Artificial Intelligence Integration on Vocational College Teachers' Professional Development: An Empirical Analysis Using Structural Equation Modeling

Artificial Intelligence Integration on Vocational College Teachers' Professional Development: An Empirical Analysis Using Structural Equation Modeling | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Artificial Intelligence Integration on Vocational College Teachers' Professional Development: An Empirical Analysis Using Structural Equation Modeling Jia Li, Yongchao He, Yu Xie, Yanting Chu, Nanyu Jiang, Zhuoxiang Lai This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7297038/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The coupling mechanism and influencing factors of artificial intelligence (AI) in empowering vocational college teachers' professional development. It analyzes the synergistic effects of three subsystems: technological penetration, teaching modes, and talent cultivation. The study highlights that AI technology promotes the transformation of teachers' knowledge system, capability system, and roles through technology embedding, data-driven approaches, and educational scenario reconstruction. Furthermore, through coupling coordination degree analysis, it is found that there is a strong synergy among the subsystems, especially in terms of personalized learning resources, classroom interaction, and the frequency of AI-driven smart classrooms. However, there is a mismatch between the talent cultivation subsystem and industry demand. Moreover, structural equation modeling (SEM) analysis further validates the positive influence of technological penetration, educational models, and talent cultivation on teachers' professional development, with a particular emphasis on the key role of talent cultivation. Finally, to address the deficiency in talent cultivation, the paper proposes countermeasures such as enhancing AI skill training for teachers, government guidance, and self-improvement among educators, aiming to support the high-quality advancement of teachers' professional development. Social science/Education Business and commerce/Information systems and information technology Physical sciences/Mathematics and computing Social science/Science technology and society Vocational college teachers' profession Artificial intelligence Coupling mechanism Structural equation model Figures Figure 1 Figure 2 Figure 3 1. Introduction The rapid development of artificial intelligence technology is reshaping the educational ecosystem [1]. Throughout the various historical stages of educational evolution, education has consistently maintained a deep integration and interaction with technologies that transform the global landscape [2]. As the primary platform for cultivating skilled talent in China, the high-quality development of vocational education not only concerns the progress of educational modernization but also directly impacts the transformation and upgrading of the economy and society. Vocational college teachers, being the key agents in vocational education, urgently require the deep integration of AI technology into the transformation of their professional competencies and role positioning. This integration is essential to address the challenges posed by the reconfigured educational scenarios, innovative teaching models, and evolving demands for teachers in the new era [3]. To advance educational modernization, enhance teachers' professional competence, and optimize their developmental pathways, it is imperative to explore the coupling mechanisms and influencing factors related to vocational college teachers. This exploration aims to achieve the dialectical unity between technology empowering education and education mastering technology. 2. Background 2.1 Study participants A total of 830 higher vocational education teachers were selected using purposeful sampling to participate in the study, with 817 valid survey responses collected, yielding a response rate of 98%. Purposeful sampling was employed as it allows researchers to precisely identify, select, and categorize information-rich cases within a qualitative research paradigm. The participants consisted of 368 male higher vocational education teachers (45%) and 449 female higher vocational education teachers (55%). Their teaching experience was distributed as follows: 5 to 10 years (15%), 10 to 20 years (55%), and 20 to 40 years (25%). The inclusion criteria for participants were as follows: (1) higher vocational education teachers with at least five years of teaching experience; (2) active engagement in teaching and research within their respective professional fields; (3) willingness to participate in in depth interviews. 2.2 Methods The synergistic coupling of AI in empowering teacher professional development for vocational education refers to the dynamic adaptation and co-evolutionary mechanisms formed between technology embedding, data-driven approaches, and the reconfiguration of educational scenarios. This occurs through the systemic integration of technological logic and educational logic within core dimensions of teacher development, including their knowledge systems, competency iteration, and role transformation [ 5 ]. Essentially, it represents a dialectical unity of bidirectional empowerment between AI technology and teacher agency within the vocational education ecosystem. This unity encompasses both the transformation of educational practices by technology and the reshaping of technological paradigms by educational actors [ 6 ]. AI technology is revolutionizing teacher professional education, offering multidimensional empowerment that disrupts traditional career development paradigms and drives deep reform from theory to practice [ 7 ]. Central to this is the coupling of AI and teacher education, defined as a bidirectional dynamic adaptation and co-evolution mechanism. This mechanism links AI technology applications with core aspects of teacher professional development (knowledge renewal, capability iteration, role transformation) [ 9 , 10 ], facilitated by technological embedding, data-driven strategies, and the reconstruction of educational contexts [ 8 ]. Figure 1 illustrates the three key manifestations of this coupling within AI-empowered teacher professional education. Technology Penetration: AI fosters the embedding of technology and the structural renewal of teachers' knowledge systems, forming a deeply interactive coupling relationship. Traditional teacher knowledge systems relied on linear knowledge transmission [ 11 ], where experts authored textbooks, and teachers delivered knowledge based on these syllabi. In contrast, the coupling between AI technology embedding and the structural renewal of teacher knowledge establishes a bidirectional mechanism characterized by the precision alignment of supply and demand and the dynamic adaptation of technology and knowledge [ 12 ]. This coupling transcends the inherent limitations of traditional linear transmission, propelling the teacher knowledge system from a "static storage-based" model towards a "dynamic metabolic" paradigm. Model of Teaching: AI facilitates the coupling between the deep expansion of educational scenarios and teacher role transformation, forging a relationship where teachers evolve from "knowledge authorities" to "intelligent collaborators". Teaching activities were highly dependent on specific physical classroom spaces [ 13 ], resulting in relatively singular and rigid organizational forms. While online teaching in recent years has overcome spatial constraints, it remains bound by temporal limitations. Teachers struggle to provide personalized, in-depth Q&A and tutoring online for extended periods, leading to unresolved student confusion [ 14 ]. The coupling between AI-enabled deep scenario expansion and teacher role transformation is essentially a dialectical unity of educational space reconstruction and professional identity reformation. This coupling, through the technological mediation of educational practice, drives the paradigm shift of teachers from traditional knowledge authorities to intelligent collaborators [ 15 ]. Self Improvement: AI promotes the mutual reinforcement and integration of data-driven approaches and teacher capability iteration [ 16 ], forming a coupling relationship based on the fusion of multi-source heterogeneous data. Traditional teacher capability iteration occurred through methods like enterprise research and exchange learning, exhibiting passivity due to dependence on opportunities for exchange and indicators like scholarly research level [ 17 ]. Conversely, the coupling between AI's data-driven nature and teacher capability iteration [ 18 ] enables the co-evolution of teacher capability elements and data elements within a closed loop of dynamic perception, intelligent diagnosis, and continuous optimization [ 19 ]. This coupling breaks through the traditional experience-dependent model of teacher capability development. Leveraging real-time feedback from data streams and dynamic adjustments via intelligent algorithms, it drives the transformation of the teacher capability system from static accumulation to dynamic metabolism [ 20 ]. These three coupling processes fundamentally represent the systematic integration of technological logic and educational logic across the entire lifecycle of teacher development. They propel comprehensive transformations in teachers' knowledge systems, capability frameworks, and role positioning, achieving a dialectical unity between "technology empowering teachers" and "teachers mastering technology". Ultimately, they provide novel theoretical and practical pathways for educational innovation and teacher professional growth [ 21 ]. As illustrated in Fig. 2 , the coupling of AI-empowered teacher professional education manifests primarily across three hierarchical levels: the Technology Penetration Layer, the Self-Enhancement Layer, and the Teaching Model Layer. Technology Application: This layer involves the reconstruction of knowledge production and dissemination pathways through technological tools such as intelligent knowledge graphs and virtual training systems. It manifests as AI technology providing novel knowledge resources, tools, and methodologies for teacher professional education [ 22 ]. This facilitates the continuous structural renewal of teachers' knowledge systems, drives innovation in both teaching content and pedagogical forms, and ultimately enables educational advancement. Self Enhancement: This layer leverages data-driven approaches to achieve precision and dynamism in teacher capability development. It manifests as the gradual emergence, driven by technology application, of self-reinvention models centered on data-driven processes [ 23 ]. This transformation impacts not only teaching practices but also teacher capability development itself. Teachers can utilize real-time feedback from intelligent data to achieve continuous optimization through the synergistic interplay of data-driven insights and their own capability iteration [ 24 ]. Teaching Model: This layer achieves the upgrading of educational objectives through role reconfiguration and scenario expansion [ 25 ]. It manifests as a shift from the traditional role of knowledge explorer to that of intelligent collaborator, thereby guiding teachers to form new teaching philosophies and talent cultivation concepts [ 26 ]. The fundamental goal is to cultivate high-order talents equipped to meet the demands of the new era [ 27 ]. These three layers interact and mutually influence one another, constituting an ecological cycle of Technology → Enhancement → Talent Cultivation → Technology. The Technology Layer, through the deep application of intelligent tools and algorithms, reconstructs the teacher knowledge system and propels the transformation of teaching models towards data-driven, intelligent forms. The Self Enhancement Layer, utilizing dynamic diagnostics and precise intervention mechanisms, translates technological advantages into practical impetus for the continuous leapfrogging of teacher capabilities. This fosters teacher self-reinvention, upgrading them from traditional knowledge acquisition to higher-order forms characterized by "human-machine collaborative innovation capacity". The Teaching Cultivation Layer, through the new demands and standards generated by teacher role transformation and educational scenario expansion, in turn drives the adaptive evolution and innovative iteration of technological tools and algorithms via a value feedback loop. This dynamic interplay forms a self-organizing educational ecosystem, promoting the continuous co-evolution of technology application, teaching models, and talent cultivation within a state of dynamic equilibrium. 3. Experimental Variables and Dataset Characterization 3.1 Data In this survey, the Technical and Vocational Education and Training sector in Hunan Province, China. Initial field visits were conducted as a preliminary investigation to inform the questionnaire design. Based on these insights, a structured questionnaire was developed. This instrument subsequently underwent pilot testing, where relevant teachers were invited to provide feedback. Following revisions and optimization, the questionnaire was validated by the pilot participants as possessing sound content validity and comprehensively covering the research themes. Subsequently, a comprehensive survey was administered utilizing a dual-mode (online and offline) distribution strategy to the target population of vocational college teachers in Hunan Province. Spanning a four-month data collection period, questionnaires were distributed to 830 vocational college teachers. During the data auditing phase, 817 valid responses were ultimately retained, yielding a high valid response rate of 98%, which exceeds the standard acceptability threshold for social science surveys. And validation factor analysis was used. Validation factor analysis, as an important statistical analysis method, is widely used in social science, psychology and education research. Its main role is to test whether the relationship between potential and observed variables in a theoretical model is consistent with the actual data. Validated factor analysis allows one to verify whether the hypothesized factor structure accurately reflects the intrinsic structure of the data and to assess the fit of the model. The questionnaire was subjected to validation factor analysis using SPSS 26.0. As shown in Table 1 , the questionnaire topics were three levels of technology application, teaching mode, and self-improvement, with a sample size of 817 for the validation factor analysis. Table 1 Validating factor Factor Technology coupling Instructional coupling Talent coupling Aggregation Analyzing sample size Amount 21 18 15 54 817 3.2 Experimental Variables According to the analysis of the data in Table 1 , the average common factor variance extraction (AVE) and the combined reliability (CR) were evaluated for the model, and the three levels of the average variance extraction AVE value was greater than 0.5, and the combined reliability CR value was greater than 0.7, and the results were shown in Table 2 , which indicated that the extraction of the measurements within the factors of the model was excellent. Table 2 Validation factor evaluation Factor Technological coupling Education coupling Talent coupling AVE 0.566 0.510 0.510 CR 0.959 0.940 0.943 As shown in Table 3 , the fit indicators for the validation factors are demonstrated. The chi-square degrees of freedom ratio X²/df = 1.06 0.05 indicate that the model has no statistically significant bias and meets the fitting requirements. gfi = 0.958 > 0.9 indicates that the model is a better fit to the sample data. rmsea = 0.009 < 0.10 indicates that the fitting error is very small, and the fit is satisfactory. rmr = 0.016 0.9, showing that the model has a very good fit compared to the independent model.NFI = 0.958 and NNFI = 0.997, which are both greater than 0.9, further proving the superiority of the model's fit, with the questionnaire content being highly correlated between each level. Table 3 Validation factor fit values Norm X²/df P GFI RMSEA RMR CFI NFI NNFI Numerical 1.060 0.074 0.958 0.009 0.016 0.998 0.958 0.997 The evaluation of the validated factor model and the analysis of the values of the validated factor fit proved that there was a high degree of correspondence, strong correlation, and strong intrinsic connection between the questions in the questionnaire and the three levels of technology application, teaching mode, and self-improvement. The results of the validated factor analysis show that the questions at each level can effectively reflect the characteristics of the latent variables, and the model is well fitted and can be analyzed for sexual coupling coordination. 3.3 Reliability Testing Reliability refers to the consistency, stability and reliability of the results of a measurement instrument over multiple measurements. It measures the degree of repeatability of results obtained when the same measurement tool is used by the same subject at different times or in different situations. The higher the reliability, the better the stability and consistency of the results, and the more accurately the instrument reflects the true characteristics of the subject. The core of reliability is to ensure that the measurement tool yields consistent results in different situations and to reduce the impact of random errors on the measurement results. As shown in Table 4 , according to the results of the folded half reliability analysis, the Cronbach's α coefficients of the first half and the second half of the questionnaire are 0.964 and 0.963, respectively, which show a very high level of internal consistency, indicating that the front and back parts of the questionnaire have a very good reliability. The total Cronbach's α coefficient was 0.968, which proved the high reliability of the overall scale. In addition, the correlation coefficient between the front and back parts was 0.984, indicating a high degree of consistency between the front and back halves of the questionnaire.The Spearman-Brown coefficient and the GuttmanSplit-Half coefficient, both of which were 0.984, also proved that the questionnaire's reliability was very high for the different splits, which indicated that the questionnaire had a very good level of reliability. 4. Experimental Results 4.1 Criteria for the indicator system In order to quantify the coupling strength of AI-enabled vocational education for higher vocational teachers, firstly, a weighted summation model is used to model the technology application subsystem (T), the teaching mode subsystem (E), and the teacher self-improvement (H) to match the corresponding indexes. The composite index of each subsystem is obtained by weighted summation of the normalized values of each index, and the weights are assigned based on the importance of the indexes in the overall system, and the sum of the weights is 1. Secondly, in order to eliminate the difference in the magnitude of the different indexes, the study adopts the Min-Max normalization analysis method to convert the data of each index into the values within the range of [0,1], which makes all the indexes under the under the same quantitative outline, avoiding the bias caused by different quantitative outlines. On this basis, the coupling strength analysis quantifies the interrelationship and the degree of coordinated development among multiple subsystems, calculates the coupling degree between systems, and evaluates the synergistic effect between different subsystems, and the higher the coupling degree, the stronger the coordination between systems. Finally, the coupling coordination degree analysis further examines the degree of synergistic evolution between subsystems, and calculates the average value of the subsystem comprehensive index to derive the overall education development level, and combines with the coupling degree index to assess the coordination between subsystems, and the closer the coupling degree of coordination is to 1, it means that the better the mutual promotion and coordination between subsystems. Design the sub-system index system. As shown in Table 5 , through brainstorming, literature research and other methods to explore the influence factors of AI on the higher vocational teachers' career after informal research + questionnaire research, excluding invalid data, the final design of technology application subsystems, teaching mode subsystems and teachers' self-improvement of the three categories, which consists of 6 observation indicators of technology application, 5 observation indicators of the teaching mode subsystems and 4 observation indicators of the teacher's self-improvement, a total of 15 observation indicators. The weights of the model were assigned. The weights of the technology penetration subsystem are more decentralized, focusing on the frequency, coverage, and auxiliary role of technology application in teaching. The teaching mode subsystem pays more attention to the coverage rate of personalized learning resources and student satisfaction, as they directly affect the teaching effect and learning experience. The weight of teacher self-improvement is more centralized, mainly focusing on teacher AI skill mastery score, AI-supported vocational skill improvement rate and teacher AI-related industry technology coverage, as it directly reflects the fit between teachers and AI. Table 5 Systematic indicator system Systems Coding Observation indicators Weights Technology infiltration T1 Utilization rate of AI teaching tools 0.12 T2 Coverage rate of AI teaching platforms 0.22 T3 Participation rate of AI training for teachers 0.18 T4 Frequency of use of AI smart classrooms 0.17 T5 Percentage of AI-assisted correction of assignments 0.16 T6 Penetration rate of AI hardware devices in schools 0.15 Teaching model E1 Coverage of AI personalized learning resources 0.23 E2 Frequency of AI participation in classroom interactions 0.18 E3 Percentage of courses using intelligent recommendation systems 0.17 E4 Student satisfaction rating of AI teaching 0.22 E5 Number of AI-supported teaching reform projects 0.20 Self elevate H1 Percentage of AI-assisted teacher research surveys 0.21 H2 Teacher AI skill mastery scores 0.25 H3 AI-supported occupational skill enhancement 0.26 H4 Teacher AI industry skill coverage 0.28 4.2 Results and analysis Referring to the existing coupling coordination degree classification standard [ 17 ], the coupling coordination degree is classified into ten levels using a uniform distribution. Table 6 Coupling harmonization results Systems Encodings Coupling C-value Harmonization index T-value Harmonization coupling D-value Level Coordination coupling Degree Technology infiltration T1 0.602 0.703 0.651 7 Primary T2 0.889 0.748 0.816 9 Good T3 0.891 0.801 0.845 9 Good T4 0.942 0.742 0.836 9 Good T5 0.832 0.703 0.765 8 Intermediate T6 0.887 0.774 0.829 9 Good Teaching model E1 0.949 0.781 0.861 9 Good E2 0.832 0.735 0.782 8 Intermediate E3 0.888 0.755 0.819 9 Good E4 0.940 0.755 0.842 9 Good E5 0.866 0.676 0.765 8 Intermediate Self elevate H1 0.895 0.742 0.815 9 Good H2 0.416 0.213 0.297 3 Intermediate H3 0.897 0.768 0.830 9 Good H4 0.345 0.219 0.275 3 Intermediate Based on the calculation results of the coupling coordination degree analysis, the coupling strength values of the subsystems and their corresponding coordination degree assessments are displayed. As shown in Table 6 , most of the subsystems T (T1 to T6), which are related to the use of AI teaching tools, platform coverage, teacher training, etc., have high coupling strengths, and the degree of coordination is in the range of “good coordination” or “medium-strong coordination”, indicating that There is a strong synergy between these subsystems. In particular, T4 has a coupling strength of 0.942 and a coordination degree of 8, indicating its close connection in the system. Subsystem E (E1 to E5), which is mainly involved in personalized learning resources, classroom interaction frequency, and intelligent recommendation system, shows high coupling strength and coordination, and E1 has a coupling strength of 0.949 and a coordination degree of 9, indicating its important role in the overall system. Subsystem H (H1 to H4), which involves the proportion of AI-assisted teacher research surveys, teacher AI skill mastery scores, etc., has a coupling strength of 0.895 and a coordination degree of 9. However, the coupling strength of H4 is lower, only 0.345, and a coordination degree of 3, showing a lower degree of coordination. Through the analysis of the coupling coordination degree, the results show that there is a high coupling strength and coordination between technology penetration, teaching mode and self-improvement, especially the subsystems of E1, E3 and T4, which show strong synergistic effect, indicating that the application of AI technology and the teaching mode are more tightly coupled in the education system, which can effectively promote the synergistic development of each link. However, the coupling strength of the H4 subsystem is low and the degree of coordination is poor, indicating that this subsystem has certain deficiencies in the matching of education and industry needs, and needs to be further optimized. Overall, although the coupling and coordination between most of the subsystems is relatively satisfactory, the coordination of a few subsystems still needs to be focused on and improved, especially in the connection of teachers' AI skill mastery. 5. Discussion Based on the above research structure using Structural Equation Modeling (SEM), SEM is a multivariate statistical analysis method used to study complex relationships between variables. Combining the characteristics of causal analysis and regression analysis, it can deal with multiple dependencies at the same time and consider the relationship between latent variables (i.e., variables that are not directly measurable) and observed variables (variables that can be directly measured). According to the coupling mechanism to study the impact of AI technology penetration, classroom teaching mode, and teacher self-improvement on teachers' professional development, and to explore the relationship between the weight and positive influence of teacher self-improvement on professional development. Teacher self-improvement is analyzed horizontally and vertically, and the degree of influence and path weights of teacher self-improvement horizontally and vertically on career development in the case of coupling of the three dimensions are sorted out from the influence relationship between technology integration and technology coverage. According to the structural equation modeling shown in Fig. 3 , the CMIN/DF of the model can be obtained as 3.901, which is greater than 3 and less than 5, and is within a reasonable range, GFI = 0.918 and AGFI = 0.898, which are all greater than 0.8, TLI = 0.962, IFI = 0.967, and CFI = 0.967, which are all greater than 0.9, and AIC = 973.971, BIC = 1228.076, which are relatively small indicators, and ECVI = 0.294. RMSEA = 0.060, which is less than 0.08 but still shows a small fitting error of the model. Most of the indicators meet the research criteria, therefore, it can be concluded that the model maintains a good balance between goodness of fit and complexity, the fit is ideal and suitable for data analysis, and the overall performance of the model shows a good degree of fit. The standardized coefficients of the above factors are all greater than 0 and P < 0.05, which proves that all of these factors have a significant positive correlation effect on career development, and proves that AI positively affects teachers' career development. Among them, the standardized coefficient of technology integration is 1.82, which is in the first place. If the degree of AI-enabled teacher career development is approximated to the AI-enabled teacher career development validity, it can be seen in the modeling results of teacher career development factors that technology penetration and teaching mode act simultaneously on self-improvement, and the values of the impact of both technology integration and technology coverage on teacher career development are positive, which indicates that all these factors have a positive predictive effect. From this result, it can be seen that the most influential factor on teachers' career development is technology integration, while technology coverage is also highly influential, so it should be emphasized in the process of teachers' talent development. 6. Conclusion Empirical analysis using coupling coordination degree and Structural Equation Modeling reveals that the coupling between talent cultivation and teacher professional development is suboptimal. This is primarily manifested in the low scores for teachers' mastery of AI skills and the inadequate coverage of AI-related industry technologies among teachers (H2, H4). Furthermore, the SEM results indicate that talent cultivation exerts the most substantial influence on teacher professional development among the factors examined. Consequently, the following conclusions can be drawn regarding teachers' mastery of AI skills and the coverage of AI-related industry technologies: The government plays a crucial role in promoting AI education in higher vocational colleges and universities, especially in addressing the insufficient mastery of teachers' AI skills. The government can encourage schools to integrate AI technology in teaching by formulating policies and increasing funding. These funds can be used for the construction of AI teaching facilities, curriculum content development, and professional training for teachers to ensure that teachers have sufficient resources and support to master AI technology. In addition, the government should also promote cooperation between higher vocational institutions and AI technology enterprises. Enterprises can provide schools with resources such as technical support, financial assistance and internship opportunities to help teachers and students better understand and apply AI technology. Through the guiding role of the government, teachers can receive more comprehensive training in AI applications and improve their technical skills in education, thus solving the problem of insufficient mastery of teachers' AI skills and effectively increasing the coverage of teachers' AI-related industry skills. Pilot AI teaching application education for teachers in schools. Pilot AI teaching application education for teachers in local schools is an important step in addressing the lack of mastery of teachers' AI skills. The government should provide financial support to help schools build relevant hardware facilities and teaching platforms so that teachers can access and learn the latest AI technologies. Through these funds, schools should strengthen teachers' practical teaching ability and purchase AI-related software tools and experimental equipment to ensure that teachers are able to apply AI technology in actual teaching and improve their technical level and classroom teaching quality. At the same time, schools should regularly organize AI-related teaching seminars and exchanges to help teachers understand and master the application of AI technology in different disciplines, and inspire them to continuously explore innovative teaching methods. Through this kind of pilot education, teachers can not only enhance their ability to apply AI technology, but also optimize their teaching methods through actual teaching practice, so as to gradually improve the overall technical level of teachers and solve the problem of insufficient coverage of teachers' AI-related industry technology. Teachers' personal attention to AI education directly affects their mastery and application of AI skills. Teachers should actively improve their knowledge and understanding of AI technology, and enhance their learning and mastery of AI technology by participating in online courses, training courses and industry seminars. This process of self-improvement helps to make up for the deficiencies in teachers' AI skills and effectively improve their application ability. Higher vocational teachers, in particular, should integrate AI technology into their daily teaching, not only limiting themselves to theoretical explanations, but also allowing students to experience the application of AI technology in real-life scenarios by means of practical sessions and intelligent assessments, so as to enhance their practical ability and innovative thinking. Teachers should also actively participate in professional development communities, communicate with peers and industry experts, and continuously improve their teaching and technical skills. Through personal attention to AI education, teachers are able to gradually solve the problem of insufficient mastery of AI skills in practice, thus promoting high-quality progress in their professional development. Declarations Acknowledgements We would like to express our gratitude to all the esteemed experts and teachers who participated in interviews, completed the questionnaire, and assisted us in conducting this research. Author contributions Jia Li participated in research design and conceptualization, provided preliminary ideas for the core ideas of the research; Yongchao He led the overall research design and technical routes of the research; Yu Xie assisted in chart drawing and data visualization; Yanting Chu assisted in proofreading the details of the paper, checked the consistency between data tables and text descriptions to ensure the rigor of the research; Nanyu Jiang and Zhuoxiang Lai provided an international perspective for improving teachers' AI skill training. All authors have read and approved the final version of the manuscript. All authors have read and approved the final version of the manuscript. Funding The work is supported by the Research on Innovation of Craftsmanship Culture Education Model in Higher Vocational Colleges under the Background of "Three Highs and Four News" Strategy (No XJK22CZY032) and the 2022 Hunan Provincial Education Science Research Project of the 14th Five-Year Plan. Data Availability Statement The datasets generated and/or analyzed within this study are not publicly available due to the inclusion of personal privacy information of study participants, but can be obtained from the corresponding author upon reasonable request. Competing interests The authors declare no competing interes. Ethics approval and consent to participate The researchers explained the research objectives to the participants in the questionnaire’s introduction. 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Editorial Department of This Journal et al. Annual Report on Frontier and Hot Issues of Chinese Education Research in 2024[J]. Educational Research, 46(02): 40–58. (2025). Editorial Department of this Journal. Annual Report on Frontier and Hot Issues of China's Educational Research in 2019[J]. Educational Research. 41(02):17–32. (2020). Tobin, K., Ritchie, S. M. & Multi-method Multi-theoretical, Multi-level Research in the Learning Sciences[J]. Asia-Pacific Education Researcher, (1). (2012). Zhu, Z. T., Dai, L., Zhao, X. W. & Shen, S. S. Cultivation of New-Quality Talents: New Mission of Education in the Digital and Intelligent Era[J] 4552–60 (E-education Research, 2024). 01. Additional Declarations No competing interests reported. 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He","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAlklEQVRIiWNgGAWjYNACAxsefv4GkrQUpMlIzjhAkpYPh20MGhKIVGwudjpN6obBeR4DhgOMHz7mEKHFcnbuNukcg9s85swNzJIztxGhxeB27rbbIC2WDQfYmHlJ0HKOx+BAAmlaDpCmZfvvHINkHskZB5uJ9stm45w/dvb8/M0HP3wkRgsSYGwgTf0oGAWjYBSMAtwAAA/fNzQThKWzAAAAAElFTkSuQmCC","orcid":"","institution":"Hunan Vocational College of Railway Technology","correspondingAuthor":true,"prefix":"","firstName":"Yongchao","middleName":"","lastName":"He","suffix":""},{"id":519277270,"identity":"6bce5e84-f115-4937-8fd7-0a0c315b3023","order_by":2,"name":"Yu Xie","email":"","orcid":"","institution":"Hunan Vocational College of Railway Technology","correspondingAuthor":false,"prefix":"","firstName":"Yu","middleName":"","lastName":"Xie","suffix":""},{"id":519277271,"identity":"c36fb13b-14ee-49ac-9adc-c80f85b1f8d8","order_by":3,"name":"Yanting Chu","email":"","orcid":"","institution":"Hunan Vocational College of 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1","display":"","copyAsset":false,"role":"figure","size":106286,"visible":true,"origin":"","legend":"\u003cp\u003eCoupled forms of AI teaching profession\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7297038/v1/6499cdb560c36a9c7e041630.png"},{"id":92608781,"identity":"178b98b0-c8fc-48ed-a878-8126d074b77a","added_by":"auto","created_at":"2025-10-01 15:45:27","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":104574,"visible":true,"origin":"","legend":"\u003cp\u003eAI empowers teachers' professional education\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7297038/v1/30b6026f08d5b4238ab64e60.png"},{"id":92609270,"identity":"043132a4-b598-4dec-b5ab-ae2e53b13ebb","added_by":"auto","created_at":"2025-10-01 15:53:27","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":139572,"visible":true,"origin":"","legend":"\u003cp\u003eStructural Equation Modeling\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7297038/v1/5638aa0e96d32a53823c2330.png"},{"id":92610224,"identity":"83171eb0-2be9-46d1-a659-6d7576f38b52","added_by":"auto","created_at":"2025-10-01 16:09:30","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1120354,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7297038/v1/df294b64-dd0a-401a-be51-53b11c29cf34.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Artificial Intelligence Integration on Vocational College Teachers' Professional Development: An Empirical Analysis Using Structural Equation Modeling","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe rapid development of artificial intelligence technology is reshaping the educational ecosystem [1]. Throughout the various historical stages of educational evolution, education has consistently maintained a deep integration and interaction with technologies that transform the global landscape [2]. As the primary platform for cultivating skilled talent in China, the high-quality development of vocational education not only concerns the progress of educational modernization but also directly impacts the transformation and upgrading of the economy and society. Vocational college teachers, being the key agents in vocational education, urgently require the deep integration of AI technology into the transformation of their professional competencies and role positioning. This integration is essential to address the challenges posed by the reconfigured educational scenarios, innovative teaching models, and evolving demands for teachers in the new era [3]. To advance educational modernization, enhance teachers\u0026apos; professional competence, and optimize their developmental pathways, it is imperative to explore the coupling mechanisms and influencing factors related to vocational college teachers. This exploration aims to achieve the dialectical unity between technology empowering education and education mastering technology.\u003c/p\u003e"},{"header":"2. Background","content":"\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Study participants\u003c/h2\u003e\u003cp\u003eA total of 830 higher vocational education teachers were selected using purposeful sampling to participate in the study, with 817 valid survey responses collected, yielding a response rate of 98%. Purposeful sampling was employed as it allows researchers to precisely identify, select, and categorize information-rich cases within a qualitative research paradigm. The participants consisted of 368 male higher vocational education teachers (45%) and 449 female higher vocational education teachers (55%). Their teaching experience was distributed as follows: 5 to 10 years (15%), 10 to 20 years (55%), and 20 to 40 years (25%). The inclusion criteria for participants were as follows: (1) higher vocational education teachers with at least five years of teaching experience; (2) active engagement in teaching and research within their respective professional fields; (3) willingness to participate in in depth interviews.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Methods\u003c/h2\u003e\u003cp\u003eThe synergistic coupling of AI in empowering teacher professional development for vocational education refers to the dynamic adaptation and co-evolutionary mechanisms formed between technology embedding, data-driven approaches, and the reconfiguration of educational scenarios. This occurs through the systemic integration of technological logic and educational logic within core dimensions of teacher development, including their knowledge systems, competency iteration, and role transformation [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Essentially, it represents a dialectical unity of bidirectional empowerment between AI technology and teacher agency within the vocational education ecosystem. This unity encompasses both the transformation of educational practices by technology and the reshaping of technological paradigms by educational actors [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. AI technology is revolutionizing teacher professional education, offering multidimensional empowerment that disrupts traditional career development paradigms and drives deep reform from theory to practice [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Central to this is the coupling of AI and teacher education, defined as a bidirectional dynamic adaptation and co-evolution mechanism. This mechanism links AI technology applications with core aspects of teacher professional development (knowledge renewal, capability iteration, role transformation) [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], facilitated by technological embedding, data-driven strategies, and the reconstruction of educational contexts [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e illustrates the three key manifestations of this coupling within AI-empowered teacher professional education.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eTechnology Penetration: AI fosters the embedding of technology and the structural renewal of teachers' knowledge systems, forming a deeply interactive coupling relationship. Traditional teacher knowledge systems relied on linear knowledge transmission [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], where experts authored textbooks, and teachers delivered knowledge based on these syllabi. In contrast, the coupling between AI technology embedding and the structural renewal of teacher knowledge establishes a bidirectional mechanism characterized by the precision alignment of supply and demand and the dynamic adaptation of technology and knowledge [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. This coupling transcends the inherent limitations of traditional linear transmission, propelling the teacher knowledge system from a \"static storage-based\" model towards a \"dynamic metabolic\" paradigm.\u003c/p\u003e\u003cp\u003eModel of Teaching: AI facilitates the coupling between the deep expansion of educational scenarios and teacher role transformation, forging a relationship where teachers evolve from \"knowledge authorities\" to \"intelligent collaborators\". Teaching activities were highly dependent on specific physical classroom spaces [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], resulting in relatively singular and rigid organizational forms. While online teaching in recent years has overcome spatial constraints, it remains bound by temporal limitations. Teachers struggle to provide personalized, in-depth Q\u0026amp;A and tutoring online for extended periods, leading to unresolved student confusion [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. The coupling between AI-enabled deep scenario expansion and teacher role transformation is essentially a dialectical unity of educational space reconstruction and professional identity reformation. This coupling, through the technological mediation of educational practice, drives the paradigm shift of teachers from traditional knowledge authorities to intelligent collaborators [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eSelf Improvement: AI promotes the mutual reinforcement and integration of data-driven approaches and teacher capability iteration [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], forming a coupling relationship based on the fusion of multi-source heterogeneous data. Traditional teacher capability iteration occurred through methods like enterprise research and exchange learning, exhibiting passivity due to dependence on opportunities for exchange and indicators like scholarly research level [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Conversely, the coupling between AI's data-driven nature and teacher capability iteration [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] enables the co-evolution of teacher capability elements and data elements within a closed loop of dynamic perception, intelligent diagnosis, and continuous optimization [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. This coupling breaks through the traditional experience-dependent model of teacher capability development. Leveraging real-time feedback from data streams and dynamic adjustments via intelligent algorithms, it drives the transformation of the teacher capability system from static accumulation to dynamic metabolism [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThese three coupling processes fundamentally represent the systematic integration of technological logic and educational logic across the entire lifecycle of teacher development. They propel comprehensive transformations in teachers' knowledge systems, capability frameworks, and role positioning, achieving a dialectical unity between \"technology empowering teachers\" and \"teachers mastering technology\". Ultimately, they provide novel theoretical and practical pathways for educational innovation and teacher professional growth [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eAs illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, the coupling of AI-empowered teacher professional education manifests primarily across three hierarchical levels: the Technology Penetration Layer, the Self-Enhancement Layer, and the Teaching Model Layer.\u003c/p\u003e\u003cp\u003eTechnology Application: This layer involves the reconstruction of knowledge production and dissemination pathways through technological tools such as intelligent knowledge graphs and virtual training systems. It manifests as AI technology providing novel knowledge resources, tools, and methodologies for teacher professional education [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. This facilitates the continuous structural renewal of teachers' knowledge systems, drives innovation in both teaching content and pedagogical forms, and ultimately enables educational advancement.\u003c/p\u003e\u003cp\u003eSelf Enhancement: This layer leverages data-driven approaches to achieve precision and dynamism in teacher capability development. It manifests as the gradual emergence, driven by technology application, of self-reinvention models centered on data-driven processes [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. This transformation impacts not only teaching practices but also teacher capability development itself. Teachers can utilize real-time feedback from intelligent data to achieve continuous optimization through the synergistic interplay of data-driven insights and their own capability iteration [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eTeaching Model: This layer achieves the upgrading of educational objectives through role reconfiguration and scenario expansion [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. It manifests as a shift from the traditional role of knowledge explorer to that of intelligent collaborator, thereby guiding teachers to form new teaching philosophies and talent cultivation concepts [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. The fundamental goal is to cultivate high-order talents equipped to meet the demands of the new era [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThese three layers interact and mutually influence one another, constituting an ecological cycle of Technology \u0026rarr; Enhancement \u0026rarr; Talent Cultivation \u0026rarr; Technology. The Technology Layer, through the deep application of intelligent tools and algorithms, reconstructs the teacher knowledge system and propels the transformation of teaching models towards data-driven, intelligent forms. The Self Enhancement Layer, utilizing dynamic diagnostics and precise intervention mechanisms, translates technological advantages into practical impetus for the continuous leapfrogging of teacher capabilities. This fosters teacher self-reinvention, upgrading them from traditional knowledge acquisition to higher-order forms characterized by \"human-machine collaborative innovation capacity\". The Teaching Cultivation Layer, through the new demands and standards generated by teacher role transformation and educational scenario expansion, in turn drives the adaptive evolution and innovative iteration of technological tools and algorithms via a value feedback loop. This dynamic interplay forms a self-organizing educational ecosystem, promoting the continuous co-evolution of technology application, teaching models, and talent cultivation within a state of dynamic equilibrium.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Experimental Variables and Dataset Characterization","content":"\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Data\u003c/h2\u003e\u003cp\u003eIn this survey, the Technical and Vocational Education and Training sector in Hunan Province, China. Initial field visits were conducted as a preliminary investigation to inform the questionnaire design. Based on these insights, a structured questionnaire was developed. This instrument subsequently underwent pilot testing, where relevant teachers were invited to provide feedback. Following revisions and optimization, the questionnaire was validated by the pilot participants as possessing sound content validity and comprehensively covering the research themes. Subsequently, a comprehensive survey was administered utilizing a dual-mode (online and offline) distribution strategy to the target population of vocational college teachers in Hunan Province. Spanning a four-month data collection period, questionnaires were distributed to 830 vocational college teachers. During the data auditing phase, 817 valid responses were ultimately retained, yielding a high valid response rate of 98%, which exceeds the standard acceptability threshold for social science surveys. And validation factor analysis was used. Validation factor analysis, as an important statistical analysis method, is widely used in social science, psychology and education research. Its main role is to test whether the relationship between potential and observed variables in a theoretical model is consistent with the actual data. Validated factor analysis allows one to verify whether the hypothesized factor structure accurately reflects the intrinsic structure of the data and to assess the fit of the model. The questionnaire was subjected to validation factor analysis using SPSS 26.0. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, the questionnaire topics were three levels of technology application, teaching mode, and self-improvement, with a sample size of 817 for the validation factor analysis.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eValidating factor\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFactor\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTechnology coupling\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eInstructional coupling\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTalent\u003c/p\u003e\u003cp\u003ecoupling\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAggregation\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eAnalyzing sample size\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAmount\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e817\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Experimental Variables\u003c/h2\u003e\u003cp\u003eAccording to the analysis of the data in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, the average common factor variance extraction (AVE) and the combined reliability (CR) were evaluated for the model, and the three levels of the average variance extraction AVE value was greater than 0.5, and the combined reliability CR value was greater than 0.7, and the results were shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, which indicated that the extraction of the measurements within the factors of the model was excellent.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eValidation factor evaluation\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFactor\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTechnological\u003c/p\u003e\u003cp\u003ecoupling\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eEducation\u003c/p\u003e\u003cp\u003ecoupling\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTalent\u003c/p\u003e\u003cp\u003ecoupling\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAVE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.566\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.510\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.510\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.959\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.940\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.943\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, the fit indicators for the validation factors are demonstrated. The chi-square degrees of freedom ratio X\u0026sup2;/df\u0026thinsp;=\u0026thinsp;1.06\u0026thinsp;\u0026lt;\u0026thinsp;3 and the p-value P\u0026thinsp;=\u0026thinsp;0.074\u0026thinsp;\u0026gt;\u0026thinsp;0.05 indicate that the model has no statistically significant bias and meets the fitting requirements. gfi\u0026thinsp;=\u0026thinsp;0.958\u0026thinsp;\u0026gt;\u0026thinsp;0.9 indicates that the model is a better fit to the sample data. rmsea\u0026thinsp;=\u0026thinsp;0.009\u0026thinsp;\u0026lt;\u0026thinsp;0.10 indicates that the fitting error is very small, and the fit is satisfactory. rmr\u0026thinsp;=\u0026thinsp;0.016\u0026thinsp;\u0026lt;\u0026thinsp;0.05, further verifying that the difference between the model's predicted values and the actual observed values is very small.CFI\u0026thinsp;=\u0026thinsp;0.998\u0026thinsp;\u0026gt;\u0026thinsp;0.9, showing that the model has a very good fit compared to the independent model.NFI\u0026thinsp;=\u0026thinsp;0.958 and NNFI\u0026thinsp;=\u0026thinsp;0.997, which are both greater than 0.9, further proving the superiority of the model's fit, with the questionnaire content being highly correlated between each level.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eValidation factor fit values\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNorm\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eX\u0026sup2;/df\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eP\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eGFI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eRMSEA\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eRMR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eCFI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eNFI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eNNFI\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNumerical\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.060\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.074\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.958\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.009\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.016\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.998\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.958\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.997\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe evaluation of the validated factor model and the analysis of the values of the validated factor fit proved that there was a high degree of correspondence, strong correlation, and strong intrinsic connection between the questions in the questionnaire and the three levels of technology application, teaching mode, and self-improvement. The results of the validated factor analysis show that the questions at each level can effectively reflect the characteristics of the latent variables, and the model is well fitted and can be analyzed for sexual coupling coordination.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Reliability Testing\u003c/h2\u003e\u003cp\u003eReliability refers to the consistency, stability and reliability of the results of a measurement instrument over multiple measurements. It measures the degree of repeatability of results obtained when the same measurement tool is used by the same subject at different times or in different situations. The higher the reliability, the better the stability and consistency of the results, and the more accurately the instrument reflects the true characteristics of the subject. The core of reliability is to ensure that the measurement tool yields consistent results in different situations and to reduce the impact of random errors on the measurement results. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, according to the results of the folded half reliability analysis, the Cronbach's α coefficients of the first half and the second half of the questionnaire are 0.964 and 0.963, respectively, which show a very high level of internal consistency, indicating that the front and back parts of the questionnaire have a very good reliability. The total Cronbach's α coefficient was 0.968, which proved the high reliability of the overall scale. In addition, the correlation coefficient between the front and back parts was 0.984, indicating a high degree of consistency between the front and back halves of the questionnaire.The Spearman-Brown coefficient and the GuttmanSplit-Half coefficient, both of which were 0.984, also proved that the questionnaire's reliability was very high for the different splits, which indicated that the questionnaire had a very good level of reliability.\u003c/p\u003e\u003cp\u003e\u003cimg 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\" width=\"685\" height=\"321\"\u003e\u003c/p\u003e"},{"header":"4. Experimental Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e4.1 Criteria for the indicator system\u003c/h2\u003e\u003cp\u003eIn order to quantify the coupling strength of AI-enabled vocational education for higher vocational teachers, firstly, a weighted summation model is used to model the technology application subsystem (T), the teaching mode subsystem (E), and the teacher self-improvement (H) to match the corresponding indexes. The composite index of each subsystem is obtained by weighted summation of the normalized values of each index, and the weights are assigned based on the importance of the indexes in the overall system, and the sum of the weights is 1. Secondly, in order to eliminate the difference in the magnitude of the different indexes, the study adopts the Min-Max normalization analysis method to convert the data of each index into the values within the range of [0,1], which makes all the indexes under the under the same quantitative outline, avoiding the bias caused by different quantitative outlines. On this basis, the coupling strength analysis quantifies the interrelationship and the degree of coordinated development among multiple subsystems, calculates the coupling degree between systems, and evaluates the synergistic effect between different subsystems, and the higher the coupling degree, the stronger the coordination between systems. Finally, the coupling coordination degree analysis further examines the degree of synergistic evolution between subsystems, and calculates the average value of the subsystem comprehensive index to derive the overall education development level, and combines with the coupling degree index to assess the coordination between subsystems, and the closer the coupling degree of coordination is to 1, it means that the better the mutual promotion and coordination between subsystems.\u003c/p\u003e\u003cp\u003eDesign the sub-system index system. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, through brainstorming, literature research and other methods to explore the influence factors of AI on the higher vocational teachers' career after informal research\u0026thinsp;+\u0026thinsp;questionnaire research, excluding invalid data, the final design of technology application subsystems, teaching mode subsystems and teachers' self-improvement of the three categories, which consists of 6 observation indicators of technology application, 5 observation indicators of the teaching mode subsystems and 4 observation indicators of the teacher's self-improvement, a total of 15 observation indicators. The weights of the model were assigned. The weights of the technology penetration subsystem are more decentralized, focusing on the frequency, coverage, and auxiliary role of technology application in teaching. The teaching mode subsystem pays more attention to the coverage rate of personalized learning resources and student satisfaction, as they directly affect the teaching effect and learning experience. The weight of teacher self-improvement is more centralized, mainly focusing on teacher AI skill mastery score, AI-supported vocational skill improvement rate and teacher AI-related industry technology coverage, as it directly reflects the fit between teachers and AI.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSystematic indicator system\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSystems\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCoding\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eObservation indicators\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eWeights\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e\u003cp\u003eTechnology\u003c/p\u003e\u003cp\u003einfiltration\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eT1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eUtilization rate of AI teaching tools\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.12\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eT2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCoverage rate of AI teaching platforms\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.22\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eT3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eParticipation rate of AI training for teachers\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.18\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eT4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFrequency of use of AI smart classrooms\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.17\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eT5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePercentage of AI-assisted correction of assignments\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.16\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eT6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePenetration rate of AI hardware devices in schools\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.15\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003eTeaching\u003c/p\u003e\u003cp\u003emodel\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eE1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCoverage of AI personalized learning resources\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.23\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eE2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFrequency of AI participation in classroom interactions\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.18\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eE3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePercentage of courses using intelligent recommendation systems\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.17\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eE4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eStudent satisfaction rating of AI teaching\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.22\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eE5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNumber of AI-supported teaching reform projects\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.20\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eSelf\u003c/p\u003e\u003cp\u003eelevate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eH1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePercentage of AI-assisted teacher research surveys\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.21\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eH2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTeacher AI skill mastery scores\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eH3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAI-supported occupational skill enhancement\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.26\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eH4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTeacher AI industry skill coverage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.28\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e4.2 Results and analysis\u003c/h2\u003e\u003cp\u003eReferring to the existing coupling coordination degree classification standard [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], the coupling coordination degree is classified into ten levels using a uniform distribution.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCoupling harmonization results\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSystems\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEncodings\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCoupling C-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHarmonization index T-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eHarmonization coupling\u003c/p\u003e\u003cp\u003eD-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eLevel\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eCoordination coupling Degree\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e\u003cp\u003eTechnology\u003c/p\u003e\u003cp\u003einfiltration\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eT1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.602\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.703\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.651\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003ePrimary\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eT2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.889\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.748\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.816\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eGood\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eT3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.891\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.801\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.845\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eGood\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eT4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.942\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.742\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.836\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eGood\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eT5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.832\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.703\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.765\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eIntermediate\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eT6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.887\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.774\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.829\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eGood\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003eTeaching\u003c/p\u003e\u003cp\u003emodel\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eE1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.949\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.781\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.861\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eGood\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eE2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.832\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.735\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.782\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eIntermediate\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eE3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.888\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.755\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.819\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eGood\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eE4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.940\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.755\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.842\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eGood\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eE5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.866\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.676\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.765\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eIntermediate\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eSelf\u003c/p\u003e\u003cp\u003eelevate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eH1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.895\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.742\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.815\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eGood\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eH2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.416\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.213\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.297\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eIntermediate\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eH3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.897\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.768\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.830\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eGood\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eH4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.345\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.219\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.275\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eIntermediate\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eBased on the calculation results of the coupling coordination degree analysis, the coupling strength values of the subsystems and their corresponding coordination degree assessments are displayed. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, most of the subsystems T (T1 to T6), which are related to the use of AI teaching tools, platform coverage, teacher training, etc., have high coupling strengths, and the degree of coordination is in the range of \u0026ldquo;good coordination\u0026rdquo; or \u0026ldquo;medium-strong coordination\u0026rdquo;, indicating that There is a strong synergy between these subsystems. In particular, T4 has a coupling strength of 0.942 and a coordination degree of 8, indicating its close connection in the system. Subsystem E (E1 to E5), which is mainly involved in personalized learning resources, classroom interaction frequency, and intelligent recommendation system, shows high coupling strength and coordination, and E1 has a coupling strength of 0.949 and a coordination degree of 9, indicating its important role in the overall system. Subsystem H (H1 to H4), which involves the proportion of AI-assisted teacher research surveys, teacher AI skill mastery scores, etc., has a coupling strength of 0.895 and a coordination degree of 9. However, the coupling strength of H4 is lower, only 0.345, and a coordination degree of 3, showing a lower degree of coordination.\u003c/p\u003e\u003cp\u003eThrough the analysis of the coupling coordination degree, the results show that there is a high coupling strength and coordination between technology penetration, teaching mode and self-improvement, especially the subsystems of E1, E3 and T4, which show strong synergistic effect, indicating that the application of AI technology and the teaching mode are more tightly coupled in the education system, which can effectively promote the synergistic development of each link. However, the coupling strength of the H4 subsystem is low and the degree of coordination is poor, indicating that this subsystem has certain deficiencies in the matching of education and industry needs, and needs to be further optimized. Overall, although the coupling and coordination between most of the subsystems is relatively satisfactory, the coordination of a few subsystems still needs to be focused on and improved, especially in the connection of teachers' AI skill mastery.\u003c/p\u003e\u003c/div\u003e"},{"header":"5. Discussion","content":"\u003cp\u003eBased on the above research structure using Structural Equation Modeling (SEM), SEM is a multivariate statistical analysis method used to study complex relationships between variables. Combining the characteristics of causal analysis and regression analysis, it can deal with multiple dependencies at the same time and consider the relationship between latent variables (i.e., variables that are not directly measurable) and observed variables (variables that can be directly measured). According to the coupling mechanism to study the impact of AI technology penetration, classroom teaching mode, and teacher self-improvement on teachers' professional development, and to explore the relationship between the weight and positive influence of teacher self-improvement on professional development. Teacher self-improvement is analyzed horizontally and vertically, and the degree of influence and path weights of teacher self-improvement horizontally and vertically on career development in the case of coupling of the three dimensions are sorted out from the influence relationship between technology integration and technology coverage.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eAccording to the structural equation modeling shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, the CMIN/DF of the model can be obtained as 3.901, which is greater than 3 and less than 5, and is within a reasonable range, GFI\u0026thinsp;=\u0026thinsp;0.918 and AGFI\u0026thinsp;=\u0026thinsp;0.898, which are all greater than 0.8, TLI\u0026thinsp;=\u0026thinsp;0.962, IFI\u0026thinsp;=\u0026thinsp;0.967, and CFI\u0026thinsp;=\u0026thinsp;0.967, which are all greater than 0.9, and AIC\u0026thinsp;=\u0026thinsp;973.971, BIC\u0026thinsp;=\u0026thinsp;1228.076, which are relatively small indicators, and ECVI\u0026thinsp;=\u0026thinsp;0.294. RMSEA\u0026thinsp;=\u0026thinsp;0.060, which is less than 0.08 but still shows a small fitting error of the model. Most of the indicators meet the research criteria, therefore, it can be concluded that the model maintains a good balance between goodness of fit and complexity, the fit is ideal and suitable for data analysis, and the overall performance of the model shows a good degree of fit. The standardized coefficients of the above factors are all greater than 0 and P\u0026thinsp;\u0026lt;\u0026thinsp;0.05, which proves that all of these factors have a significant positive correlation effect on career development, and proves that AI positively affects teachers' career development. Among them, the standardized coefficient of technology integration is 1.82, which is in the first place.\u003c/p\u003e\u003cp\u003eIf the degree of AI-enabled teacher career development is approximated to the AI-enabled teacher career development validity, it can be seen in the modeling results of teacher career development factors that technology penetration and teaching mode act simultaneously on self-improvement, and the values of the impact of both technology integration and technology coverage on teacher career development are positive, which indicates that all these factors have a positive predictive effect. From this result, it can be seen that the most influential factor on teachers' career development is technology integration, while technology coverage is also highly influential, so it should be emphasized in the process of teachers' talent development.\u003c/p\u003e"},{"header":"6. Conclusion","content":"\u003cp\u003eEmpirical analysis using coupling coordination degree and Structural Equation Modeling reveals that the coupling between talent cultivation and teacher professional development is suboptimal. This is primarily manifested in the low scores for teachers' mastery of AI skills and the inadequate coverage of AI-related industry technologies among teachers (H2, H4). Furthermore, the SEM results indicate that talent cultivation exerts the most substantial influence on teacher professional development among the factors examined. Consequently, the following conclusions can be drawn regarding teachers' mastery of AI skills and the coverage of AI-related industry technologies:\u003c/p\u003e\u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eThe government plays a crucial role in promoting AI education in higher vocational colleges and universities, especially in addressing the insufficient mastery of teachers' AI skills. The government can encourage schools to integrate AI technology in teaching by formulating policies and increasing funding. These funds can be used for the construction of AI teaching facilities, curriculum content development, and professional training for teachers to ensure that teachers have sufficient resources and support to master AI technology. In addition, the government should also promote cooperation between higher vocational institutions and AI technology enterprises. Enterprises can provide schools with resources such as technical support, financial assistance and internship opportunities to help teachers and students better understand and apply AI technology. Through the guiding role of the government, teachers can receive more comprehensive training in AI applications and improve their technical skills in education, thus solving the problem of insufficient mastery of teachers' AI skills and effectively increasing the coverage of teachers' AI-related industry skills.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003ePilot AI teaching application education for teachers in schools. Pilot AI teaching application education for teachers in local schools is an important step in addressing the lack of mastery of teachers' AI skills. The government should provide financial support to help schools build relevant hardware facilities and teaching platforms so that teachers can access and learn the latest AI technologies. Through these funds, schools should strengthen teachers' practical teaching ability and purchase AI-related software tools and experimental equipment to ensure that teachers are able to apply AI technology in actual teaching and improve their technical level and classroom teaching quality. At the same time, schools should regularly organize AI-related teaching seminars and exchanges to help teachers understand and master the application of AI technology in different disciplines, and inspire them to continuously explore innovative teaching methods. Through this kind of pilot education, teachers can not only enhance their ability to apply AI technology, but also optimize their teaching methods through actual teaching practice, so as to gradually improve the overall technical level of teachers and solve the problem of insufficient coverage of teachers' AI-related industry technology.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eTeachers' personal attention to AI education directly affects their mastery and application of AI skills. Teachers should actively improve their knowledge and understanding of AI technology, and enhance their learning and mastery of AI technology by participating in online courses, training courses and industry seminars. This process of self-improvement helps to make up for the deficiencies in teachers' AI skills and effectively improve their application ability. Higher vocational teachers, in particular, should integrate AI technology into their daily teaching, not only limiting themselves to theoretical explanations, but also allowing students to experience the application of AI technology in real-life scenarios by means of practical sessions and intelligent assessments, so as to enhance their practical ability and innovative thinking. Teachers should also actively participate in professional development communities, communicate with peers and industry experts, and continuously improve their teaching and technical skills. Through personal attention to AI education, teachers are able to gradually solve the problem of insufficient mastery of AI skills in practice, thus promoting high-quality progress in their professional development.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to express our gratitude to all the esteemed experts and teachers who participated in interviews, completed the questionnaire, and assisted us in conducting this research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJia Li participated in research design and conceptualization, provided preliminary ideas for the core ideas of the research; Yongchao He led the overall research design and technical routes of the research; Yu Xie assisted in chart drawing and data visualization; Yanting Chu assisted in proofreading the details of the paper, checked the consistency between data tables and text descriptions to ensure the rigor of the research; Nanyu Jiang and Zhuoxiang Lai provided an international perspective for improving teachers\u0026apos; AI skill training. All authors have read and approved the final version of the manuscript. All authors have read and approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe work is supported by the Research on Innovation of Craftsmanship Culture Education\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eModel in Higher Vocational Colleges under the Background of \u0026quot;Three Highs and Four News\u0026quot; Strategy (No XJK22CZY032) and the 2022 Hunan Provincial Education Science Research Project of the 14th Five-Year Plan.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and/or analyzed within this study are not publicly available due to the inclusion of personal privacy information of study participants, but can be obtained from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe researchers explained the research objectives to the participants in the questionnaire\u0026rsquo;s introduction. All the participants answered the questions with their consent and completed the informed consent form. The data was distributed anonymously, compiled, and analyzed. Relevant guidelines and regulations carried out in all experiments and methods. We confirm that all experimental protocols have been approved by the Academic Committee of Hunan Railway Vocational and Technical College.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eChen, L. T. \u0026amp; Huang, L. Investigation on the Current Situation and Improvement Strategies of Vocational College Teachers' AI Education Literacy Based on the AI-TPACK Model[J]. \u003cem\u003eVocat. Tech. Educ.\u003c/em\u003e \u003cb\u003e46\u003c/b\u003e (08), 69\u0026ndash;75 (2025).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eThomas, S. \u003cem\u003eKuhn. 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Educational Research. 41(02):17\u0026ndash;32. (2020).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTobin, K., Ritchie, S. M. \u0026amp; Multi-method Multi-theoretical, Multi-level Research in the Learning Sciences[J]. Asia-Pacific Education Researcher, (1). (2012).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhu, Z. T., Dai, L., Zhao, X. W. \u0026amp; Shen, S. S. \u003cem\u003eCultivation of New-Quality Talents: New Mission of Education in the Digital and Intelligent Era[J]\u003c/em\u003e4552\u0026ndash;60 (E-education Research, 2024). 01.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Vocational college teachers' profession, Artificial intelligence, Coupling mechanism, Structural equation model","lastPublishedDoi":"10.21203/rs.3.rs-7297038/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7297038/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe coupling mechanism and influencing factors of artificial intelligence (AI) in empowering vocational college teachers' professional development. It analyzes the synergistic effects of three subsystems: technological penetration, teaching modes, and talent cultivation. The study highlights that AI technology promotes the transformation of teachers' knowledge system, capability system, and roles through technology embedding, data-driven approaches, and educational scenario reconstruction. Furthermore, through coupling coordination degree analysis, it is found that there is a strong synergy among the subsystems, especially in terms of personalized learning resources, classroom interaction, and the frequency of AI-driven smart classrooms. However, there is a mismatch between the talent cultivation subsystem and industry demand. Moreover, structural equation modeling (SEM) analysis further validates the positive influence of technological penetration, educational models, and talent cultivation on teachers' professional development, with a particular emphasis on the key role of talent cultivation. Finally, to address the deficiency in talent cultivation, the paper proposes countermeasures such as enhancing AI skill training for teachers, government guidance, and self-improvement among educators, aiming to support the high-quality advancement of teachers' professional development.\u003c/p\u003e","manuscriptTitle":"Artificial Intelligence Integration on Vocational College Teachers' Professional Development: An Empirical Analysis Using Structural Equation Modeling","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-01 15:45:22","doi":"10.21203/rs.3.rs-7297038/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b99763c4-03ca-488d-90ae-b8857a9bb1fa","owner":[],"postedDate":"October 1st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":55179037,"name":"Social science/Education"},{"id":55179038,"name":"Business and commerce/Information systems and information technology"},{"id":55179039,"name":"Physical sciences/Mathematics and computing"},{"id":55179040,"name":"Social science/Science technology and society"}],"tags":[],"updatedAt":"2025-10-01T15:45:25+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-01 15:45:22","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7297038","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7297038","identity":"rs-7297038","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}