产教融合背景下职业学生智能素养培育与就业服务创新研究

Background: The pressures of Industry 4.0 have driven the incorporation of artificial intelligence (AI) in Technical and Vocational Education and Training (TVET) to improve the development of practical skills. Nonetheless, there is still a lack of empirical agreement regarding the effects and implementation of AI. Methods: We conducted a literature review using databases like IEEE Xplore, Scopus, ERIC, and Google Scholar, as well as grey literature from conference proceedings and UNESCO-UNEVOC reports, to find empirical studies on AI in Technical and Vocational Education and Training (TVET). Our search included keywords such as "artificial intelligence," "machine learning," and "vocational training." After screening titles/abstracts and full texts against our inclusion criteria (focused on TVET settings with measurable outcomes), we identified 11 studies published between 2021 and 2025. Each study was coded by methodology, AI technology type, vocational domain, country, and reported outcomes. Results: Evaluations in vocational trades show AI-driven simulators enhance hands-on skills. Lee et al. found that an AI-guided XR welding trainer improved welding accuracy and learning rate over traditional VR instruction. An Indonesian "AI teaching factory" boosted students' technical proficiency, efficiency, and industry readiness. Surveys indicate high student satisfaction: Malaysian polytechnic students using an AI-powered robotics trainer saw increased understanding and confidence, while TVET students with ChatGPT reported improved comprehension and engagement. Analytical studies highlight curriculum alignment: a decision analysis in the End-of-Life Vehicle sector identified AI integration, tool training, and industry partnerships as priorities for employability. Discussion: Overall, AI applications promise to enhance vocational skill acquisition and engagement. However, much of the research focuses on short-term pilots or perceptions rather than long-term outcomes. Ongoing challenges include limited infrastructure and inadequate teacher preparedness. Future efforts should prioritize rigorous, longitudinal evaluations of AI-enabled TVET interventions using standardized skill and employment outcomes metrics.

With the rapid development of artificial intelligence technologies and the ongoing transformation of the intelligent manufacturing sector, higher vocational education is encountering unprecedented opportunities and challenges. This study, grounded in the framework of industry-education integration, systematically explores the development pathways and practical models for integrating AI technologies into the intelligent manufacturing curriculum. Through a combination of theoretical analysis and empirical investigation, it reviews the theoretical foundations and current state of industry-education collaboration in intelligent manufacturing, identifying the key misalignment between existing talent development practices and industrial demands. Drawing on a case study of the Intelligent Manufacturing Industry College at Guangzhou Vocational College of Technology &Business, the research constructs an AI-enhanced curriculum system for industry-education integration, addressing critical dimensions including curriculum objective formulation, content restructuring, pedagogical innovation, and digital resource platform development. Furthermore, targeted strategies are proposed to address implementation challenges related to technological integration, faculty capacity building, and ethical and safety considerations. This study not only contributes theoretical insights to curriculum reform in intelligent manufacturing-related programs within higher vocational institutions but also provides actionable guidance for strengthening talent supply in support of regional industrial upgrading.

… of embedding AI literacy and application-oriented learning into vocational … of artificial intelligence (AI) on vocational education and its potential to facilitate industry-education integration. …

This study addresses persistent challenges in traditional talent cultivation models, including misalignment with industry demands, outdated instructional content, and limited depth in school-enterprise collaboration. To overcome these issues, the study proposes an innovative approach by integrating Back Propagation Neural Network (BPNN) technology from the field of artificial intelligence to construct a multimodal, integrated teaching quality evaluation system. By aggregating diverse data sources—including classroom performance, online learning behaviors, and hands-on practice videos—the study builds a comprehensive digital representation of the teaching process. An enhanced BPNN model is introduced, optimized by determining the ideal number of hidden nodes to improve evaluation performance. The method employs a hybrid fusion strategy, in which features from different data modalities are extracted independently, processed through dedicated neural networks, and fused at the decision level. This design enhances the model’s adaptability to complex instructional environments and improves predictive accuracy. Empirical validation uses 1,500 samples collected from 18 public courses offered by Nanjing Institute of Industry Technology. The experimental results show that the improved BPNN model significantly outperforms baseline models in both prediction accuracy and convergence speed. Specifically, it achieves a prediction accuracy of 92% for teaching quality rated as “excellent” and 90% for those rated as “very good.” This study provides a novel and effective solution for evaluating teaching quality in vocational education, fostering deeper integration between industry and education, advancing talent development, and supporting broader goals of industrial transformation and upgrading.

This study addresses the issues of outdated teaching content and monotonous teaching methods in higher vocational education by constructing a dynamic teaching system based on the Scrum agile framework, innovatively integrating artificial intelligence (AI) technologies to achieve intelligent teaching upgrades. Using a quasi-experimental design, the research compares the teaching effectiveness among a traditional teaching group, a traditional agile group, and an AI-enhanced group with 360 students from a vocational college in Jiangsu Province. The findings reveal that: (1) The agile teaching system, through modular courses (20% theory, 30% case studies, 40% practical projects, and 10% evaluation), short-cycle iterations (2-week Sprints), and industry-school collaboration mechanisms, significantly improves students' comprehensive abilities (experimental group: 84.6 vs. control group: 78.4, p < 0.001). (2) AI empowerment yields differentiated effects: task allocation algorithms increase team collaboration efficiency by 25%, while sentiment analysis bots reduce anxiety levels among low-performing students by 23%. However, the dimension of innovative thinking requires a balance between technological optimization and challenge-based design. (3) A three-dimensional dynamic evaluation model (30% knowledge, 40% skills, and 30% competencies) effectively aligns with vocational competency standards. This research provides a theoretical paradigm and practical pathway for the digital transformation of vocational education through the "Agile + AI" approach.

The rapid development of artificial intelligence technology demands higher requirements for employment and talent training. The integration of industry and education is an important way to solve the mismatch between industrial demand and talent supply. Therefore, this study starts from the perspective of the integration of industry and education. We collect recruitment texts from the perspective of “industry” and mine the specific requirements of the artificial intelligence post system through the LDA topic model and the combination of Word2Vec and K-means. We then conduct expert consultations and adjust the selected indicators from the perspective of “education.” Finally, we construct a four-dimensional vocational ability grade evaluation index system, including basic vocational skills of artificial intelligence, database, network skills, algorithm and design skills, and research and practice skills. The intuitionistic fuzzy analytic hierarchy process, which can eliminate the subjective uncertainty of experts in the scoring process, is applied to calculate the index weights. We find that the weight of algorithm and design skill is the highest, which is an important criterion for artificial intelligence professional ability evaluation. Among the second-level indicators, practical indicators such as team spirit, innovation ability, and communication ability are the focus of investigation from the perspective of industry, while in education, the cultivation of knowledge and skills such as programming ability, applied mathematics ability, data structures, and algorithms are more important.

Artificial intelligence is reshaping production systems, organizational routines, and occupational skill profiles. Under these conditions, industry--education integration can no longer be understood as a loose arrangement of internships, guest lectures, or episodic school--enterprise cooperation. It must evolve into a dynamic, data-informed, and ethically governed ecosystem in which educational institutions and industrial actors co-design talent standards, co-produce learning resources, and co-evaluate outcomes. This article develops a conceptual framework for AI-driven industry--education integration from the perspectives of capability formation, knowledge co-creation, platform coordination, and governance. Drawing on theories of constructive alignment, experiential learning, organizational knowledge creation, activity theory, and triple-helix collaboration, the framework positions AI not as a substitute for teachers or workplace mentors, but as a mediating infrastructure that connects labour-market intelligence, curriculum reform, authentic practice, assessment, and continuous improvement. The article proposes five interdependent dimensions: strategic alignment, data and intelligence infrastructure, curriculum and pedagogy, practice and innovation ecology, and governance and ethics. It then explains four operating mechanisms through which the framework functions: dynamic skills sensing, personalized capability development, project-based knowledge circulation, and feedback-enabled institutional adaptation. On this basis, the article outlines a staged implementation path for universities and vocational institutions and develops an evaluation matrix that links learner growth, enterprise value, institutional transformation, and social inclusion. The study argues that AI-driven integration is most effective when it remains human-centered, problem-oriented, and publicly accountable. Its long-term value lies in building adaptive institutions that can respond to technological change while preserving educational depth, equity, and professional judgment.

… Digital literacy not only encompasses the ability to retrieve and … a deep integration with the standards of vocational education … The development of artificial intelligence technology has …

… studies connect the TPACK framework with this integration, adopt both grounded theory and … assessing vocational teachers’ digital literacy within industry–education integration contexts…

The digital transformation of vocational education has moved beyond the question of whether teachers should use digital tools. In regions where industrial renewal and vocational reform overlap, the more difficult question is how teachers' professional capacity should be rebuilt when artificial intelligence, data-based training resources and school-enterprise co-development become part of everyday institutional work. Taking Liaoning Province as the analytical setting, this paper examines how vocational teachers' roles are being reorganised under three connected pressures: the spread of AI-enabled pedagogy, the demand for deeper industry-education integration and the regional need to support industrial upgrading. The study adopts qualitative document analysis of international frameworks, Chinese national policy documents, Liaoning provincial education documents and recent empirical studies on TVET teacher digital competence. The findings suggest that teacher role change is better understood as capacity reconstruction than as simple role replacement. Four dimensions are identified: curriculum translation between occupational tasks and learning outcomes, AI-assisted instructional design and verification, boundary work in school-enterprise cooperation, and ethical stewardship of digital assessment and learner data. The paper argues that AI application strengthens vocational education only when it is embedded in industry-linked curriculum renewal and supported by organisational arrangements for teacher development. For vocational colleges in Liaoning, the practical priority is not to train teachers to operate isolated tools, but to build collaborative mechanisms through which teachers, enterprises and digital platforms jointly update curriculum, assessment and workplace learning. The paper offers a regional policy-informed framework for managing vocational teacher development in AI-enabled vocational education.

Under the tide of global digitalization, vocational education is undergoing profound changes. This paper studies the practical education and innovative application of artificial intelligence large model in higher vocational education, and analyzes the development status, achievements and existing problems in this field at home and abroad. With its data processing and analysis capabilities, the large model is reshaping the ecology of vocational education, but the technology landing is facing challenges such as adaptation gap, inadequate capacity, ethical norms . These challenges can be solved and the sustainable development paradigm of the deep integration of artificial intelligence and vocational education can be established through the paths to industry-education collaboration, teacher literacy reconstruction and teaching scene integration. This paper puts forward some strategies, such as the reconstruction of demand-oriented teaching mode and the innovation of teaching technology with the integration of virtual and real, in order to improve the quality of teaching and cultivate high-quality skilled personnel to adapt to the era of intellectualization.

Abstract:In the context of rapid advancements in artificial intelligence (AI) technology, vocational education, as a crucial pathway for cultivating applied skill talent, is undergoing profound systemic transformation. Educational ecology theory, emphasizing holism, dynamism, diversity, and sustainable development, provides theoretical support for the optimization and innovation of vocational education. Research indicates that AI technology is reshaping the vocational education ecosystem, leading to significant changes in key aspects such as stakeholder roles, resource allocation, teaching models, and external environments. Meanwhile, in the process of dynamic equilibrium and co-evolution, the vocational education ecosystem faces multiple challenges, including inadequate technological adaptability, uneven resource distribution, and lagging policy support. However, it also presents opportunities for curriculum optimization, deeper industry integration, and personalized training. This study systematically analyzes vocational education in the AI era from the perspective of the educational ecosystem. Based on educational ecology theory, it proposes strategies for optimizing the vocational education ecosystem. These strategies aim to enhance the coordination and sustainability of the vocational education ecosystem, providing both theoretical support and practical pathways for its development in the AI era. Through this research, the study further enriches the practical application of educational ecology theory and offers scientific guidance and reference value for vocational education in adapting to technological transformations.

The application of artificial intelligence in education prompts an evolution in the professional competencies required of teachers. Current discussions on teacher AI literacy are predominantly situated within the context of general education, failing to capture the unique characteristics of vocational education, such as industry-education integration and school-enterprise collaboration. Consequently, a specific framework for vocational college teachers is absent, and existing research has not addressed this need. This study, grounded in empowerment theory, constructs an AI literacy framework for vocational college teachers. It elaborates on the competency dimensions related to human-computer collaboration, including the use of AI to understand industry demands, design instructional scenarios, and align curriculum with workplace requirements. The research further analyzes the practical constraints on literacy enhancement from the perspectives of policy environments, institutional support mechanisms, and teacher cognition, proposing corresponding developmental pathways. This study aims to provide a theoretical reference and practical guidance for the professional development of educators in the vocational sector.

Amid accelerating industrial transformation driven by artificial intelligence and digital manufacturing, vocational education faces an acute shortage of du-al-qualified teachers who can bridge academic instruction and enterprise prac-tice. This study investigates the construction and optimisation of faculty teams in Shandong’s vocational colleges within the paradigm of industry–education integration. Drawing upon Human Capital Theory, the TVET-oriented TPACK framework, and the AI-enabled professional learning model, a mixed-methods design combined questionnaire data (132 valid responses) with semi-structured interviews and a case study at Shandong X College. Findings reveal structural and competence imbalances: only 49.4 per cent of teachers hold dual qualifica-tions, fewer than 30 per cent engage in enterprise practice exceeding three months annually, and merely 22.7 per cent employ AI-assisted or digital-twin pedagogy. Three interlocking barriers were identified—rigid institutional mechanisms, resource scarcity, and cognitive constraints regarding digital and industrial teaching. To address these challenges, the study proposes a Tri-ple-Entity Collaboration and Dual-Drive framework linking full-time teachers, enterprise engineers, and part-time practitioners, supported by policy incen-tives and AI-based capability development. Implementation at Shandong X Col-lege increased dual-qualified faculty to 58.2 per cent, raised digital-teaching coverage to 65.3 per cent, and improved enterprise participation by 40 per cent. Theoretically, this research extends Human Capital Theory by integrating AI-mediated capacity growth and enriches the TVET-TPACK model through quantitative validation. Practically, it offers a replicable approach for aligning vocational faculty development with industrial upgrading in China’s digital economy.

Against the backdrop of deep integration between educational digital transformation and the “AI+” strategy, enhancing teachers' digital literacy has become pivotal to elevating the quality of industry-education integration in vocational education. As a core technology of artificial intelligence, machine learning offers new pathways to overcome digital barriers in industry-education integration. However, existing research has yet to establish an integrated analytical framework linking “industry-education integration – machine learning empowerment – teacher digital literacy.” Centered on TOE theory, this study employs literature review and multi-case in-depth analysis to systematically explore the development patterns of vocational education teachers' digital literacy in dual scenarios. It identifies the core dimensions as industry adaptability, tool application, and data assistance, with core competencies focused on tool operation, scenario alignment, and teaching adaptation. It dissects three-tiered challenges—technological tool inadequacy, organizational training imbalance, and environmental collaboration gaps—along with their underlying causes. A systematic enhancement pathway is proposed: “Technology Empowerment - Organizational Assurance - Environmental Support.” This research provides actionable solutions for enhancing vocational educators' digital literacy while laying theoretical and technical foundations for low-threshold adoption of machine learning in vocational education. It propels the transformation of industry-education integration toward intelligent, deep convergence. Future exploration may focus on lightweight machine learning system architectures and cross-disciplinary teaching case repository development.

Against the backdrop of digital transformation, private vocational undergraduate universities face an urgent need to overcome the limitations of traditional industry-education integration models. This study constructs an integrated theoretical model combining industry-ducation integration with AI-driven approaches, clarifying the synergistic mechanism between the "data-algorithm-platform" technological foundation and the multi-agent network. By analyzing the elemental structure of digital collaborative innovation, AI-enabled dimensions, and dynamic processes, the study proposes a pathway construction scheme based on the principles of systemic coupling, agile iteration, and value co-creation. It designs an implementation plan comprising a three-phase strategy and a resource virtualization aggregation mechanism, while establishing a three-dimensional safeguard system covering technology, organization, and institutions. This forms a self-adaptive and optimizable collaborative ecosystem. The research provides an innovative theoretical framework and practical pathway for private vocational undergraduate universities to achieve deep integration between the educational chain and the industrial chain.

The study investigates the competency assessment outcome of AI-driven training, student engagement, and demographic factors. Previous studies have examined these factors individually, but this research integrates them to assess their combined impact on competency scores. Variables such as competency scores, AI-driven training, student engagement, gender, and vocational training levels were systematically collected following FAIR principles. Python libraries were used for cleaning and preprocessing the dataset; missing values were filled and outliers were handled using the Tukey method. The use of EDA further disclosed strong positive correlations with student engagement and competency scores resulting from AI-driven training. Nonetheless, since it is an observational study, these associations must not be taken to be causal. Inferential statistics - like t-tests and ANOVA - were established by gender and vocational training level. Machine learning algorithms were used to predict competency scores, and Random Forests showed the highest predictive power compared to linear regression (R2 = 0.68 vs. 0.41). This suggests the necessity of modeling non-linear relationships in competency prediction. Inferential statistics (ANOVA, t-tests) revealed gender and vocational training-level effects. Random Forests outperformed linear regression (R2 = 0.68 vs. 0.41), uncovering non-linear relationships. KMeans clustering revealed three student groups necessitating individualized interventions: Cluster 1 (high AI engagement/low competency) requires skill-building support; Cluster 2 (balanced engagement/competency) is served by ongoing adaptive training; and Cluster 3 (low engagement/high competency) requires engagement-fostering strategies. These results highlight the importance of AI-supported training and student interaction to improve competency attainment. These findings have practical implications for vocational education and training institutions by promoting personalized learning approaches that are responsive to the various needs of students. Ethical considerations of AI-based evaluation, including bias and fairness, are worthy of exploration.

The rapid integration of artificial intelligence across industries necessitates systematic AI literacy development in higher vocational education to prepare students for AI-driven professional environments. This study develops and validates a comprehensive competency-based ladder development pathway specifically designed to enhance AI literacy among vocational students. The research employs a mixed-methods approach combining theoretical framework construction, empirical investigation, and practical implementation validation. The three-tier pathway model integrates foundational cognitive, skills application, and comprehensive innovation layers to address diverse learning needs while maintaining progression standards. Through empirical investigation involving 2850 students across 15 institutions, the study identifies distinct learner profiles and competency deficits, informing personalized development strategies. The validation experiment with 420 participants demonstrates significant improvements across all competency dimensions, with overall AI literacy gains of 56.0% and sustained retention rates exceeding 85% at six-month follow-up. The innovative pedagogical approaches incorporate project-driven learning, experiential methodologies, and hybrid delivery models to optimize competency development. The comprehensive evaluation framework provides robust assessment tools that balance formative and summative approaches while maintaining alignment with industry standards. Results indicate that students in the ladder pathway intervention achieved 34.7% higher cognitive assessment scores, 42.3% superior performance on skills application tasks, and 28.9% better innovation competency outcomes compared to traditional instruction. This research contributes to the theoretical understanding of competency-based AI education while providing practical implementation guidance for enhancing workforce readiness in the artificial intelligence era.

The application of artificial intelligence (AI) in vocational education, particularly in Vocational High Schools (SMK), has a significant impact on improving students' employability skills. This study aims to examine the impact of AI integration in vocational education, focusing on the development of technical skills (hard skills) and interpersonal skills (soft skills). Using a Systematic Literature Review (SLR) approach, this article analyzes various studies discussing the implementation of AI in vocational education, as well as the challenges and opportunities that exist. The findings indicate that AI can improve learning effectiveness through personalized materials, the use of industry-based simulations, and the development of communication and collaboration skills among students. However, challenges faced include limited infrastructure, a lack of technical skills among educators, and resistance to change from schools and students. This study also emphasizes the need for educational policies that support technological development, improve educator skills, and strengthen collaboration between education and industry to create curricula relevant to job market needs, so that vocational high school graduates can compete globally in an increasingly digital world.

This study synthesizes empirical evidence on AI-supported skill assessment systems in higher vocational education through a systematic review and meta-analysis. Despite growing interest in generative AI within higher education, empirical research on AI-enabled assessment remains fragmented and methodologically uneven, particularly in vocational contexts. Following PRISMA 2020 guidelines, 27 peer-reviewed empirical studies published between 2010 and 2024 were identified from major international and Chinese databases and included in the analysis. Using a random-effects model, the meta-analysis indicates a moderate positive association between AI-supported assessment systems and skill-related learning outcomes (Hedges’ g = 0.72), alongside substantial heterogeneity across study designs, outcome measures, and implementation contexts. Subgroup analyses suggest variation across regional and institutional settings, which should be interpreted cautiously given small sample sizes and diverse methodological approaches. Based on the synthesized evidence, the study proposes a conceptual AI-supported skill assessment framework that distinguishes empirically grounded components from forward-looking extensions related to generative AI. Rather than offering prescriptive solutions, the framework provides an evidence-informed baseline to support future research, system design, and responsible integration of generative AI in higher education assessment. Overall, the findings highlight both the potential and the current empirical limitations of AI-enabled assessment, underscoring the need for more robust, theory-informed, and transparent studies as generative AI applications continue to evolve.

AI provides TVET students with specialized education through adaptive teaching which integrates automated administrative processes into Technical and Vocational Education and Training (TVET). Student engagement increases and learning memorization improves and workforce readiness improves through AI-powered learning tools that employ intelligent tutoring systems with virtual simulations supported by machine learning algorithms. The implementation of AI in TVET faces several challenges since it affects data privacy through algorithms while needing advanced systems and costs substantial funds and remains inaccessible to people who lack digital capabilities. Using Socio-Technical Systems Theory and Diffusion of Innovations Theory and Skill-Biased Technological Change the study investigates TVET Artificial Intelligence development prospects including its operational challenges and business opportunities. The study underlines two crucial elements: both the development of moral AI frameworks and the establishment of effective teacher education together with digital technology and financial support to create inclusive AI systems. AI holds transformative potential for skill-based education provided organizations achieve ethical standard alignment in their planning activities and guarantee universal access to its advantages. Research efforts should prioritize both long-term projects about AI policy development and the establishment of fair practices and clear system operations with accessible services. KEYWORDS: AI in TVET, Artificial Intelligence in Education, Adaptive Learning, Vocational Training, Ethical AI Frameworks, Digital Divide, Teacher Training, Workforce Readiness, Intelligent Tutoring Systems, Educational Technology Policy

… the digital competencies of IVET students in Greece, focusing on their engagement with AI in … Convenience sampling was employed in collaboration with school administrators, who …

The industrial application of artificial intelligence (AI) technology is profoundly reshaping the global labor market landscape. As the core carrier for cultivating technical and skilled talents, vocational colleges face a dual situation of "opportunity reconstruction" and "risk intensification" in student employment. Based on the latest industry data, policy documents, and institutional practice cases, this paper systematically analyzes the impact mechanism of AI on the employment of vocational college students, exploring research from three dimensions: opportunity expansion, practical challenges, and collaborative response strategies. The results show that AI creates new opportunities for vocational college students by generating technology-applied positions, promoting vocational education reform, and expanding emerging employment channels. However, it also brings challenges such as accelerated job substitution, intensified skill mismatch, and escalated employment competition. The study proposes a four-dimensional collaborative strategy involving "school-enterprise-government-individual", including dynamic curriculum reform, deep integration of industry and education, precise policy support, and personal ability upgrading. The aim is to build a path for vocational college students to enhance their employment resilience in the AI era and provide theoretical reference and practical guidance for the high-quality development of vocational education.

Vocational students often experience career path anxiety due to uncertainty about labor market demands, limited mentoring, and misalignment between curricula and industry needs. In Indonesia, this is amplified by uneven career guidance despite mandates for workforce readiness. Recent advances in artificial intelligence (AI) enable adaptive, data-driven, and psychologically informed support that links students’ skills with real-time labor markets. This study used a design science research approach to build and evaluate an AI-driven career guidance system with three components: (1) a supervised machine learning skills mapping engine, (2) an adaptive mentoring module using an AI chatbot and mentor matching, and (3) a real-time labor market intelligence module using natural language processing to analyze job postings and trends. A mixed-methods evaluation involved 180 vocational students from three schools in South Kalimantan assigned to intervention and control groups. Quantitative data were collected through pre–post career anxiety surveys and system performance metrics, while qualitative data were gathered through interviews and focus group discussions. Analysis included paired-sample t-tests, predictive model evaluation, and thematic analysis. Students using the AI system showed a significant 26.7% reduction in career path anxiety compared with minimal change in the control group (p < 0.001). The skills mapping model achieved 87% accuracy in predicting suitable career pathways with strong precision, recall, and F1-scores. Engagement was high: 65% repeatedly conducted skill-gap analyses, 79% joined adaptive mentoring, and 87% downloaded personalized career roadmaps. Qualitative findings revealed greater confidence, clearer direction, and better alignment between students’ competencies and labor market expectations. The AI-driven career guidance system effectively reduced career anxiety while strengthening readiness through personalized skills mapping, adaptive mentoring, and real-time labor market intelligence. The study shows that human-centered AI can enhance vocational guidance, bridge school–industry gaps, and support more confident, evidence-based career decisions among vocational students in Indonesia nationwide.

Vocational students often experience high career decision anxiety due to uncertainty about labor market demands, limited mentoring, and gaps between school curricula and industry needs. This issue is critical in Indonesia, where vocational education plays a key role in national human capital development. This study used a mixed-methods design involving 320 vocational students and developed an AI-driven career guidance system integrating three modules: a supervised machine learning–based skills mapping engine, an adaptive mentoring module with an AI chatbot and mentor matching, and a labor market intelligence component using natural language processing to analyze job postings. Data were collected through pre- and post-intervention surveys, system analytics, and interviews. Quantitative analysis used descriptive statistics, paired t-tests, and regression modeling, while qualitative data were analyzed thematically. Results showed a 35% reduction in career anxiety after eight weeks of system use. The skills mapping module achieved 87% matching accuracy, the mentoring module supported over 2,400 chatbot interactions, and 78% of students reported increased confidence in career planning. Labor market analytics processed 500 job postings to provide real-time insights. The findings confirm that the AI-driven system effectively reduces career anxiety and improves vocational students’ career readiness.

Vocational education plays a critical role in equipping learners with practical skills aligned with industry demands. However, rapid technological advancements and evolving job requirements necessitate frequent updates to curriculum content. Traditional curriculum design methods often fail to address these dynamic needs, leading to mismatched learning experiences and inadequate skill development. This research focuses on developing an Intelligent Recommendation System (IRS) for vocational education curriculum content, leveraging Artificial Intelligence (AI) technologies to create personalized and industry-relevant learning pathways. The system utilizes a dataset comprising vocational curriculum content records (e.g., course titles, descriptions, skill categories, and industry relevance) and student profiles (e.g., learning preferences, academic performance, and career goals). Interaction logs are also incorporated to track user engagement with recommended courses and materials. The data undergo pre-processing, including tokenization and stop word removal, followed by feature extraction using Word2Vec to capture semantic relationships between terms. The core of the system is a Refined Gorilla Troop Optimized Deep Neural Network (RGTO-DNN) model, designed to enhance recommendation accuracy and efficiency. Experimental results demonstrate the effectiveness of the proposed system in improving recommendation accuracy and efficiency, with performance metrics showing high levels of accuracy (97.2%), precision (94%), recall (95.4%), and F1-score (96.3%). The IRS significantly enhances student engagement, learning outcomes, and curriculum design by providing a data-driven approach to modernizing vocational education. These findings highlight the potential of AI-driven recommendation systems to revolutionize vocational education by delivering a more personalized, efficient, and industry-relevant learning experience.

Abstract It is expected that by utilizing digital technologies, advanced robotics and artificial intelligence, the manufacturing base of Europe will become stronger and allow production re-shoring from other trade areas to take place. The European competitiveness is tied to better competences of the workforce and fast implementation of new technologies. This requires new approaches for formal and non-formal education. For this, we propose a new robotics learning concept and collaboration scheme to support both MSc level education, but also non-formal education with industry. The non-formal education example could be a combination of an education package followed by rapid experimenting with a robot system. In order to facilitate the learning process, we have established the Tampere RoboLab and joint academia-industry education modules for both formal and non-formal education. The Tampere RoboLab operates with similar principles as e.g. Fab Labs (fabrication laboratories), but the focus is on indoor stationary and mobile robotics. Aside from education, the concept allows system interoperability testing and pre-competitive research to be done in the same premises as well as field robotics by providing the state of art localisation and perception sensors, and computation and communication devices. This paper will introduce the concept, used hardware and software configurations, education modules and the forms of industry-academia collaboration.

In response to the critical shortage of high-skilled computer professionals in China's workforce (less than 30%), this study develops a technologically-enhanced “curriculum integration and pathway articulation” model for computer-related vocational education. The model establishes a cohesive technological ecosystem comprising: a Multi-Agent System (MAS) for dynamic competency objective formulation; a knowledge graph-based orchestration engine for curriculum integration; a recommender system employing multi-objective optimization for personalized pathway planning; and a cloud-native “One Repository, Two Systems, N Platforms” implementation framework. Algorithmic innovations include graph traversal for learning sequence optimization and blockchain-based credit transfer mechanisms. Preliminary applications demonstrate significant reductions in course redundancy and improvements in student competency assessments. The research provides a technically-grounded framework for constructing vertically integrated modern vocational education systems aligned with industrial digital transformation needs.

In order to cope with the structural contradiction between the supply and demand of industrial robot technology skills in the field of intelligent manufacturing, and improve the fit between talent training and regional industrial demand, this paper proposes an "order-type" talent training path optimization model relying on regional industrial colleges. The model is based on the theory of school-enterprise collaboration and symbiosis, and constructs a full-process closed-loop system covering "demand coupling, scheme co-system, process co-management, evaluation and sharing". In the path to the implementation of key link, the introduction based on the fuzzy Delphi method and analytic hierarchy process (AHP) of course module, and a method for determining weights and designed a kind of considering enterprise satisfaction and increase students' ability of double objective optimization algorithm, used to dynamically adjust the teaching resources. Practical application shows that the optimized path effectively promotes the precision of the talent training, the students' comprehensive professional ability and the satisfaction of choose and employ persons of enterprises, for the higher vocational education teaching deeply integrated production provides a paradigm of the operation.

This case study evaluates the innovative digital talent cultivation model at Guangdong Polytechnic of Science and Technology (GPST), specifically within the Huawei Kunpeng Digital Industry Academy.The research identifies the academy's strategies to bridge the gap between academic learning and industry needs.Key components of the GPST model include a collaborative curriculum development with Huawei, project-based learning to enhance practical skills, and a faculty team with dual qualifications in academia and industry.The study demonstrates that this approach has led to significant outcomes, such as high employability rates, advanced skill acquisition, and a thriving culture of innovation and entrepreneurship among graduates.The findings emphasize the importance of industry-academia partnerships in shaping educational programs that produce graduates ready for the digital workforce.The paper concludes that GPST's adaptable and industry-aligned model is effective for preparing students for the dynamic demands of the digital economy and suggests that such collaborations are essential for the future of vocational education and talent development.

In the era of Industry 5.0, vocational education in mechanical manufacturing and automation has become crucial. This study examines key factors affecting students' employment prospects in these fields and offers strategies to address challenges. It highlights integrating theoretical and practical knowledge, aligning with industry needs and incorporating advanced technologies like intelligent manufacturing and robotics [1]. To meet evolving job market demands, the study advocates a dual-teacher model with academic and industry experts, emphasising the development of comprehensive skills such as innovation [2], teamwork and project management. Additionally, it stresses the importance of entrepreneurship education and industry training in enhancing employability and adaptability. The findings provide insights for educational institutions and enterprises to improve vocational education quality and meet modern manufacturing needs collaboratively. The research framework is shown in Figure 19.1.

To promote the effective application of industrial robot technology in integrated engineering teaching methods within universities, this study analyzes the background of modern industrial colleges deepening industry-education integration and advancing collaborative education. Using the KUKA industrial robot laboratory as a platform, a practical teaching model of " PDCA cycle with flipped classroom" is proposed. Through innovative approaches such as safety management system optimization and digital twin applications, after four years of teaching practice, the industrial robot laboratory has maintained high-quality experimental and practical training courses even under high operational loads. This management model can serve as an important reference for laboratory management in modern industrial colleges.

Amidst the rapid paradigm shift toward a digital economy, the cross-border e-commerce industry faces an unprecedented demand for high-quality, interdisciplinary technical talents. However, a significant "supply-demand" mismatch persists as traditional talent cultivation models in higher vocational colleges struggle to synchronize with industrial digital transformation. Drawing upon the theory of industry-education integration, this study systematically investigates the new competency requirements—centered on digital literacy, data-driven decision-making, and cross-cultural communication—imposed by the digital era. By dissecting the structural contradictions in current vocational programs, such as outdated curricula and superficial school-enterprise collaboration, this paper constructs a robust "Environment-Mechanism-Path-Goal" four-dimensional linkage framework. We propose five strategic implementation pathways: reconstructing position-course integrated curricula, co-building immersive practical platforms, cultivating "dual-qualified" faculty teams, formulating multi-party evaluation standards, and synergistically promoting employment. The research findings demonstrate that deepening industry-education integration through an "educational ecosystem" is essential for bridging the skills gap. This study provides both theoretical insights and practical benchmarks for the high-quality development of cross-border e-commerce education and its alignment with global digital trade trends.

This study explores the role of vocational education institutions (VEIs) in supporting China's transition to New Quality Productive Forces (NQPF)—a new economic paradigm characterized by intelligent, digital, and green production systems. Drawing on the legacy of Chen Jiageng, a pioneering industrialist and educator, we propose a values-driven leadership model that integrates ethical principles with intelligent technological transformation.Through a mixed-method approach involving surveys and case studies, we identify systemic barriers such as curricular inertia, misaligned incentives, and limited adoption of AI and digital tools. Our findings reveal that values-driven leadership, coupled with smart education technologies, enables VEIs to evolve into agile, innovation-oriented ecosystems.We propose a tripartite strategy: (1) fostering a shared vision for education–technology–talent synergy; (2) establishing AI-enhanced governance for industry-academia collaboration; and (3) promoting digital pedagogies and lifelong learning models. This research contributes to the discourse on sustainable vocational education reform in the era of intelligent manufacturing and offers a replicable model for global contexts.

Vocational high schools are one of the educational stages impacted by Indonesia's low quality of education. Vocational High Schools play a crucial role in improving human resources. Graduates of Vocational High Schools can continue their education at universities or enter the workforce directly. Many students are found to have not yet considered their career path after graduation. At the same time, graduates are still expected to find mismatched employment with their expertise and skills. This research uses CRISP-DM, or Cross Industry Standard Process for Data Mining, to build machine learning models. The approach used is content-based filtering. This model recommends items similar to previously liked or selected items by the user. Item similarity can be calculated based on the features of the items being compared. After students receive job recommendations through intelligent job matching, they can use these recommendations as references when applying for jobs that align with their results. This process helps students direct their steps toward finding jobs that match their profiles, ultimately increasing their chances of success in the job market. These recommendations are crucial in guiding students toward career paths that align with their abilities and interests. The Intelligent Job Matching Model developed in this research provides recommendations for the job-matching process. This model benefits graduates by providing job recommendations aligned with their profiles and offers advantages to the job market. By implementing the Model of Intelligent Job Matching in the recruitment process, applicants with job qualifications can be matched effectively.

Skill-job alignment remains a significant challenge in vocational education, where students often graduate with limited guidance on career pathways matching their acquired competencies. The gap between vocational training outcomes and dynamic labor market demands underscores the need for intelligent systems that facilitate personalized employment recommendations. To address this, a Machine Learning-Powered Skill-Job Matching Recommendation System is proposed for vocational graduates, integrating a novel hybrid model named Scalable Slime Mould optimized-Adaptive Random Forest Tree (SSM-ARFT). The model utilizes a curated dataset combining vocational student profiles, academic performance, certifications, and job requirement metadata gathered from institutional databases and public employment platforms. Data preprocessing is performed using normalization techniques to ensure uniformity across varying data types. For feature extraction, Principal Component Analysis (PCA) is employed to identify the most influential attributes for job-role alignment. The core framework involves mapping students’ skill profiles to job attributes through multi-level filtering and learning stages. The proposed SSM-ARFT algorithm enhances the ARFT by introducing slime mold-inspired metaheuristics for dynamic feature selection and adaptive learning, ensuring robustness and scalability across varying datasets. This intelligent recommendation system helps guide vocational students toward employment options that are closely aligned with their competencies. The proposed method is implemented by using Python 3.10.1. The models were trained and tested with k-fold cross-validation data. The findings determine that the suggested model outperforms traditional methods in metrics such as precision, F1 score, recall, and accuracy, which range from 91% to 94%. The research concludes that the proposed model offers a practical, scalable solution for effective skill-job matching, thereby enhancing graduate employability in vocational sectors.

… education system. It aims to use big data and data mining to improve vocational education and develop students’ professional … evaluation system for vocational education by using a …

Vocational education plays a crucial role in preparing a skilled workforce to meet industrial demands. However, the high unemployment rate among vocational high school (SMK) graduates, reaching 8.62% in February 2024 (BPS), indicates a gap between graduates' competencies and industry requirements. This study aims to examine the relationship between digital literacy, emotional intelligence, and job readiness among vocational students in Kabupaten Lima Puluh Kota. This research employed a quantitative approach with data collected through questionnaires and tests administered to 195 students from SMK Negeri 1 Guguak and SMK Negeri 2 Guguak. Multiple regression analysis was used to evaluate the impact of digital literacy and emotional intelligence on job readiness. The results show that digital literacy significantly influences job readiness (t-value = 6.568, p < 0.05), highlighting the importance of mastering digital tools for employability. Similarly, emotional intelligence has a significant effect (t-value = 1.804, p < 0.05), demonstrating its role in workplace adaptability and collaboration. Furthermore, both variables collectively contribute to job readiness (F-value = 28.623, p < 0.05), explaining 54.9% of its variance. Moreover, strengthening digital literacy and emotional intelligence also supports the development of STEAM-based competencies, which are essential for preparing vocational students for the dynamic demands of the modern workforce. These findings emphasize the need for integrating digital literacy and emotional intelligence training in vocational education to enhance graduates' competitiveness in the labor market

Vocational education (VE) is one of the important components of the global development of skilled workforces. Incorporating emerging technology into Vocational education is essential to creating a skilled work force and providing people with the practical skills needed for a variety of industries. Vocational and regional colleges play an important role in preparing students with more practical skills that enables them to compete in the global economy. A paradigm shifts in the way skills are taught and learned is necessary, nevertheless, as the demands on vocational education have increased dramatically due to the growing automation and technological power of the global economy. VE is being revolutionized by artificial intelligence (AI), which provides creative ways to improve instruction, training, and skill development by changing pedagogy and learning approaches. This chapter examines how AI is changing vocational programs, emphasizing how it might improve teaching and learning methods and equip students for the changing needs of a workforce powered by AI.

Chinas vocational education reform has introduced diverse talent development models, many of which remain unimplemented. This study investigates work-based vocational training for skilled professionals, exploring pathways to cultivate high-level technical talents in modern vocational education and supporting talent development initiatives. Using the Smart Elderly Care Services and Management program at a preschool teacher training college as a case study, we conducted longitudinal research and proposed concrete measures for school-enterprise collaboration. The findings demonstrate that work-based models significantly enhance vocational students professional competencies and employability, providing actionable references for optimizing talent development programs in vocational institutions. Future recommendations include strengthening institutional safeguards for school-enterprise partnerships, improving teachers practical teaching skills, expanding work-based models across more disciplines, and exploring integration with digital education technologies.

… of artificial intelligence (AI) in application-based contexts has created the need for skilled workers with AI … , it is necessary to integrate AI into vocational education and training (VET) in …

This study investigates the role of generative Artificial Intelligence (AI) in enhancing Apprenticeship Systems (ASs) by transforming Tacit Knowledge (TK) into Explicit Knowledge (EK), thereby improving Knowledge Transfer (KT) efficiency. A controlled experiment was conducted with 50 novice live-stream hosts, divided into the Experimental Group (EG) and the Control Group (CG). The EG used to train tools augmented with AI, while the CG used traditional methods. The experimental design included competency tests in seven areas, including on-camera presence, communication skills, and learning ability, and the use of statistical methods to compare the performance results of the two groups. The results established a significant improvement within the EG. The resultant indicators for expressiveness in shots (85 vs. 70), verbal expression (88 vs. 72), and learning capacity (86 vs. 71) exhibited statistically significant differences (p-values < 0.01). These outcomes suggest that the utilization of AI tools effectively enhances the development of various competencies, accelerates learning, enhances adaptability, and provides instant corrective feedback. The study's implication includes the utilization of AI in apprenticeship models, which have the potential for higher scalability, preservation of crucial TK, and workforce development, especially in industries that require Experiential Learning (EL).

In order to deeply analyze the feasibility and optimization strategies of this model in vocational education, this paper focuses on the application of embedded neural network technology. By comparing with other talent cultivation models, especially delving into the essence of project-based teaching, this paper defines the practical, refined, and entrepreneurial characteristics unique to project-based teaching under modern apprenticeship systems. These characteristics constitute the core competitiveness of the advanced apprenticeship training model. This paper emphasizes the establishment of a quality management system based on standards, rigorous processes, and customized solutions. Embedded neural networks, with their powerful data processing and pattern recognition capabilities, help this paper reveal the significant advantages of this training model in resource integration, personalized teaching, skill inheritance, and market docking.

Context: The Fourth Industrial Revolution has accelerated the integration of automation technologies into the world of work, raising important questions about the future of Vocational Education and Training (VET). While existing literature has primarily focused on the labor market impacts of automation, few studies have investigated its direct effects on VET curricula. This article addresses this gap by assessing how automation may influence the structure and content of technical courses offered by Brazil's National Service for Commercial Apprenticeship (Senac), one of the country's largest VET providers.  Approach: We implemented a three-stage methodology to estimate the impact of automation on technical education: (i) Technological mapping, (ii) prompt development, and (iii) assessment. In the third stage, we combined human expertise with generative Artificial Intelligence tools (GPT-4 and Claude 2) to evaluate 2,100 Course Competency Indicators (CCIs) across 35 technical courses. This dual approach enabled a scalable yet context-sensitive analysis, leveraging both the depth of human judgment and the efficiency of AI.  Findings: The technological mapping identified seven key categories of automation technologies: 3D/4D Printing and Modeling, Applied AI, Data Analytics, Digital Platforms and Applications, Extended Reality, IoT and Connected Devices, and Robotics. The developed prompt provided structured guidance for assessing automation impact on CCIs, including instructions for classifying technologies, estimating impact levels, and justifying the results. The assessment showed that 70.3% of the CCIs are at Medium (39.1%) or Low (31.2%) levels of automation impact, suggesting that the courses remain current and relevant, challenging the narrative of rapid obsolescence in technical education. Digital Platforms and Applications were the most frequently cited technology, appearing nearly three times more often than Applied AI and Data Analytics. In contrast, 3D/4D Modeling and Extended Reality had limited relevance in the current course content.  Conclusions: This research contributes to global discussions on the future of VET in the context of rapid technological change. It also highlights how automation risk assessments can support curriculum development by identifying where updates or innovations are most needed. Strengthening the alignment between training programs and emerging labor market demands will be essential to ensuring inclusive, future-oriented vocational education. 

Higher technical education plays a strategic role in equipping the workforce to navigate rapid technological advancements and evolving labor market demands. Within the Rome Technopole framework, Spoke 4 targets ITS Academies, promoting the development of flexible, modular programs that integrate advanced technical skills with ethical, legal, and societal perspectives. This study reports on a pilot training initiative on Artificial Intelligence (AI) co-designed by the Istituto Superiore di Sanità (ISS), aimed at exploring the ethical, practical, and educational relevance of AI in higher technical education. The module was developed and tested through a multi-stakeholder collaboration involving educators, institutional actors, and learners. A four-phase approach was adopted: (1) initial stakeholder consultation to identify needs and content directions, (2) collaborative design of the training module, (3) online delivery and engagement using a CAWI-based focus group, and (4) mixed-method evaluation, combining quantitative assessments and open-ended qualitative feedback. This design facilitated asynchronous participation and encouraged critical reflection on the real-world implications of AI. Through the four-phase approach, the pilot module was developed, delivered, and assessed with 37 participants. Quantitative analysis revealed high ratings for clarity, relevance, and perceived utility in terms of employability. Qualitative feedback highlighted the interdisciplinary design, the integration of ethical reasoning, and the module’s broad applicability across sectors—particularly Healthcare and Industry. Participants suggested including more real-world case studies and collaborative learning activities to enhance engagement. The findings support the feasibility and added value of embedding ethically informed, interdisciplinary AI education in professional technical training pathways. Developed within the Rome Technopole ecosystem, the pilot module offers a promising approach to fostering critical digital literacy and preparing learners for responsible engagement with emerging technologies.

This paper realizes the combination of blockchain technology and modern apprenticeship for the first time. With the development of technology and the innovation of education mode, modern apprenticeship is a kind of education mode combining school education and workplace learning, which is facing the demand of transformation and upgrading. The purpose of this paper is to design a smart platform for modern apprenticeship management system based on blockchain technology, and to build a transparent, secure, objective and traceable system framework based on Hyperledger Fabric by taking advantage of blockchain’ s decentralization, peer-to-peer transmission and smart contracts. Effectively promote the integrity, effectiveness, reliability and security based on the education process. This paper introduces in detail the hierarchical architecture design, the upper chain data structure design, the user authority function module design, the design of intelligent contract and the choice of consensus mechanism. Among them, the intelligent contract automates the apprentice's authentication and record management process, ensuring the transparency and immutability of operation. The consensus mechanism is practical Byzantine fault tolerance (PBFT) algorithm provides a guarantee for system stability, security and efficiency.

Under the background of the integration of production and education, it is of great significance to innovate the cooperative governance mechanism between schools and enterprises in higher vocational colleges. Although the current collaborative governance between schools and enterprises has achieved some results, there are some problems such as insufficient cooperation depth, poor information communication, uneven distribution of benefits and unclear responsibilities. Digital innovation has brought it a turning point. Digital technology has the advantages of improving the efficiency of information sharing and communication, optimizing the governance process, enhancing the accuracy and depth of cooperation, promoting the reform of governance model, expanding the scope and space of cooperation and promoting the quality of personnel training. However, it also faces challenges such as complex technology application and high cost, data security and privacy protection, and insufficient digital literacy of personnel. In order to realize digital innovation, it can be achieved by constructing digital governance platform, innovating governance model, perfecting governance system and improving digital literacy of personnel. In the future, higher vocational colleges, enterprises, governments and industry associations need to cooperate in many ways to promote the digital transformation of school-enterprise collaborative governance and help high-quality economic and social development.

Digital transformation in vocational education is often discussed in terms of platforms, infrastructure, or curricular modernization. Less attention has been paid to the redistribution of pedagogical and organizational work that accompanies these changes. This article examines how vocational teachers’ professional roles are being reshaped at the intersection of school–enterprise partnership and AI-enabled pedagogy. The study adopts an integrative review of scholarship on vocational education digitalization, teacher professionalism, work-based learning, and artificial intelligence in education, combined with a policy-text analysis of national Chinese and Liaoning provincial documents. The analysis shows that digital transformation does not simply add new tools to existing routines. It expands teachers’ work in five directions: learning design, boundary-spanning coordination with industry, data-informed mentoring, curriculum and resource curation, and ethical governance of AI use. School–enterprise partnership functions as an organizational mechanism because it redistributes curriculum development, assessment, and practice supervision across institutional boundaries. AI-enabled pedagogy functions as a pedagogical mechanism because it changes how teachers diagnose learner needs, organize practice, provide feedback, and monitor progression. In Liaoning Province, where digital campus construction and industrial upgrading are advancing together, the coupling of these two mechanisms is especially visible. The article argues that the future vocational teacher is neither a traditional lecturer nor a mere platform operator, but a professional who connects occupational standards, digital resources, and student development. On this basis, the paper proposes practical pathways for vocational colleges, including teacher industry residency, discipline-specific AI professional development, workload recognition for partnership work, and human-centered governance of educational data and AI applications.

: This study aims to explore the path of promoting digital technology in vocational education under the collaboration of schools and enterprises, delving into the key role of digital technology in vocational education. Through literature analysis and case studies, the positive impact of digital technology and the crucial role of school-enterprise collaboration are discovered. By discussing the integration of digital technology, updating educational content, and training teachers and students, it aims to meet the needs of the digital age. Finally, it summarizes the key elements needed for the promotion of digital technology, calling for cooperation among all parties to promote the digital transformation of vocational education.

Now, more than ever, school-enterprise collaboration in modern vocational education has the power to transform the future. Investigating this powerful alliance through the lens of the unity of knowledge and action reveals a natural synergy, shared values, and deep compatibility between this educational philosophy and collaborative practice. In an era defined by rapid AI advancement, the urgent challenge of bridging the gap between knowledge and practice must be met head-on. By fully embracing the unity of knowledge and action, schools and enterprises can: - Forge a shared destiny community, empowering both partners to achieve unprecedented results - Jointly develop dual-qualified faculty, ensuring students learn from the best of both worlds - Co-create agile, industry-responsive professional clusters and curricula, keeping pace with tomorrow's demands - Share advanced, integrated virtual-physical internship and training systems, equipping students for real-world impact Together, these strategies will not only cultivate high-skilled talents ready to thrive in the age of AI, but also drive the innovative, high-quality growth of modern vocational education itself.

… for teachers to possess digital literacy skills to effectively prepare … work placements are prevalent in many professional … in secondary vocational schools by school-enterprise cooperation …

Teaching digitalization and integration of industry and education are developing deeply in the field of education, this study designs and constructs the digital practical training system, innovates the teaching mode of school-enterprise collaboration, and applies it to the teaching practice of tourism specialty. The performance of the digital training system for tourism majors is tested by concurrency test, business success rate test and target system thing test. Design teaching experiments to verify the teaching effect of the digital practical training system and the school-enterprise collaboration model by comparing the gaps and changes between the experimental group and the control group in the competitiveness of students' employment, the utilization rate of resources, the tourism market research, the tourism marketing, the results of the digital practical training, and the development of tourism projects. The maximum number of users in concurrent testing of the digital practical training system for tourism majors is 20, the average number is 10.182, and all the operations of users are processed, achieving good test results. Before the experiment, there is basically no difference between the two groups in the six aspects of employment competitiveness, resource utilization, tourism market research, tourism marketing, digital practical training results and tourism project development. After the experiment, the two groups showed large differences. The scores of the experimental group were higher than those of the control group in all 6 dimensions, and the difference in the scores of each dimension was more than 5 points. The teaching effectiveness of the experimental group rose more than 4.9 points in all 6 dimensions. And the score difference between the pre-and post-test of the control group is not more than 0.5 points. In this paper, digital practical training system and school-enterprise collaboration model have better teaching effect.

The rapid changes in ICT technology have made it necessary for industry talents to constantly adapt to technology updates, and meanwhile, continuous integration and collaboration of business further strengthen the comprehensive requirements for talents. Application-oriented colleges and universities guided by vocational needs and based on engineering scene, need to carry out teaching design, aiming at developing professional job skills in line with market demand. The core content of the curriculum co-construction of school-enterprise cooperation includes the construction of practical talent training system, related standard of education and teaching, faculties of “dual abilities”, high-level practice base, certification training as well as curriculum and employment docking platform. During this process, enterprises can help colleges and universities create application-oriented curricula and promote the deep curriculum transformation of colleges and universities in talent training, professional teaching, quality education and human resource service with the close connection between professional education and regional industry development, in order to achieve the ultimate goal of industry-teaching integration.

… effectively enhance the professional abilities of graduates … and the digital economy, especially the acceleration of digital … enhancing the professional competence and development …

Modern apprenticeship is an effective and important talent training model in line with the law of vocational education in order to adapt to the Made in China 2025 strategy and cultivate high-quality and high-skilled talents in the field of advanced manufacturing.Therefore, the implementation of modern apprenticeships is of great strategic importance for improving the quality of vocational education.However, at present, the implementation of modern apprenticeship in higher vocational colleges is facing many problems, and deepening the integration of industry and education, further improving the school-enterprise collaborative education mechanism, and cultivating innovative technical talents are the only way for the development of higher vocational education.

The paper aims to explore the key role of platform enterprises in the digital transformation of vocational education, discusses the challenges and opportunities brought by digital technology, and how stakeholders from schools and enterprises can cooperate to improve the quality of digital transformation in vocational education. Guided by Maslow's hierarchy of needs, survey research method, case study method, element analysis method, etc. were adopted. (1) investigating the current situation of vocational education reform and innovation in C city, (2) analyzing the practical difficulties and needs of vocational education development, (4) conducting attribution analysis and element analysis were conducted on existing problems based on the hierarchy of needs, (4) proposing corresponding countermeasures and suggestions. By studying the current situation of digital construction in modern vocational education in C city and from Maslow's hierarchy of needs, the paper explores the practical difficulties and needs of the digital development of vocational education in terms of safety needs, social needs, esteem needs, and self-actualization hierarchy of needs, proposes countermeasures and suggestions to accelerate the digital transformation of vocational education, cultivate new types of labor force, and enhance technological innovation capabilities. From the perspective of the hierarchy of needs, the integration of industry and education in vocational education, as well as school enterprise cooperation, requires both parties to recognize the elements of safety needs, social needs, esteem needs, and self -actualization needs, and based on the mutual satisfaction of these needs, promotes collaboration and coupling. The paper proposes a series of optimization strategies, including consolidating the digital foundation of vocational colleges, reshaping the professional course training system, accelerating the teaching reform of vocational colleges, building a new high-quality talent highland, promoting the application of diversified scenarios, and strengthening the construction of digital ecological environment.

With the growing demand for applied financial talent in society, financial majors face the challenge of improving the effectiveness of practical teaching. To address this issue, this study has constructed a financial comprehensive practical teaching platform based on school-enterprise cooperation. This platform closely integrates practical teaching content with actual corporate projects and job requirements, develops rich teaching resources, and establishes a process-oriented assessment system focused on competency evaluation. The application results indicate that teaching through this platform significantly enhances students' financial practical abilities and competitiveness in the job market. The significance of this study lies in exploring the path of cultivating financial talents through in-depth cooperation between schools and enterprises, providing an effective framework for constructing practical teaching platforms for financial majors, and offering valuable insights for advancing the education of applied financial talents. Future research will continue to enrich and optimize the platform's functional design, resource development, and assessment mechanisms to continually enhance the practical abilities and employment quality of students majoring in finance.

Against the backdrop of intelligent technologies such as artificial intelligence (AI) and big data driving revolutionary industrial transformations, this study aims to address the fundamental restructuring of industrial design paradigms and talent roles in the AI era by constructing a dynamic competency model centered on "value-system-collaboration" and its enabling evaluation system. The research first demonstrates the paradigmatic shift of industrial design from a "tool for creating objects" to a "definer of intelligent ecosystems," clarifying the upgrading of designers' roles to "ecosystem architects." Based on this, a three-tier competency ecosystem framework is proposed, with value ethics as the guide, systems thinking as the backbone, and human-machine collaboration as the lifeblood. To implement this framework, an intelligent evaluation closed-loop is designed, shifting from "summative assessment" to "formative empowerment." Through full-process data perception and diagnostic feedback, teaching is driven to accurately align with competency objectives. This study provides a systematic discussion from theoretical model to evaluation system for cultivating the core competencies of industrial design talents facing the in-telligent era.

… From the perspectives of students and instructors, evaluations and assessments … Vocational education concentrates on a single or mature skill to train applied IM talents. For the …

In the age of digital intelligence, supported by the rapid development of artificial intelligence generated content (AIGC) and intelligent manufacturing, the textile and apparel industries are undergoing a comprehensive transformation, moving from traditional manufacturing to data-driven design and intelligent production. These aspects of production are gradually being implemented throughout the fashion industry's value chain, from spinning to retail. Consequently, there is a growing demand for fashion designers who are highly versatile and possess strong digital and innovative skills. This study aims to construct a training mechanism for vocational undergraduate fashion design education. The challenges facing domestic higher vocational education—such as outdated and rigid curricula, similar competency structures, the decline of practical teaching and school-enterprise cooperation, and one-sided evaluation mechanisms—are analyzed in detail. Based on this analysis, a competency framework oriented toward "digital intelligence + future skills" is proposed, as well as a "five-in-one" approach to cultivating fashion design professionals, including the use of artificial intelligence technology in content and teaching-method development, the development of an interdisciplinary curriculum based on cooperation between art and engineering, the creation of authentic school-enterprise co-creation teaching scenes, the digital construction of faculty teams, and the improvement of outcome-based evaluation and skill-certification systems. This provides a theoretical basis and a practical model for the construction of Chinese-style vocational undergraduate fashion design education, along with a "China solution" for the global popularization and application of vocational education.

Based on the fact that information technology will continue to be used in manufacturing projects and promote the development of Intelligent Manufacturing Engineering, this paper studies the application of information technology in intelligent manufacturing education. With the transformation of many enterprises towards intelligent manufacturing, the informatization of manufacturing industry, the virtualization of physical resources and the intellectualization of production process are developing continuously. More and more enterprises pay attention to the informatization and intellectualization of manufacturing industry. This paper takes the intelligent manufacturing productive training base as the carrier of deepening the integration of production and education, and combines the education supply and industrial demand of Intelligent Manufacturing Talents in China to study the cultivation and research of talents in higher vocational colleges, so as to serve the regional economic development.

To meet the demand for talent training in the green, intelligent, and integrated development of the petrochemical industry, this article explores the training of innovative talents through intelligent integration of industry and education. Artificial intelligence and petrochemical engineering featured “intelligent oil” class is proposed to train talents with engineering practice ability and technological innovation ability. The “dual system” talent training model and the “target problem oriented” curriculum system are implemented in the talent training process, and the intelligent teaching system is introduced through a case study of the course titled “Principles of Chemical Engineering”.

The competency requirements for digital-intelligent finance and business are shifting towards a multi-dimensional and dynamic structural transformation, and the training center needs to construct an appropriate teaching model and job matching mechanism. This study proposes a three-layer framework that includes a teaching paradigm transformation, a job adaptation mechanism, and a collaborative adaptation path. By deconstructing the competency domain into four sub-domains of data perception, logical deduction, decision response, and value evaluation, this study achieves a modular reorganization of training elements and constructs a task-driven immersive framework. This study adopts dynamic job profiling, a virtual-real collaborative task chain, and personalized path generation to form a job adaptation mechanism. By optimizing the mapping between modules and functions, designing dynamic resource scheduling rules, and establishing an effectiveness and competency calibration model, this study achieves the coordination of teaching elements and job requirements. This research provides a theoretical basis and a technical path for the teaching model design of the training center.

Driven by the manufacturing power strategy and education digital transformation, intelligence-education integration has become the core driver for reshaping intelligent manufacturing talent cultivation in higher vocational colleges. Facing the talent shortage bottleneck in the intelligent manufacturing industry, this study systematically analyzes the current dilemmas in talent cultivation, such as mismatched training orientation, disjointed teaching system, and lagging training platform construction. Drawing on practical experience from multiple vocational colleges, a “three-dimensional collaboration + four-linkage coordination” talent cultivation model is proposed. Relying on industry-education synchronization, digital-intelligent empowerment, and literacy integration, the model realizes precise alignment between talent cultivation and industrial needs through curriculum reconstruction, teaching innovation, training platform upgrading, and evaluation optimization. Practical data from 10 pilot colleges shows that the model significantly improves students' digital skills and employment quality, with the employment rate increasing by 17 percentage points and the digital skill compliance rate rising by 43 percentage points. This study provides a feasible path for the development of intelligent manufacturing professional clusters in vocational colleges and offers reference for vocational education reform.

Background: Competitive, integrated employment (CIE) rates for individuals with disabilities have continued to be much lower than their peers without disabilities. Integration of advanced technologies to develop and refine innovative, scalable, and sustainable programs that promote CIE in high-demand, economically self-sustaining fields will lead to beneficial outcomes for this population. Objective: The Delivering Innovative Vocational Education through Virtual Reality Technology (DIVE-VRT) project will integrate virtual reality and artificial intelligence technologies into an existing, highly successful transition and employment program to train and certify youth and adults with lifelong and acquired disabilities in skilled trades. Methods: The DIVE-VRT model will be tested and refined with multiple cohorts over the 5-year project. The development of an accessibility AI system, using generative AI and natural language processing (NLP), will be implemented in a user-friendly project website providing user training, resources, and AI-powered site monitoring, moderation, data collection, and support. Findings: This project is in the pilot phase. A third-party evaluation team is collecting qualitative and quantitative data to assess the effectiveness of the DIVE-VRT program in increasing CIE outcomes for program participants. Conclusions: Implications and considerations for utilizing advanced technology in rehabilitation and mental health counseling will be presented.

… Collaborations between industry and vocational institutions … ensuring that India builds an AIready workforce. However, the … such as annotation work, AI evaluation, customer interactions, …