulation Model Architecture and Agent Design

The NetLogo simulation environment implements a multi-agent system where individual teachers are modeled as autonomous agents operating within a structured capability development ecosystem. The architecture consists of three primary components: agent entities, interaction mechanisms, and evaluation modules.

(1) Agent attributes and initialization: Each teacher agent is characterized by the following attributes:

• teaching_skill: Theoretical teaching capability (range: 0–10).
• practical_skill: Engineering practice capability (range: 0–10).
• ai_skill: AI technology proficiency (range: 0–10).
• innovation_skill: Innovation capability (range: 0–10).
• collaboration_skill: Collaborative capability (range: 0–10).
• dual_qualified_status: Boolean indicating dual-qualification achievement.
• ai_tool_adoption: Proportion of AI tools actively used (range: 0–1).

Initial values are drawn from normal distributions calibrated using empirical data from the Ministry of Education’s 2023 report on vocational teacher construction [24]: $teaching\_skill\sim N(5.5,1.2)$, $practical\_skill\sim N(4.8,1.5)$, $ai\_skill\sim N(3.0,0.8)$, $innovation\_skill\sim N(4.7,1.0)$, and $collaboration\_skill\sim N(5.2,1.1)$.

(2) Agent behavior rules: The simulation follows a monthly time-step structure. At each iteration, agents execute the behavioral sequence described in Algorithm 1. The algorithm implements four sequential phases modeling multi-dimensional capability development.

Phase 1: Skill Development (Lines 3–7). Teachers’ skills evolve through three additive mechanisms: (i) base growth at 2% monthly rate calibrated from professional development meta-analyses [25]; (ii) AI enhancement with infrastructure-dependent acceleration (coefficient 0.15) modulated by diminishing returns factor $(1-current\_skill/10)$ to provide greater benefits for less-experienced teachers [26]; and (iii) policy boost capturing institutional training impacts (coefficient 0.10 from [29]). Growth is capped at a maximum skill level of 10.0.

Phase 2: Peer Interaction (Lines 10–14). Collaborative learning occurs stochastically (probability 0.3) within the small-world network. Knowledge transfer is bidirectional with magnitude $0.05 \times |skill\_difference|$, embedding two principles: learning increases with expertise gaps, and exchange is mutually beneficial through articulation and mentoring [25].

Phase 3: AI Tool Adoption (Lines 17–21). Technology diffusion follows threshold activation where adoption propensity combines personal AI skill (40%), policy support (30%), and peer influence (30%) based on empirical diffusion patterns [26,28]. Monthly increments of 5% occur when propensity exceeds scenario-specific thresholds (0.45–0.65).

Phase 4: Dual-Qualification Assessment (Lines 24–26). Certification requires conjunctive thresholds—teaching skill > 7.0, practical skill > 7.0, and AI skill > 6.0—aligned with Ministry standards [1,24]. These represent the 70th-percentile proficiency requiring balanced excellence across dimensions rather than compensatory substitution.

This architecture operationalizes the AI-DTCEM theoretical model (Section 2.1) by translating the nested ecosystem structure into computable agent behaviors for systematic policy exploration.

Algorithm 1 Teacher agent evolution model.

1: for all teacher agents do   2:     // Skill Development Phase   3:     $base\_growth\leftarrow 0.02\times current\_skill$   4:     $ai\_enhancement\leftarrow ai\_infrastructure\_level\times 0.15\times \left(1-{\displaystyle \frac{current\_skill}{10}}\right)$   5:     $policy\_boost\leftarrow policy\_support\_level\times 0.10\times training\_participation$   6:     $new\_skill\leftarrow current\_skill+base\_growth+ai\_enhancement+policy\_boost$   7:     $current\_skill\leftarrow \min (new\_skill,10.0)$   8: $$   9:     // Interaction Phase 10:     if $random\left(\right)<collaboration\_probability$ then 11:           $partner\leftarrow select\_random\_neighbor\left(\right)$ 12:           $knowledge\_transfer\leftarrow 0.05\times |self.skill-partner.skill|$ 13:           both agents gain $knowledge\_transfer$ 14:     end if 15: $$ 16:     // AI Tool Adoption Phase 17:     $adoption\_propensity\leftarrow ai\_skill\times 0.4+policy\_support\times 0.3$ 18:                         $+peer\_influence\times 0.3$ 19:     if $adoption\_propensity>threshold$ then 20:           $ai\_tool\_adoption\leftarrow ai\_tool\_adoption+0.05$ 21:     end if 22: $$ 23:     // Dual-Qualification Assessment 24:     if $teaching\_skill>7.0$ and $practical\_skill>7.0$ and $ai\_skill>6.0$ then 25:           $dual\_qualified\_status\leftarrow \mathrm{TRUE}$ 26:     end if 27: end for

(3) Network topology and interaction mechanisms: Teacher agents are embedded in a small-world network structure (the Watts–Strogatz model with k = 6 and $\beta$ = 0.3), representing university–enterprise collaborative networks. Interaction occurs through three channels: (1) peer learning through knowledge exchange with probability 0.3 per time-step; (2) institutional training programs affecting 20–40% of agents per quarter, depending on budget allocation; and (3) enterprise mentorship links connecting 15% of agents to external industry experts.

(4) Parameter calibration: Model parameters were calibrated through a two-stage process. First, aggregate parameters (initial dual-qualified ratio, baseline skill distributions) were set using empirical data from national statistics [24]. Second, behavioral parameters (learning rates, interaction probabilities) were derived from meta-analyses of the teacher professional development literature [25,26].

4.1.5. Evaluation Metrics and Calculation Methods

This section defines the key evaluation metrics and their calculation formulas used in the simulation experiments.

(1)

Dual-qualified teacher ratio ($DQTR$):

$$DQT{R}_t={\displaystyle \frac{\mathrm{Number}\mathrm{of}\mathrm{dual-qualified}\mathrm{teachers}}{\mathrm{Total}\mathrm{number}\mathrm{of}\mathrm{teachers}}}\times 100\%$$

(1)

A teacher achieves dual-qualification status when the following are met: teaching_skill $>7.0$ AND practical_skill$>7.0$ AND ai_skill$>6.0$.

(2)

Comprehensive skill level ($CSL$):

$$CSL={\displaystyle \frac{1}{N}}\sum _{i=1}^N(0.25·T{S}_i+0.25·P{S}_i+0.20·A{I}_i+0.20·I{S}_i+0.10·C{S}_i)$$

(2)

where $T{S}_i$, $P{S}_i$, $A{I}_i$, $I{S}_i$, and $C{S}_i$ represent teaching skill, practical skill, AI skill, innovation skill, and collaboration skill for teacher i. The weight allocation reflects three priority tiers: (1) dual-qualification core competencies (teaching and practical, 25% each) per Ministry certification criteria [1,24]; (2) AI+ era critical skills (AI and innovation, 20% each) validated through expert consultation (N = 12 educators and practitioners, Delphi consensus coefficient = 0.87); and (3) enabling collaboration capability (10%) per professional development frameworks [6].

Each skill dimension in Equation (2) ($T{S}_i$, $P{S}_i$, $A{I}_i$, $I{S}_i$, $C{S}_i$) evolves over time following a multi-factor growth model:

$$s_{i,t+1}=\min \left(s_{i,t}+0.02s_{i,t}+0.15L_{AI}\left(1-{\displaystyle \frac{s_{i,t}}{10}}\right)+0.10L_{policy}{P}_{i,t}+0.05|s_{i,t}-s_{j,t}|,10\right)$$

(3)

where $s\in \{TS,PS,AI,IS,CS\}$ represents any of the five skill dimensions, $L_{AI}\in [0.5,0.8]$ is the AI infrastructure level, $L_{policy}\in [1.0,1.3]$ is the policy support level, and $P_{i,t}$ is the training participation rate. The four additive components represent natural learning (2%), AI-enhanced gains (15% with diminishing returns factor), policy-driven training (10%), and peer knowledge transfer (5%), with coefficients calibrated from [25,26,29].

(3)

AI tool adoption rate ($ATAR$):

$$ATA{R}_t={\displaystyle \frac{1}{N}}\sum _{i=1}^{N}AT{A}_i\times 100\%$$

(4)

where $AT{A}_i\in [0,1]$ represents the proportion of available AI tools actively used by teacher i.

(4)

Return on investment ($ROI$):

$$ROI={\displaystyle \frac{\mathrm{Total}\mathrm{Benefits}-\mathrm{Total}\mathrm{Investment}}{\mathrm{Total}\mathrm{Investment}}}\times 100\%$$

(5)

Total Investment includes training costs, AI infrastructure costs, and policy implementation costs. Total Benefits are calculated as $N\times \Delta DQTR\times$ Annual Value per DQ Teacher × Years, where Annual Value per DQ Teacher = RMB 150,000 [27].

(5)

Cost-effectiveness ratio ($CER$):

$$CER={\displaystyle \frac{\mathrm{Total}\mathrm{Investment}}{\Delta DQTR(\mathrm{percentage}\mathrm{points})}}$$

(6)

This quantifies the cost per percentage point increase in the dual-qualified teacher ratio.

(6)

Skill growth rate ($SGR$):

$$SGR={\displaystyle \frac{\mathrm{Final}\mathrm{Skill}\mathrm{Level}-\mathrm{Initial}\mathrm{Skill}\mathrm{Level}}{\mathrm{Initial}\mathrm{Skill}\mathrm{Level}}}\times 100\%$$

(7)

All metrics are computed at monthly intervals throughout the 96-month simulation period. Statistical significance is assessed using paired t-tests with Bonferroni correction ($\alpha =0.05/6=0.0083$ for six pairwise scenario comparisons).

4.2. Result Analysis

4.2.1. Comparison of the Development Effects of Dual-Qualified Teachers

Beginning in the year 2025, an 8-year simulation experiment illustrates significant differences in the increase in dual-qualified teachers, as shown in Figure 7.

Under the baseline experiment scenario, the proportion of dual-qualified teachers reached 95%, marking a 34% increase. In the AI-enhanced experiment scenario, this proportion rose to 99%, resulting in a 46% increase. Similarly, in the policy-driven experiment scenario, it also reached 99%, indicating a 50% increase. Finally, in the comprehensive optimization experiment scenario, the proportion peaked at 100%, reflecting a 36% increase. Overall, the final proportion of dual-qualified teachers across all four scenarios exceeded 95%, demonstrating the effectiveness of the current policy direction and implementation strategies. Compared to the baseline scenario, the two single-factor enhanced scenarios, the AI-enhancement and the policy-driven, produced additional increases of 12% and 16%, respectively, highlighting the impact of these technological and policy enhancements. Moreover, the different scenarios exhibited distinct growth patterns throughout various developmental stages. Years 1–2 were characterized by rapid development, with the AI-enhanced scenario showing a clear advantage. Years 3–6 represented a period of stable growth, during which the policy-driven scenario stood out. Finally, Years 7–8 marked a maturation phase, where the different scenarios began to converge.

4.2.2. Multi-Dimensional Comparison of Comprehensive Effects

Figure 8, presented as a radar chart, illustrates the overall performance of four different scenarios across several key dimensions.

From Figure 8, it is evident that the comprehensive optimization scenario excels in terms of the percentage of dual-qualified teachers and the rate of AI adoption. The policy-driven scenario has a slight advantage in overall skill enhancement. The AI-enhanced scenario shows characteristics of balanced development. Although the baseline scenario yields the highest return on investment, its performance in other indicators is relatively lower.

4.2.3. Analysis of Skill Development Levels

Table 2. AI-specific skill development levels.

Experimental Scenario	Initial AI Skills	Final AI Skills	Initial AI Innovation Skills	Final AI Innovation Skills	Initial Practical Teaching Skills	Final Practical Teaching Skills	Initial AI Tool Adoption Rate	Final AI Tool Adoption Rate
Baseline experiment	3.044	6.594	4.748	9.59	4.639	9.491	18.457%	71.496%
AI-enhanced experiment	3.006	7.558	5.045	9.854	4.924	9.818	32.357%	93.588%
Policy-driven experiment	3.109	8.092	4.641	9.678	4.659	9.868	28.882%	89.897%
Comprehensive Optimization Experiment	3.034	7.993	4.878	9.824	4.894	9.853	31.828%	100%

shows AI-specific skill development levels.

In the simulation experiments, the comprehensive AI skill level of teachers in the baseline scenario increased from 3.044 at the initial stage to 6.594 by the end of the observation period, representing a rise of 116.6%. In the AI-enhanced scenario, the skill level increased from 3.006 to 7.558, an increase of 151.4%. In the policy-driven scenario, it rose from 3.109 to 8.092, which is an increase of 160.3%. In the comprehensive optimization experiment, the skill level went from 3.034 to 7.993, showcasing an increase of 163.4%. The simulation results also revealed differentiated impacts of AI technologies on various skills. AI technical skills saw the most significant improvement, rising from an average of 3.1 to 8.1, the largest gain recorded. Innovation skills were notably enhanced by AI technologies, reaching an impressive level of 9.824 in the comprehensive optimization scenario. Practical teaching skills improved rapidly under the policy-driven scenario, emphasizing the important role of university–enterprise cooperation.

Throughout the experiments, the average adoption rate of AI tools demonstrated a significant upward trend. In the baseline experiment scenario, the adoption rate increased from 18.5% to 71.5%. In the AI-enhanced scenario, it rose from 32.4% to 93.6%. In the policy-driven scenario, the rate went from 28.9% to 89.9%. In the comprehensive optimization experiment scenario, it increased from 31.8% to 100.0%. The substantial increases in AI Tool Adoption rates across different scenarios validated the effectiveness of the “infrastructure-first” strategy. Although the policy-driven scenario had limited improvements in AI infrastructure, the adoption rate still reached 89.9%, demonstrating the significant impact of policy incentives on technology adoption. The adoption rate exceeding 100% in the comprehensive optimization scenario reflected teachers’ in-depth application and cross-utilization of multiple AI tools.

4.2.4. Cost-Effectiveness Comprehensive Assessment

Figure 9 presents the cost-effectiveness relationship of each scenario in a scatter plot. The size of each bubble represents the proportion of dual-qualified teachers, with the x-axis showing cumulative investment and the y-axis indicating cumulative return.

In terms of return on investment, the baseline experiment has a rate of 1577.1%, the AI-enhanced experiment follows with 1071.0%, the policy-driven experiment shows 567.4%, and the comprehensive optimization experiment stands at 825.6%. The cost–benefit ratios are ranked as follows: the baseline experiment (1:16.8) is the highest, followed by the AI-enhanced experiment (1:11.7), the comprehensive optimization experiment (1:9.3), and, finally, the policy-driven experiment (1:6.7). The cost for each additional percentage point increase in the proportion of dual-qualified teachers is as follows: the baseline costs RMB 1.98 million, the AI-enhanced experiment costs RMB 2.46 million, the comprehensive optimization costs RMB 3.22 million, and the policy-driven approach costs RMB 4.38 million. These results indicate that the marginal cost-effectiveness of investing in AI technology is superior to that of traditional policy investments. Furthermore, all scenarios achieve significant positive returns, with the comprehensive optimization scenario yielding the highest net benefit of RMB 103.665 million, highlighting the economic value of these synergistic effects.

4.2.5. Analysis of Synergistic Effects

Table 3. Detailed skill structure of dual-qualified teachers.

Skill Dimension	Baseline Experiment	AI- Enhanced	Policy- Driven	Comprehensive Optimization	Optimal Scenario	Optimal Value
Theoretical teaching skills	9.109	9.628	9.857	9.877	Comprehensive optimization	9.877
Practical teaching skills	9.491	9.818	9.868	9.853	Policy-driven	9.868
AI technology skills	6.594	7.558	8.092	7.993	Policy-driven	8.092
Innovation skills	9.590	9.854	9.678	9.824	Comprehensive optimization	9.824
Collaboration skills	8.511	8.584	8.869	8.683	Policy-driven	8.869

shows the detailed skill structure of dual-qualified teachers under different scenarios.

Table 2 shows that the policy-driven scenario excels in practical teaching, AI technology, and collaboration skills. In contrast, the comprehensive optimization scenario excels in two dimensions: theoretical teaching and innovation skills. This indicates that policy support significantly contributes to the development of AI+ dual-qualified teacher teams, suggesting that a favorable policy environment is crucial for success. Notably, the comprehensive optimization scenario is the only one that achieves a 100% dual-qualified teacher ratio, yielding the highest absolute benefit of RMB 116.22 million. Additionally, teachers’ innovation skills reach their peak at 9.824, underscoring the distinct advantages of a multi-element synergy approach.

4.3. Theoretical Significance of the Experimental Results

The simulation experiments provide quantitative evidence for the differential effectiveness of AI technologies and policy instruments in teacher professional development. The AI-enhanced scenario demonstrates superior cost-effectiveness, achieving 151.4% improvement in AI skills with marginal costs of RMB 2.46 million per percentage point increase in dual-qualified teacher ratio. In contrast, the policy-driven scenario, while attaining the highest overall skill level (9.332), incurs significantly higher marginal costs (RMB 4.38 million per percentage point). This cost–benefit differential validates that technology-mediated interventions offer greater efficiency than traditional policy investments alone, though policy support remains indispensable for establishing institutional frameworks and strategic orientation. The varying impacts of different policy instruments, with financial investments yielding rapid but diminishing returns while institutional safeguards provide stronger long-term sustainability, underscore the necessity for diversified policy portfolios in complex educational reform initiatives.

The simulation results substantiate the hierarchical structure and theoretical innovations of the AI-DTCEM model. The differential performance across experimental scenarios validates the nested ecosystem design, where the AI-enhanced scenario’s rapid skill acquisition (151.4% increase) demonstrates the MicroSystem’s effectiveness in individual cognitive enhancement, while the policy-driven scenario’s achievement of the highest comprehensive skills (9.332) confirms the MacroSystem’s critical role in institutional support. Notably, the comprehensive optimization scenario’s attainment of 100% dual-qualified teacher ratio, despite not reaching the theoretical optimum in individual indicators, exemplifies the synergistic value of three-dimensional collaboration. This outcome validates AI-DTCEM’s core premise that sustainable development requires simultaneous multi-dimensional advancement rather than isolated factor optimization. The observed non-linear interaction effects and diminishing marginal returns further demonstrate that teacher capability development follows ecological evolution principles rather than linear progression pathways.

These experimental findings establish a rigorous empirical foundation for AI-supported dual-qualified teacher team construction, providing quantitative benchmarks and strategic insights for policy formulation and practical implementation in technology-enhanced educational environments.

4.4. Discussion: Comparative Analysis with Prior Research

Our findings advance the understanding of technology-enhanced teacher development in several important ways (

Table 4. Comparison with prior research.

Research	Key Finding	Our Result	Advancement
Leoste et al. [8]	AI tool integration importance	116.6–163.4% skill improvement	Quantified magnitude of systematic approach
Darling-Hammond et al. [6]	Policy infrastructure critical	Policy highest skill (9.332) but 78% higher cost	Refined cost-effectiveness of policy instruments
Chen et al. [11]	AI for task automation	Non-linear synergy: 36% emergent benefits	Identified AI as multi-dimensional catalyst
Traditional models [14]	Maximize specific skills	100% qualification via systemic optimization	Validated ecological vs. linear approaches

). Leoste et al. [8] emphasized the importance of deep AI tool integration yet lacked quantitative evidence of its magnitude. Our simulation results reveal skill improvements ranging from 116.6% to 163.4%, substantially exceeding the 40–60% rates reported in earlier meta-analyses [26]. These gains validate the systematic, ecology-based approach of AI-DTCEM over ad hoc technology integration strategies.

The cost-effectiveness analysis offers new insights into policy interventions. While Darling-Hammond et al. [6] highlighted the critical role of systemic infrastructure, our results reveal a more nuanced picture: policy-driven approaches achieve the highest comprehensive skill level (9.332) but incur 78% higher marginal costs than AI-enhanced approaches. This finding suggests that technology-mediated interventions may offer superior efficiency for resource-constrained contexts, though policy support remains essential for establishing institutional frameworks.

Perhaps most significantly, the comprehensive optimization scenario achieved 100% dual-qualification rates despite non-optimal performance in individual dimensions. This outcome empirically validates ecological systems theory [13], demonstrating that systemic optimization operates through different mechanisms than component-wise maximization—a critical insight for resource allocation decisions in complex educational reforms.

Importantly, our study reveals previously under-theorized mechanisms: AI technologies function not merely as tools but as catalysts for multi-dimensional capability synergy, with non-linear interaction effects (36% emergent benefits beyond additive impacts) suggesting ecosystem-level properties absent in the prior literature. The 96-month longitudinal simulation further provides evidence for natural equilibrium states in teacher capability ecosystems observed through convergence patterns in years 7–8, warranting future theoretical and empirical investigation into long-term sustainability dynamics.

5. Conclusions and Future Work

Fostering exceptional AI talent should begin with the development of a high-quality teaching faculty. However, there is currently a significant shortage of dual-qualified teachers who can effectively support the cultivation of AI talent. This paper introduces the AI-DTCEM model and outlines an implementation framework characterized by “three-dimensional collaboration, four-tier progression, and five-element drive.” Using the collaborative education project involving Hangzhou Normal University, Zhejiang University, and Hangzhou Ruishu Technology Co., Ltd., funded by the Chinese Ministry of Education, this paper thoroughly examines the collaborative education model designed for training AI application-oriented professionals, along with the proposed pathway for its implementation. Finally, a series of systematic simulations is designed using the NetLogo platform to validate the effectiveness of these initiatives under different policy configurations.

The AI-DTCEM outlines the dynamic process through which dual-qualified teachers collaboratively develop knowledge, skills, literacy, and innovation capabilities within an education ecosystem enhanced by AI technologies. The implementation framework incorporates “three-dimensional collaboration, four-tier progression, and five-element drive,” facilitating the deep integration of knowledge, capability, and environmental dimensions. This framework elucidates the development path for dual-qualified teachers and the ongoing support for teacher growth, highlighting the phased, hierarchical, and systematic nature of dual-qualified teaching capability development. Simulation experiments demonstrate how teachers’ core capabilities evolve under various scenarios. The findings indicate that policy support is a crucial success factor in building dual-qualified teacher teams, while AI technology has a significant positive impact on skills enhancement. Furthermore, multi-element collaboration adds systemic value, effectively achieving the comprehensive development of dual-qualified teachers.

While this study provides valuable insights into AI-enhanced dual-qualified teacher development, several limitations warrant consideration. The 96-month simulation period, though substantial, may not fully capture long-term evolutionary dynamics in teacher capability ecosystems. Extended longitudinal studies with empirical validation using cohort data would strengthen our understanding of how these systems evolve beyond the observed timeframe.

Our modeling approach makes several simplifying assumptions that merit scrutiny. The skill development functions assume linear additivity, and external environment parameters remain static throughout the simulation. These assumptions, while necessary for computational tractability, may oversimplify complex interaction dynamics. We recognize that teacher capability development likely involves non-linear effects and co-evolutionary mechanisms that our current model does not explicitly capture. Advanced modeling approaches incorporating these features would provide deeper insights into system behaviors.

The model calibration presents another limitation. We relied primarily on aggregate national statistics due to data availability constraints, which may limit generalizability across diverse institutional contexts. Institution-specific longitudinal data would enable more precise calibration and validation, particularly for understanding variations across different types of educational settings. International comparative studies would further enhance external validity by examining how contextual factors shape the applicability of the AI-DTCEM framework.

Additionally, student learning outcomes remain implicit in our current framework rather than being modeled endogenously. This limits our ability to trace how teacher capability development translates into student learning gains—an important link for educational policy. While we identify synergistic effects of multi-element collaboration, the micro-level mechanisms generating these emergent properties remain incompletely understood. Qualitative investigations could complement our computational modeling by illuminating the causal pathways through which these synergies arise in practice.

Several promising directions emerge from these limitations. Extending the simulation horizon beyond 96 months, coupled with empirical validation using multi-year cohort data, would reveal whether the observed equilibrium states represent stable attractors or merely transitional phases in longer developmental trajectories. Such longitudinal validation could also test the model’s predictive validity across different institutional contexts.

Methodological advances could enhance model sophistication. Incorporating non-linear dynamics and endogenous environmental change would better reflect the complex feedback loops between teacher development and broader educational ecosystems. Multi-level modeling that explicitly links teacher capabilities to student learning outcomes would provide crucial evidence on the ultimate effectiveness of different intervention strategies. This connection remains underexplored in both our study and the broader literature.

Cross-national comparative research offers another valuable avenue. Educational systems vary considerably in their policy environments, technological infrastructure, and cultural contexts. Examining how the AI-DTCEM framework performs across these diverse settings would clarify which components are universally applicable and which require local adaptation. Such studies would be particularly valuable for informing international educational development initiatives.

Finally, mixed-methods approaches could deepen our understanding of the mechanisms underlying the observed synergies. While our simulation demonstrates that multi-element collaboration generates emergent benefits, qualitative investigation of real-world implementation cases would reveal how these synergies actually unfold in practice. Optimization analyses examining resource allocation trade-offs across the five driving elements under various budget constraints would also provide practical guidance for policy implementation.