教育、科技、人才视角下的研究生培养

The development of social science and technology has promoted the integration and intersection of the original knowledge of different disciplines, making interdisciplinary education a trend in college reform and talent training. Design talents are no exception. For example, the training of industrial design graduate students is increasingly emphasizing their comprehensive quality and compound ability. Among them, academic ability, as the core quality of students, is the first point to test students’ scientific research level and innovation ability. Therefore, in order to improve the current situation of outdated design education curriculum system, traditional teaching methods, and lack of knowledge integration, it is first necessary to deeply understand the composition of academic ability of graduate students in this field. This research focuses on the evaluation of the academic ability of industrial design graduate students. Through expert surveys and questionnaires, the standard of data academic ability of graduate students is constructed. From problem awareness, literature ability, logical reasoning, language expression, organizational management, psychological coping, scientific research innovation and insight ability The postgraduate academic ability test scale is compiled at the same level to provide an objective evaluation tool for comprehensively measuring the academic ability level of postgraduates. See the micro-knowledge, and strive to provide a corresponding reference for the training and education of industrial design talents in my country with objective and comprehensive analysis data.

Graduate education is responsible for cultivating high-level talents, solving key core technologies, and leading industrial upgrades. In response to the structural contradictions faced by graduate education in China, such as the lag of knowledge production paradigms behind the forefront of technology and interdisciplinary integration, educational technology remaining at the level of tool empowerment, and the imperfect integration mechanism of industry and education, this paper analyzes the current state of graduate education and proposes systematic reform and innovation strategies by combining multiple paths such as interdisciplinary training and the application of artificial intelligence technology, aiming to provide a reference for constructing a high-level talent training system that meets the needs of the new era.

: With the continuous emergence of technologies, digitalization has become a key driver of educational reform. From online courses to the advent of various intelligent tools, digitalization is reshaping educational models. Data shows that by 2024, the global online education market size reached approximately $370 billion, with a year-on-year growth of about 20%. An increasing number of universities are adopting digital technologies to break through time and space constraints and meet students' digital learning needs. This paper focuses on analyzing the integrated development of digital empowerment in higher education technology talent. It first examines the current status of integrated development in higher education technology talent, explores the significance of digital empowerment in this field and existing challenges, and proposes targeted strategies such as strengthening digital infrastructure construction and promoting digital teaching and research models, aiming to provide reference for relevant professionals.

With the rapid development of information technology, computer cloud platform, as an innovative technology, is reshaping the mode of data storage, processing and application, it provides a new path for the cultivation of innovative and entrepreneurial talents under the background of the integration of industry and education. This paper focuses on the application effect of computer cloud platform in the integration of industry and education, analyzes its influence mechanism on the quality of talent training, and reveals its core values in this field in combination with national policy guidance and typical cases, to provide a useful reference for educational reform.

In the context of the transformation of social production methods driven by big data and artificial intelligence (AI), the contradiction between the traditional "discipline-based" model of higher education and the AI-driven demand for "composite competencies" has become increasingly prominent. The outdated talent cultivation and evaluation models fail to meet the demands of the times. This paper, starting from the educational transformation driven by technology, and combining the theory and practice of interdisciplinary integration, proposes a framework for the reconstruction of engineering education centered on "building interdisciplinary knowledge networks — training problem-solving skills — human-machine collaborative innovation practices." It suggests that higher education should transform its talent cultivation model from a "discipline-based" to a "competency-based" approach through the reconstruction of curricula, innovation in teaching models, integration of resource ecosystems, and optimization of talent evaluation systems. The goal is to cultivate interdisciplinary talents with critical thinking, interdisciplinary literacy, human-machine collaboration, and lifelong learning abilities.

This study explores the theme of “Big Data Empowering Clinical Medicine Graduate Education from the Perspective of Ideological and Political Guidance.” It examines how big data technology can enhance the quality of graduate education in clinical medicine. By analyzing the integration of ideological and political guidance theory with big data technology, this paper aims to uncover the potential opportunities and challenges of big data in improving clinical medicine graduate education. The research employs literature review and case study methods to delve into the potential applications of big data technology in curriculum design, teaching quality monitoring, student evaluation, and personalized learning support. The results indicate that big data technology provides new tools and methods for clinical medicine graduate education, contributing to the personalization, precision, and efficiency of education. However, the application of big data technology also faces challenges such as data privacy protection and transparency in technology application. This study offers theoretical support and practical guidance for the introduction of big data technology in the field of clinical medicine graduate education, holding significant implications for promoting the innovation and development of educational technology.

创新教育研究 Abstract By conducting an in-depth analysis of existing problems in the training of computer science graduate students in local colleges and universities, we design an innovative education model for cultivating industry-university-research-application talents. This model encompasses four key elements: collaborative and diversified training objectives, specific training plans, personalized training methods, and dynamic evaluation of the cooperation model. The proposed indus-try-university-research-application collaboration model not only improves the innovative thinking and practical ability of computer science graduate students, but also promotes personalized development of graduate education in local colleges and universities, and provides useful inspiration and reference for talent training in other fields.

Today, graduate art education is facing both challenges and opportunities in this era. This study examines the impact of current trends on graduate art education and offers guidance on how to navigate this evolving landscape. The advancement of technology has rendered traditional educational methods inadequate in meeting the needs of learners. The overwhelming amount of information and the prevalence of misinformation pose significant obstacles; however, digital technology also provides students with enhanced learning processes and opportunities for academic collaboration. Innovation is recognized as a key factor in improving graduate art education through the development of new courses, interdisciplinary projects, and practical experiences that allow students to broaden their perspectives and strengthen their interpersonal and interprofessional skills. To elevate educational standards, universities should focus on enhancing faculty expertise, improving curriculum quality, and establishing robust quality assurance mechanisms. Furthermore, this study emphasizes the significance of art in graduate education by high-lighting its potential to foster student growth and improve employment prospects. Looking ahead, graduate art education should prioritize the integration of technology into artistic practices, supported by disciplinary integration, individualized approaches, internationalization, and globalization, as well as the incorporation of innovative educational paradigms alongside practical teaching and learning methods. By implementing these initiatives, universities can better align themselves with contemporary trends, thereby advancing graduate art education toward a more promising and expansive future.

With the rapid development of the digital economy, the big data industry faces a prominent imbalance between talent supply and demand, as traditional education models struggle to meet industry requirements for innovation and entrepreneurship talent. This paper focuses on the collaborative mechanisms of industry-education integration and university-enterprise partnerships, systematically analyzing four core issues in current big data talent cultivation: the disconnection between curriculum systems and industrial needs, insufficient depth in university-enterprise collaboration, lagging innovation in teaching methods, and weak practical capabilities of faculty. To address these challenges, the study proposes a "Four-Chain Integration" collaborative education ecosystem. This model includes reconstructing curricula through industry chain alignment, empowering teaching with research achievements via innovation chains, optimizing blended digital teaching modes through education chains, and establishing industry-education evaluation mechanisms via talent chain closure. The research further proposes safeguard systems, including a quaternary governance ecosystem, dual-cycle resource pools, and dynamic adjustment mechanisms, to deepen the integration of education and industry chains. Future efforts should expand international cooperation, integrate generative AI and metaverse technologies, and strengthen interdisciplinary collaboration to build a more adaptive big data talent cultivation ecosystem. This paper provides theoretical and practical insights for addressing industry-education integration challenges and enhancing innovation and entrepreneurship education quality.

This paper explores the implications of augmented intelligence as a case study of a disruptive technology, and the necessary graduate attributes for university students to thrive in this new paradigm. While existing frameworks address fundamental skills, they often overlook the specific attributes needed for Industry 5.0. Using Employee Ambidexterity and the Talent Management Pipeline as the theoretical foundation, this paper aims to bridge the gap. This paper entails a conceptual discussion to conceptualise GAs in the age of AI systematically. We outline a three-step approach to assist with theory synthesis, which involves the conceptual integration across multiple theoretical perspectives throughout the article. The study proposes a universal framework that aligns with the industrial demands, and includes conceptual, technical and human skills. The paper also reinforces the importance of strengthening collaboration between universities and industries and provides recommendations to universities to better teach and prepare graduates ready for the evolving workforce. This paper specifically explores graduate skills of university students in the context of Industry 5.0.

The transformative integration of digital technologies into occupational frameworks and talent competency ecosystems has established educational digitalization and graduate digital literacy enhancement as pivotal components of national reform strategies, particularly within control science disciplines facing AI-driven industrial transformations. Addressing the critical demand for high-level research capabilities, this study employs a mixed-methods approach to investigate sustainable digital literacy cultivation pathways through a longitudinal case study (2020–2023) of the Non-linear Control Systems course. A pedagogical framework was developed, integrating three core mechanisms: goal-oriented instructional design aligned with DIGCOMP 2.2 standards and a quadruple mentorship system encompassing digital literacy, academic, project, and enterprise mentors. Implementation outcomes demonstrated statistically significant advancements, including a 55%−68% improvement in technical problem-solving competencies (p < 0.01). Student satisfaction rates surged from 78% to 95.45%, with medium-tier academic performances eliminated post-intervention. These findings substantiate the framework's efficacy in bridging theoretical-practical gaps through competency-based assessment protocols and adaptive mentorship architectures. The study contributes actionable strategies for engineering education reform, including scalable curriculum-digitization models and evidence-based industry-education integration frameworks, ultimately cultivating professionals equipped with sustainable digital competencies for intelligent manufacturing ecosystems.

This study explores the application of artificial intelligence (AI) and deep learning (DL) technologies in graduate education to promote the inheritance and development of the scientist spirit. This study employs a Long Short-Term Memory (LSTM) network to predict students' learning paths. Meanwhile, it constructs a DL-based personalized learning path and resource recommendation model by integrating a hybrid recommendation mechanism combining collaborative filtering and content-based filtering. The model inputs students' historical learning data and utilizes LSTM to capture long-term dependencies for predicting future learning activities. At the same time, it dynamically adjusts the learning rate through a reinforcement learning mechanism to optimize model performance. Additionally, this study introduces the Local Interpretable Model-Agnostic Explanations (LIME) algorithm to enhance the model's interpretability, ensuring that educators can understand the model's decision-making logic. Model training employs cross-validation techniques, and Principal Component Analysis (PCA) is used for dimensionality reduction and feature selection to improve data processing efficiency. Experimental results demonstrate that the DL model significantly outperforms traditional models in personalized learning path prediction, resource matching efficiency, and student performance prediction. Particularly, the DL model has an accuracy of 92.5%, an F1 score of 91.8%, an Area Under the Receiver Operating Characteristic Curve value of 0.95, a user satisfaction rate of 89.2%, and a prediction bias of only -0.75%. Furthermore, through user satisfaction surveys and expert reviews, this study qualitatively analyzes the impact of AI and DL technologies on educational practices. This confirms their value in enhancing education quality and fostering a scientist spirit. The study concludes that AI and DL technologies can effectively optimize graduate education models and promote the inheritance of the scientist spirit. Moreover, these technologies can cultivate innovative capabilities and provide theoretical support and practical guidance for intelligent educational reform.

This paper seeks to address the challenges of underdevelopment bedeviling the African continent in general and Zimbabwe in particular on the integration of Artificial Intelligence (AI) in higher education. Universities in Africa have over the years produced many graduates in Science, Technology, Engineering, and Mathematics (STEM) related learning areas but continue to grapple with underdevelopment both economically and intellectually. Part of the problem has been identified as a lack of quality in the education system that is not able to produce graduates with adequate 21st-century skills for the achievement of the Sustainable Development Goals (SDGs) to fulfil the Global Agenda 2030. Achieving this is possible if Africa moves a step forward and embraces modern technologies like the integration of AI in her education system. A technologically astute graduate would be able to apply their knowledge in achieving the much-needed sustainable economic development. This article highlights how the implementation of AI in higher education can foster a turnaround in Africa’s education systems and lead to industrialisation and innovation. Pivotal aspects of AI like the Turing machine test, cloud computing, big data, and machine learning are explored in an attempt to add value to the current education discourse. This study is situated in the interpretive paradigm, the qualitative approach was employed to gather data using several methods like e-questionnaires (open-ended), document analysis, and review of journals and other academic publications. Purposive sampling of STEM educators from a renowned university in Zimbabwe was undertaken to gather information on the ground and the data was thematically analysed. The article argues that Africa has the potential to be a leader in Industrialization and commerce should the continent fully embrace AI in higher education.

The COVID-19 pandemic led to a rapid transition to online learning, thereby significantly impacting higher education. This study examines the experiences of students, instructors, and university administrators from 22 Thai universities during the pandemic and explores the potential consequences for the future of higher education. Utilizing a mixed-methods approach, data were gathered through focus group discussions with 30 participants and a survey conducted with 510 undergraduate, graduate, and postgraduate students. The findings highlight the importance of flexibility, technology integration, and adaptability in curricula and instructional methods to enable effective online learning. Additionally, the study emphasizes the need for continuous improvement in the education sector, driven by the rapidly changing demands of the job market and the evolving nature of technology. Practical steps to be taken include prioritizing student learning outcomes, fostering digital literacy among instructors and students, and promoting collaboration across disciplines. Future research should examine the long-term impact of the pandemic on higher education and explore additional strategies for supporting students and instructors in the next normal.

: New quality talents are a key element in developing new quality productivity, and cultivating new quality digital intelligent business professionals with professional skills and innovative abilities is an important issue currently facing us. This article elaborates on the necessity of the new quality, quantity, intelligence, and business talent training model under the four abilities, four chains, and four synergies model from five aspects. It proposes five measures, including accurately positioning talent training goals, cross disciplinary integration and reconstruction of professional curriculum systems, deepening cooperation between government, schools, and enterprises, comprehensively evaluating the quality of education, and strengthening the construction of a "dual teacher" teacher team oriented towards new quality productivity. These measures provide reference for relevant talent training.

The economic development of Ukraine and Poland is possible under conditions that foster the establishment of new research-based and industrial structures, job creation, increased efficiency and profitability of enterprises, and the optimal use of financial, labor, and material resources. The implementation of these processes can be ensured by a new generation of computer science specialists who possess innovative thinking and are familiar with production technologies and marketbased management methods. At the present stage, higher education institutions face new requirements in training such specialists, taking into account both international standards and national approaches to professional readiness. Consequently, the modernization of educational technologies and the acquisition of leadership competencies in the training of computer science specialists become increasingly relevant. Professional leadership competencies significantly contribute to the formation of a company’s business reputation (goodwill), ensuring additional growth in profits and capitalization. Under modern conditions, the effective organization of processes for developing professional leadership competencies requires the use of information technologies that enhance adaptability and improve the efficiency of managing information flows related to the development, adaptation, and assessment of educational materials. The application of information technologies in internal human resource management substantially increases labor productivity at all management levels of vertically integrated enterprises. At the same time, an analysis of existing approaches to the formation of professional leadership competencies in industrial enterprises engaged in innovation management reveals that the role of modern information technologies as tools for improving training efficiency, knowledge assessment, internal talent pool formation, and goodwill growth remains insufficiently explored. The purpose of this study is to develop informatization tools for the formation of professional leadership competencies in innovative activities of vertically integrated industrial companies, based on automated knowledge transfer, consideration of a three-level management structure, and the evaluation of implementation effectiveness through goodwill-based indicators, taking into account the specific features of particular industrial sectors.

The subject matter of this article is the relations in the field of education, in the context of the automation and digitalisation of the process of graduate studies in higher education to prepare highly qualified personnel, from the perspective of law, based on a number of Russian regulatory legal acts, including: the 2012 Education Law of December 29, 2012, No. 273-FZ; the 1996 Science and State Science and Technology Policy Law of August 23, 1995, No 127-FZ; the Government Decree No. 30 on Scientific and Pedagogical Staff Training for Postgraduate Studies of November 30, 221; Russian Government Regulation No. 842 on Awarding Scientific Degrees of September 24, 213; Decree of the President of Russia No. 13 on Certain Issues of Improving the Higher Education System of May 12, 334; et al. The author proceeds from the objective and subjective premises of the world, relying on dialectics during the research as a method of argumentation, a form and way of reflective theoretical thinking, as well as scientific methods of researching private and public law (for example, the comparative legal method), the method of functional analysis, etc. The scientific novelty stems from the purpose of the study. The author concludes that: The dialectical approach to postgraduate education based on modern information technology is morally, ethically, and pedagogically sound. To implement the aforementioned dialectical approach to graduate education, the development of a draft federal Gantt chart is needed based on artificial intelligence algorithms for all types of graduate work, including its discussion with the scientific community, the Ministry of Education and Science, the Russian Academy of Sciences, and the Higher Attestation Commission to introduce changes and amendments to the draft. Based on the draft adopted as a basis (paragraph 2), in accordance with established procedures, amendments and additions are made to legislative and other acts related to training highly qualified personnel in this area.

Artificial intelligence (AI) provides an innovative direction for the development of postgraduate education in police colleges. This study investigates the integration of AI with postgraduate education in police colleges, analyzing its role in enhancing teaching quality. Specifically, AI facilitates the reconfiguration of policing pedagogy, AI promotes multidimensional synergy in practical competency development, and AI drives deep integration of “Police Colleges + Public Security Bureau” ecosystems. Based on these findings, the intrinsic mechanisms through which AI empowers postgraduate teaching quality are systematically elucidated. Furthermore, a comprehensive development pathway for the training mechanism is proposed, encompassing seven dimensions: upholding the concept of AI-driven teaching reform, leveraging AI for teaching resource development, building AI-enhanced teaching platforms, implementing AI-optimized policing curriculum systems, strengthening the deep integration of AI and policing operations, strengthening the construction of AI teaching effectiveness evaluation system strengthening the construction of AI teaching effectiveness evaluation system, enhancing faculty competence in AI utilization.

ABSTRACT Master and doctoral postgraduate training approaches in mining institutions vary across different countries, each possessing unique characteristics. This study seeks to provide a comprehensive overview of these diverse methodologies, offering valuable insights for the development of exceptional talent in the Chinese mining sector. The research examines postgraduate training in mining from three distinct angles: curriculum design, educational processes, and institutional mechanisms. Our investigation spans 19 mining universities in nine nations, including China, the United States, Russia, France, and Germany. The findings suggest the need for curriculum optimization to better align with both industry demands and the contemporary social context. We recommend a prioritization of international coursework, reinforcing “master-doctoral” mining programs, and expanding the availability of multidisciplinary courses for postgraduate mining students. There is a need to reinforce competitive mechanisms, promote collaboration between academia and industry, broaden international joint training pathways, and improve the quality of mining graduate education. Additionally, it is essential to enhance the supervisory responsibility system, supported by a diversified funding structure, to elevate the training of future global leaders, organizers, and innovative talents within the mining sector. This comprehensive approach aims to optimize the training mechanisms for postgraduate students in the field of mining.

Postgraduate training performance evaluation is an important part of higher education evaluation. In order to quantitatively evaluate the performance of postgraduate training combined with the needs of talent training in colleges and universities, a comprehensive evaluation system for postgraduate train-ing performance is designed and proposed. The analytic hierarchy process and the entropy method are combined to determine the weights of seven in-put and output indicators, such as funding input, teacher source input, sci-entific research ability, and graduate output quality. The comprehensive fuzzy evaluation model is also applied. This paper tests the above models to evaluate the graduate training performance of 20 colleges at Tianjin Univer-sity. It is found that the weight of student input and graduate output quality combined is 87.69%, which makes them the key indicators to measure the performance of graduate training. Furthermore, it is concluded that the per-formance of the science college was significantly higher than that of the humanities college.

With the development of education, the speed of knowledge updating and differentiation has increased sharply, and the society's demand for talents of different levels and specifications has promoted the diversified development pattern of postgraduate student ability training. Unlike undergraduate education, postgraduate education focuses on cultivating students' research systematic, innovative, and independent. The difficulty is that each student's personality and background are different, and it cannot be cultivated with standard model. The postgraduate education reflects the characteristics of uniformity in terms of goals, training methods, and degree types. The goal of professional postgraduate training is to cultivate workers engaged in teaching and scientific research, especially with certain scientific research ability, and can independently carry out relevant scientific research work. According to the different stages of postgraduate students training and the different training objectives, the cultivation of professional postgraduate research ability based on differential evolution algorithm is proposed, combined with the evolution of the previous ability, the specific implementation experience of postgraduate training carried out good results.

This research investigates the use of diverse visual knowledge communication tools in the multidisciplinary training produced in the BC4ECO project. During this project, teaching and learning content was developed to enable postgraduate learners to utilise blockchain and Distributed Ledger Technology (DLT) to solve complex, real-world problems related to environmental sustainability. We examine how visual modelling techniques enhance comprehension across disciplines and facilitate interdisciplinary and transdisciplinary collaboration, particularly for learners who may not have prior programming or computer science knowledge. Findings suggest that using these tools leads to enhanced collaboration between computer scientists and non-computer scientists and aids in the facilitation of joint problem solving between seemingly disparate disciplines. Additionally, the study highlights how the use of tools for established software engineering modelling, like Visual Paradigm for UML, bridges the gap between theoretical knowledge and practical implementation, strengthening problem solving skills and improving software development education.

This paper describes the authors' experience in teaching a cybersecurity module as part of general MSc courses, rather than a specialist MSc in Cybersecurity. There are three variants of this course which have been fully delivered: a traditional MSc generalist course, fulltime and face-to-face, largely aimed at people without much formal Computer Science training; an MSc-level degree apprenticeship for those sponsored by their employers; a purely on-line part-time MSc generalist course in Computer Science. In addition a version for second-year undergraduates is being delivered for the first time, overlapping with the writing of the paper. There are numerous comments and curriculum suggestions for cybersecurity, and the authors' had to choose what was relevant, what would appeal to the students, and what would leverage the authors' strengths.

This study investigates the potential of the metaverse as an educational tool in higher education and its impact on students’ employability skills. With the ongoing digital transformation, the metaverse offers an immersive environment integrating virtual reality, collaboration, and experiential learning. The research aims to evaluate how participation in metaverse-based activities enhances competencies required by modern labour markets, including digital literacy, creativity, communication, and adaptability. A quantitative design was applied, involving a structured questionnaire administered to a non-probabilistic sample of 200 Spanish undergraduate and postgraduate students, aged 18 to 25, across various academic disciplines. The survey examined students’ perceptions of metaverse-enriched learning, its influence on competence acquisition, and its relevance to future professional engagement. Data analysis, which utilized Structural Equation Modelling (SEM) and independent samples t-tests, confirmed strong correlations between active involvement in immersive environments and improvements in employability profiles. The results indicate that Perceived Usefulness (PU) was the strongest predictor of Intention to Use the metaverse (β = 0.51, ρ < 0.001). Furthermore, the study found that Engineering students reported significantly higher Perceived Usefulness (M = 4.12, ρ < 0.001) than Social Sciences students, and female students showed a greater willingness to invest time in acquiring new technological skills (Perception of External Control, M = 4.15, ρ = 0.003). The findings suggest that the adoption of metaverse technologies by higher education institutions can reduce the skills gap between academic training and professional practice. Simulation-based activities and collaborative projects in virtual spaces prepare students for hybrid and digitally intensive workplaces. The study concludes that integrating the metaverse strategically into curricula supports the development of twenty-first-century skills essential for long-term employability.

This Innovate Practice Full Paper presents an innovative idea and method of organized engineering education. With the rapid globalization of China's economy, and especially the emergence of a large number of innovative large-scale enterprises supported by advanced technology, a great number of engineering-proficient talents is urgently needed. However, there is a disparity between the talent cultivation system in China and the societal demand for well-educated specialists. Every year, more than 9 million people enter various colleges and universities in China. Likewise, a large number of students graduate and enter the job market annually. Although China's higher education system has made great progress, the quality of engineering performed by graduates from various types of colleges and universities is low and remains far from meeting society's demands. Through extensive research and analysis, we have come to believe that the main reasons for this phenomenon can be summed up in two points: firstly, the system for teaching engineering needs further improvement. Engineering education is in a very fragmented state and lacks unified standards. Different colleges and universities have different understandings of engineering education, leading to the educational methods they use being inconsistently applied. Secondly, social engineering education resources are not used optimally. With this in mind, we propose an innovative and logical solution to the engineering education problems China faces. The method attaches importance to the teaching of organized courses, emphasizes the practice of organized engineering, and vigorously carries out the construction of a planned practice base. Through more than ten years of exploration and practice, we have made a series of achievements and gained a wealth of experience, opening up a road for China's engineering education. We hope that our research can contribute to the organized and uniform development of engineering education around the world.

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Taking the perspective of new quality productivity, this study explores the promotion effect of the intermingling of intelligent computing and traditional culture on the cultivation of innovative talents, and constructs an evaluation system containing four primary indicators and 14 secondary indicators of educational activities, student practice, collaborative innovation and teaching resources. The cloud integration model is used to deal with the ambiguity and randomness of the complex system, and the network hierarchy analysis method ANP is used to determine the weights of the indicators and reveal the dynamic association of each element. It is found that: the indicator B2 of student practice category has the highest weight of 0.329, in which the number of awards of C5 innovation competition and the number of C4 students' project participation are the core driving factors, with the weights of 0.103 and 0.078, respectively. the cloud integration model verifies the scientificity of the evaluation system. The evaluation value of the traditional culture innovation talent evaluation system constructed in this paper is 0.798, and the integrated cloud model belongs to “very good” grade. However, the mapping intervals of C14 Resource Library Call Frequency and C13 Teacher Integration Background are low, 0.346 and 0.413 respectively, which need to be adjusted and optimized. The innovative talent cultivation program of colleges and universities constructed in this study can make up for the shortcomings in traditional talent cultivation performance evaluation, has certain practicality and effectiveness, and helps to improve the quality of traditional culture innovative talent cultivation.

Generative AI has presented a dual opportunity to increase the efficiency and innovation of the new media advertising education, but the depth of its integration with the goal of talent cultivation has not been fully explored. Based on the theory of "human-machine collaborative creation" and "AI dynamic assessment", this research puts forward a new framework for cultivating talents: "Technology Empowerment — Capability Development — Ethics Shaping." Through the development of a two-loop teaching model, which includes "creative idea-content production-strategy optimization," and the integration of a multidimensional AI assessment system, the experimental results show that the model not only reduces the duration of the project, but also improves the students' technical application skills, cross-disciplinary cooperation, and ethical awareness. The research suggests a deep integration of AI tool chain with talent training goals, using dynamic feedback mechanism and cooperation platform between school and enterprise.

Addressing the challenges of output-based education (OBE) talent cultivation evaluation in traditional universities—specifically, the overemphasis on outcomes at the expense of processes and insufficient technological integration—this paper proposes a dual-dimensional evaluation framework supported by big data and artificial intelligence (AI). Based on 1,000 questionnaires, interviews, and empirical studies conducted across 20 universities nationwide, we designed an indicator system encompassing both teaching and practical processes as well as employment and innovation outcomes. Additionally, we developed a closed-loop mechanism for data collection, analysis, and feedback. The results indicate that this framework enhances evaluation objectivity by 38% and improves graduate job fit by an average of 21%. It offers a technical pathway and valuable university-industry collaboration experience for advancing OBE reform in traditional universities.

With the rapid growth of technologies like the metaverse, AI, and VR, traditional models for training new engineering talent face both challenges and opportunities. Current educational systems struggle with outdated curricula, a disconnect between industry needs and educational content, and limited teaching methods. This research explores how to optimize the talent cultivation system for new engineering disciplines within the metaverse context by integrating technology and education. The study highlights how metaverse technologies can enhance course diversity, practical learning, and student engagement, while also emphasizing the importance of interdisciplinary collaboration and industry synergy. Ultimately, the research proposes an innovative model for talent development, aiming to better align education with rapidly advancing technological needs.

No abstract available

Abstract In order to meet the needs of the digital media industry and the development needs of all sectors of society, this study improves the traditional digital media talent training model. This paper proposes an interdisciplinary talent cultivation model in digital media based on resource recommendation system, researches on recommendation algorithms of learning resources, researches on collaborative filtering algorithms based on user’s similar interest preferences as the goal, and further constructs learner models. Finally, we take the graduate students of digital media art majoring in digital media art in an art college as an empirical study to analyze the effect of the application of learning resource recommendation system in the talent cultivation of digital media art majors. The main conclusions drawn in this paper are that the P-value is greater than 0.05 in the five questions about the learning interest of learning digital media art majoring in digital media art, and the P-value is less than 0.05 in the five questions about the learning interest of learning digital media art, indicating that there is no significant difference between the experimental group and the control group in the learning interest of learning digital media art, and that the experimental group and the control group in the learning interest of learning digital media art majoring in digital media art after the experiments There is a significant difference in the learning interest of digital media art between the experimental group and the control group. The proportion of the experimental class was higher than that of the control class in terms of creative ability and open-mindedness and in terms of the improvement of self-confidence, which were 12 percentage points and 9 percentage points, respectively.

In the era of digital intelligence, the rapid progress of interdisciplinary fields and the continuous expansion of application domains have brought new challenges and high standards to the cultivation of graduate talents in Management Science and Engineering. Exploring effective innovative practical teaching approaches in this field, and strengthening the comprehensive skills of graduate students has become a significant issue in cultivating complex innovative talents. Taking Guangdong University of Finance & Economics as an example, this study analyzes the transformation of graduate education models, constructs a "Dual-Agent, Six-Dimensional, Four-Drive" talent cultivation model, emphasizing the creation of scientific research platforms and the construction of international faculty teams, to cultivate talents that meet the needs of the times.

In the context of globalization and informatization, cultivating the information ability and innovative literacy of university digital talents is of great significance for cultivating talents in supply chain majors. However, some shortcomings remain in the information ability evaluation and innovative literacy cultivation of university talents in the current education system. In this context, this study conducted in-depth research on the information ability evaluation of digital talents in universities and its impact on the cultivation of innovative literacy. The fuzzy comprehensive evaluation method was first adopted to make a quantitative evaluation of the information ability of university digital talents, which aimed to compensate for the shortcomings of existing qualitative evaluation methods. Then, regression models, as well as mediating and moderating effect models, were constructed to deeply analyze the impact of information ability on innovative literacy cultivation, aiming to reveal the complexity of the impact mechanism. This study aimed to provide new perspectives and methods for theory and practice, thereby improving the information ability evaluation system and the innovative literacy cultivation mechanism of digital talents in universities. This study is not only theoretically significant, but it also provides a practical reference for universities in cultivating talents in supply chain major.

Abstract Based on artificial intelligence technology, this paper builds an innovation and entrepreneurship education platform for college students with the goal of improving their innovation and entrepreneurship spirit and ability. For faculty information, competition information, and other aspects related to the collection and analysis of student portraits. The use of a collaborative filtering algorithm allows students to receive personalized test questions while also modeling innovation and entrepreneurship competitions and creating a set of team-matching methods that utilize student portraits. Using hierarchical analysis to construct the evaluation system model of graduate innovation and entrepreneurship education, it can be analyzed that in the student portrait, when the students basically have no attendance, the assessment grade is also 0. Students who have participated in the innovation and entrepreneurship education platform have received salaries of more than 11K in 4 cities, while social workers have only received salaries of more than 11K in 1 city. Basically, more than 70% of those who agree with the learning mode of innovation and entrepreneurship platform and who are very interested in it are more than 70%. Resource allocation (0.3537) has the greatest influence on the cultivation of innovative and entrepreneurial talents, and the importance of constructing innovative and entrepreneurial resource allocation should be highly emphasized.

: High-level talents in the artificial intelligence (AI ) field are an essential scientific and technological strategic resource and a vital force supporting industrial development. From the perspective of high-level flow in the global artificial intelligence field, the paper constructs a conceptual model of the impact mechanism of mobility on scientific research performance. It uses the structural equation model and the partial least square algorithm to estimate model parameters. The study shows that: ① Mobility runs through the entire life cycle of a scientific researcher's career and can affect individual scientific research performance from the two dimensions: human capital and social capital. Still, the types of capital accumulated by mobility are different at different stages. In the education stage, mobility mainly improves the continuous human capital. In contrast, in the work stage, mobility seeks to improve its human capital and build a scientific research collaboration network to enhance the output and scientific influence by enhancing social capital. ② The industry-academia intersectoral mobility optimizes the allocation of scientific and technological human resources among the different sectors, promoting the scientific research output and research influence of scientific researchers. Although industry-academia intersectoral mobility can improve human capital and build a collaboration network, due to the different value systems of different sectors, the adaptation costs of entering a new environment are higher, so the impact on performance is slightly lower than that of non-intersectoral mobility. The conclusions of this study have specific implications for rationally promoting the conscience flow of researchers in emerging fields, improving the scientific research evaluation mechanism in the context of industry-academia integration, and focusing on industry-academia collaborative training of talents in emerging fields.

Priority development of education is the fundamental guarantee for the construction of high-level talent highlands. Technological self-reliance and self-improvement are strategic support for the construction of high-level talent highlands, and talent innovation guidance is the intrinsic driving force for the construction of high-level talent highlands. The construction of a high-level talent highland in the Guangdong Hong Kong Macao Greater Bay Area requires the integration of innovative elements that connect the “education chain, technology chain, and talent chain”. It is necessary to build a three-dimensional integrated spatial architecture of “education technology talent”. We need to build a community that integrates “education highland, technology highland, and talent highland”. Accelerating the construction of high-level talent highlands in the Guangdong Hong Kong Macao Greater Bay Area requires the construction of a highland for scientific and technological innovation talents, a highland for technical and skilled talents, and a highland for scientific and technological finance talents. Based on this, this article selected 399 listed high-tech enterprises in the Greater Bay Area as the research objects, and conducted multiple regression on the relevant data of sample enterprises from the perspective of property rights heterogeneity from 2018 to 2021 to reveal which open innovation model can effectively solve specific situational problems faced by enterprises. The multi role matching mechanism can effectively improve the performance of new product development and its market competitive advantage of enterprises, as well as how the nature of property rights affects the relationship between open innovation strategies and new product development performance of enterprises. The research results show that, firstly, open innovation between enterprises is beneficial for the precise improvement of product development performance by high-level talents in enterprises. Secondly, open innovation in industry, academia, and research is beneficial for improving talent performance in enterprises, but its effect is weaker than that of open innovation between enterprises. Enterprises focus more on the open innovation model between enterprises.

The discipline of information and communication engineering has the characteristics of rapid updating of knowledge system, wide range of scientific research directions, the rapid development of cutting-edge topics and high requirements for innovation ability. It is the forefront of competition in the field of science and technology of major countries in the contemporary world, as well as the strategic, basic and leading discipline supporting economic and social development and ensuring national security. It is also the most impacted discipline and more "neck sticking" technologies as our country changes in the international situation. In this paper, in order to meet the needs of cultivating innovative talents for postgraduates of information and communication engineering, we construct a teaching system including educational concept, curriculum system, teaching mode and teaching materials which are guided by high-level innovation. In recent years, practice has proved that the teaching system can effectively improve the quality of innovative talent training, as well as the innovation ability and achievement level of postgraduates.

Against the strategic backdrop of the nation's vigorous promotion of the "Digital China" initiative and the "East Data, West Computing" project, the digital economy has emerged as the core engine driving Qinghai Province's high-quality development. However, constrained by objective conditions such as geographical location, economic foundation, and educational resources, Qinghai faces severe challenges in its digital economic development process, including insufficient digital talent pool, structural imbalance, and high attrition rates. Therefore, this paper adopts a collaborative education perspective involving administration, industry, academia, and research. It delves into the theoretical logic and intrinsic mechanisms of this approach in empowering digital talent cultivation, arguing that the optimized allocation of systemic elements and deep integration of diverse stakeholders can generate a value-added effect where the whole is greater than the sum of its parts. Based on this, the paper proposes specific implementation pathways: establishing a precise strategy guidance system; innovating talent cultivation models that deeply integrate industrial chains with professional chains; building high-level digital industry-education integration practice bases; establishing a dual-qualified teacher workforce through mutual appointment and shared use; and fostering a full-cycle talent service ecosystem encompassing attraction, cultivation, utilization, and retention. The aim is to overcome Qinghai's digital talent shortage through deep collaboration and mechanism innovation among administration, industry, academia, and research entities, thereby providing robust talent support and intellectual assurance for the sustainable development of the regional digital economy.

In China, efforts to develop new quality productive forces have placed higher and more complex demands on the competencies of audit professionals. Yet university-based audit talent development still tends to have a narrow disciplinary focus, limited training in data- and AI-related skills, and underdeveloped practice environments, making it difficult to effectively meet these emerging requirements. In this context, higher audit education needs to respond to the drive to develop new quality productive forces and to reshape existing audit talent development systems. This paper constructs a three-dimensional mechanism framework of intelligent empowerment, cognitive empowerment, and ecological empowerment, and uses it to clarify the role of artificial intelligence (AI) in reconstructing audit knowledge and skill structures, optimizing learning processes and assessment approaches, and reshaping collaborative education ecosystems. On this basis, it proposes practice pathways for AI-enabled audit talent development in six areas: digital and intelligent curriculum development, strengthened teacher support systems, personalized capability-enhancement schemes, multi-dimensional learning outcome evaluation systems, immersive practice environments, and improved government–industry–university–research collaboration mechanisms. The study aims to provide a reference for universities seeking to advance audit education reform and to develop high-quality audit professionals aligned with the requirements of new quality productive forces.

: This paper investigates the issues and countermeasures of high-quality talent cultivation in western universities under the backdrop of digitization. Firstly, it clarifies the meaning and value of high-quality talent cultivation in western universities in the digital era, emphasizing the importance of modernizing mechanisms. Secondly, it analyzes the significance of mechanism innovation in promoting modern digital education, accelerating the digital transformation of universities, promoting balanced development of higher education, and enhancing education governance and service capabilities. Finally, it proposes countermeasures including establishing a three-dimensional talent cultivation model integrating university-enterprise cooperation deeply, advancing the

Advancements in artificial intelligence (AI) signal a significant shift toward more automated and data-driven workplaces, emphasizing the need for Human Resource Development (HRD) to prepare the workforce with adequate AI competencies for AI-empowered environments. AI policies and initiatives play a crucial role in providing the frameworks and actions that guide HRD efforts. In this study, we analyzed education and workforce policies in national AI strategies (NAISs) from 50 countries to delineate educational policy priorities, strategies, and support resources for developing an AI-competent workforce. Our analysis revealed that only 13 countries demonstrated high-level prioritization with clear objectives and comprehensive measures, primarily developed economies in Europe. We identified six categories of educational and training strategies focused on AI talent preparation and workforce reskilling. Additionally, four types of support resources were highlighted as key investments to enhance the success of these educational and training strategies. The findings have significant implications for HRD practice, particularly in designing and implementing AI-focused, workplace-oriented, and inclusive HRD curricula, programs, and policies that consider contextual and cultural factors. The results suggest the need for further HRD education research to explore emerging theories, AI workforce training models, pedagogical strategies, and the impact of contextual and cultural elements on workforce competency development.

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University-industry cooperative education is an important way to cultivate graduate students' innovative ability and practical ability. However, there are some problems in the traditional joint training model of graduate students, such as low efficiency, conflict of objectives of cooperative subjects, a mismatch between supply and demand of cooperative entities, and so on. The big data technology has brought new opportunities and challenges to the joint training of graduate students by university-industry cooperation institutions. Based on analyzing the connotation and characteristics of the big data era, the paper points out that the arrival of the big data era can improve the information integration efficiency of university-industry cooperation institutions, optimize the traditional joint training model of graduate students, and provide an effective evaluation mechanism of educational quality for university-industry cooperation institutions. At the same time, the paper discusses the difficulties of data collection and disclosure of data privacy faced by university-industry cooperative education in the big data era. The paper also discusses how to deal with the challenges from the perspective of the government, colleges and universities, scientific research institutions and enterprises.

Amid the surge of digital intelligence, persistent gaps between theory and practice and the limited depth of university–industry collaboration have constrained the quality of graduate training in big data and its alignment with labor‑market needs. This paper proposes an industry–education integration model tailored to the discipline, and articulates a theoretical framework for a tripartite collaboration among universities, enterprises, and technology. Grounded in digital‑intelligent technologies, the framework aims to cultivate graduates’ three‑dimensional capability profile-‘theory–technology–scenario’. The study not only offers a reproducible model for big data graduate education, but also positions industry–education integration as a ‘converter’ that smooths the transition from academia to industry, thereby supplying the digital economy with a cohort of professionals who combine academic depth with practical acuity.

Since quantum technologies are quickly advancing to be used in actual applications, there is an increased demand to have graduate programs prepare recent graduates and practicing professionals from science and engineering disciplines to have competencies to enter the nascent quantum workforce. In this presentation, we detail key lessons learned from developing and launching the University of Calgary professional master's program in Quantum Computing, deliberately created to prepare students entering this rapidly evolving domain of employment. The presentation highlights the program's innovative curriculum, featuring hands-on lab experiences with tools like Qiskit, and a stream-based structure encompassing theory, software, hardware, and business. A distinctive strength of the program lies in its partnership with Quantum City-a Calgary-based initiative that connects academia, industry, and government to foster Alberta's quantum ecosystem. This collaboration provides students with valuable exposure to real-world applications and meaningful industry engagement. Educators, program developers, and academic leaders attending the presentation will leave with actionable recommendations and insights to build accessible, industry-focused quantum education programs to support today's quantum economy.

The software engineering graduate apprenticeship program at the School of Computing, University of Glasgow, has a significant emphasis on work-based learning (WBL), with approximately 80% of the students' time over four years spent in the workplace. This work-based aspect of the program plays a more prominent role in the final two years, by which time apprentices are undertaking increasingly larger roles in the workplace. In this report, we present how we addressed the challenge of structuring and assessing WBL in these senior years such that there is a balance between professional competency attainment and ranked academic achievement, while providing a fair and flexible structure. Based on a model of WBL presented by Raelin in 1997, we outline our rationale for dividing the assessments into workplace projects, workplace journal, and a portfolio of artefacts. The projects are categorised into different types both for flexibility and to encourage academia-industry collaboration, the workplace journal encourages higher forms of reflection, while the portfolio assessment encourages and assesses the attainment of professional competencies. We present our experience of implementing this structure, an analysis of student feedback, and the adjustments made in response. With an increasing focus on work-ready skills and competency-based education in software engineering education, we expect the theory-informed structure we developed, and our experience of running and adapting it, to serve as an exemplar for developing a WBL program at research-led institutions.

Purpose: To suggest how business schools can respond when generative AI automates routine, entry-level tasks and erodes early-career opportunities. The paper addresses a focused question: What can a business school do when graduates’ entry-level jobs are replaced or reconfigured by AI?Approach: This is a perspective article that synthesises recent empirical studies, labour-market evidence, and international policy guidance. Drawing on this integrative review, the paper develops a practical institutional blueprint for programme design, governance, and university-industry collaboration.Findings: The existing literature indicates that traditional “first-rung” roles are thinning in AI-exposed occupations while expectations for day-one fluency with AI-augmented workflows rise. To bridge this capability gap, the paper proposes a coordinated blueprint: (1) reframe curricula around human-AI complementarity; (2) redesign assessment to evaluate judgment, verification, and communication; (3) build experiential pipelines that replicate the developmental function of first jobs; (4) co-design early-career roles through university-industry collaboration; (5) invest in student well-being and ethical governance; (6) sustain staff development; and (7) address common concerns (academic integrity, equity of access). Collectively, these actions enable business schools to restore apprenticeship-style learning within and immediately after degree programmes.Originality: The paper links near-term labour-market disruption from generative AI to concrete, institution-level strategies in business education. It offers an actionable, literature-informed blueprint that moves schools beyond placement facilitation to co-creation of AI-era entry pathways, showing how higher education can rebuild the apprenticeship-like learning once provided by traditional entry-level jobs.

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Recent notable efforts to establish new technical standards and best practices for digital imaging, including the Federal Agency Digitization Guidelines Initiative in the United States and the Metamorfoze effort in the Netherlands, present important educational challenges and perhaps one of the biggest opportunities for the imaging science community to increase the level of imaging literacy in the ranks of new and upcoming cultural heritage professionals. This paper establishes the contexts for and presents the preliminary results of an educational exercise on digitization quality carried out in collaboration between academia and industry. A graduate level course introduces students to emerging standards and best practices and reinforces this information with training in the use of the GoldenThread image quality software via a server-based “virtual laboratory” environment. Recognizing that improvements in teaching imaging concepts are also needed, we present examples from an image quality interpretation manual developed to complement classroom discussion and laboratory exercises.

In the rapidly evolving era of the digital economy, there is a noticeable gap between the supply and demand of digital talents. From the perspective of the current path of economic construction in various countries, the importance of digital talents is gradually becoming prominent, and more and more countries are paying attention to the cultivation of digital talents and the reform of higher education systems. The school enterprise education cooperation model, as a new training model in the digital era, has attracted social attention. This paper, taking into account the current state of higher education, highlights the necessity of establishing a holistic model for nurturing talents through university-industry collaboration in future development. Furthermore, it provides a comprehensive understanding and definition of the university-industry collaboration model in the context of the digital economy. Building upon these insights, the concluding remarks emphasize the need to reexamine the model of university-industry collaboration in nurturing talents from a fresh perspective.

University–industry (U–I) collaboration takes on many forms, from research services, teaching and training, to curiosity-led research. In the chemical industries, academic chemists generate new knowledge, address novel problems faced by industry, and train the future workforce in cutting-edge methods. In this study, we examine the dynamic structures of collaborative research contracts and grants between academic and industry partners over a 5-year period within a research-intensive Australian university. We reconstruct internal contract data provided by a university research office as records of its collaborations into a complex relational database that links researchers to research projects. We then structure this complex relational data as a two-mode network of researcher-project collaborations for utilisation with Social Network Analysis (SNA)—a relational methodology ideally suited to relational data. Specifically, we use a stochastic actor-oriented model (SAOM), a statistical network model for longitudinal two-mode network data. Although the dataset is complicated, we manage to replicate it exactly using a very parsimonious and relatable network model. Results indicate that as academics gain experience, they become more involved in direct research contracts with industry, and in research projects more generally. Further, more senior academics are involved in projects involving both industry partners and other academic partners of any level. While more experienced academics are also less likely to repeat collaborations with the same colleagues, there is a more general tendency in these collaborations, regardless of academic seniority or industry engagement, for prior collaborations to predict future collaborations. We discuss implications for industry and academics.

Research and Development becomes one of the main pillars to build knowledge based economy nowadays. In highly competitive market, there is a real need for efficient mechanisms to have a successful technology development model. Open Innovation through University Industry Collaboration (UIC) is one of promising mechanisms to develop new technologies that can seed national knowledge economy. UIC model brings valuable benefits to both academia and industry in order to have efficient technology development processes. In addition, UIC offers university researchers an opportunity to have an exposure to industry and creates training and internship opportunities for university students. Trilateral collaboration model, which adds end-user to UIC arrangement, could bring an additional advantage to align the product development with actual customer needs making introduction of new product more successful. This paper gives an overview about the main aspects of trilateral collaboration and shows a real trilateral collaboration case between academia, technology provider and end-user to develop an actual product that serves end-user needs showing advantages and challenges of proposed model.

ABSTRACT University-industry research collaboration (UIRC) is a major source for research and innovations and economic growth. Despite the extensive evidence on the importance of such collaboration in developed and developing countries, the literature related to strengthen such collaboration along with its innovation performance is still scarce. Scholars believed that the impact of education and training on researchers have a vigorous influence on research and innovations. Moreover, to enhance the competencies of education and training on researchers, it is mandatory to refurbish education and skills system in conjunction with technological infrastructure system along with their reinforcing factors i.e. knowledge sharing and research and development cooperation, respectively. In this paper, we evaluate the influence of education and skills and technological infrastructure system along with their corresponding reinforcing factors in the blended system thinking method to strengthen education and training. Evidence from UIRC in Malaysia provides empirical corroboration that the role of education and skills system and technological infrastructure system along with their reinforcing factors have a positive influence on education and training. Thus, the findings of this research suggest that intensifying the quality of education and skills system and technological infrastructure system with the reinforcing effect can enhance the effectiveness of education and training.

The Institute of Coding (IoC) is a new £40m+ initiative by the UK Government to “transform the digital skills profile of the country”. In the context of widespread national and international educational and economic policy interventions, it responds to the apparently contradictory data that the United Kingdom (UK) has a digital skills shortage across a variety of sectors, yet its higher education system produces computing graduates every year who end up unemployed, or underemployed. The Institute is a large-scale national intervention to address some of the perceived issues with formal educational routes versus industry-focused skills and training, for example: technical skills versus “soft” or “work-ready” skills; industry-readiness versus “deep education”; inclusion and diversity of the current and future technical workforce; and managing expectations for the broad digital, data and computational skills demands of employers across a wide range of economic sectors. Alongside these activities at the higher education-industry interface, we have also seen substantial computer science curriculum reform across the four nations of the UK. In this paper, we outline the background, evidence base and rationale for the IoC (especially within the complex UK policy context); its key themes, current activities and outputs; as well as anticipate its likely impact over the coming years. Furthermore, we reflect on the potential replicability of aspects of the Institute (and related initiatives in the UK) to other nations or regions with similar ambitions to address the “digital skills crisis”.

The UK is not the only country with a serious digital skills crisis, but it is one with a formal Government inquiry (The Shadbolt Report) and response. It also has very detailed tracking of people into, through and out of higher education into employment. The Institute of Coding (https://instituteofcoding.org/) is a new £40m+ initiative by the UK Government to transform the digital skills profile of England. It responds to the apparently contradictory data that the country has a digital skills shortage across a variety of sectors, yet has unemployed computing graduates every year. The Institute is a large-scale national intervention funded by Government, industry and universities to address some of the perceived issues with formal education versus industry skills and training, for example: technical skills versus soft skills, industry-readiness versus "deep education", and managing expectations for the diverse digital, data and computational skills demands of employers across a wide range of economic sectors. Its work ranges from the development of specialist, in-demand digital skills to the provision of work experience, employability skills and ensuring work-readiness of computing graduates, and the provision of digital skills for those from a non-digital background. It is also addressing under-representation and under-achievement by a variety of groups, notably women (only 16% of university students) but also ethnic minorities and other groups.

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With the promotion and popularization of the ERP application, there is a serious problem in the supply and demand of applied talents between information-based enterprises and higher education institutions. University-industry collaboration became the basic method of solving the problem. Based on the present condition and the characteristics of the ERP informationized talents demand, this article researches its university-industry collaboration code, establishes a new innovation system to cultivate the human capacity that the ERP industry needed and the university-industry collaboration integration design of informationized talents training.

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This study examines the influence of technology-enhanced collaborative learning and industry–university collaboration on graduate employability, with learning engagement analyzed as a mediating psychological mechanism. Conducted at Universitas Tomakaka, the research employs a quantitative explanatory design using structured questionnaires and EViews-based modeling to test direct and indirect effects. The findings demonstrate that technology-enhanced collaborative learning significantly contributes to employability by strengthening students’ digital communication, problem-solving abilities, and collaborative competencies. Industry–university collaboration also shows a strong direct effect on employability, reflecting the importance of workplace exposure, authentic project assignments, and industry-level feedback in preparing students for professional roles. Mediation analysis further confirms that learning engagement partially mediates both relationships, revealing that active, emotional, and cognitive involvement enhances the translation of learning experiences into employability outcomes. Engagement emerges as a critical mechanism through which students internalize collaborative learning environments and industry expectations, transforming them into concrete professional skills. These findings highlight that employability development is not merely the result of structural or technological interventions but depends heavily on the psychological processes that shape how students participate in and interpret their learning experiences. This study concludes that integrating digital collaborative learning with structured industry partnerships is essential for producing competent, work-ready graduates.

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Preparing students for advanced technical careers requires more than foundational theory; it demands a shift to genuine research methodology. With the rising need for university-industry partnerships in AI innovation, this case study dissects a successful student-industry AI engineering collaboration project and transforms it into an essential preparation model easily adopted by technology personnel. The student participants include undergraduate and graduate engineering, data science, and computer science students working directly with practicing industry AI engineers. While universities publish most AI research and generate higher-novelty work, industry contributes state-of-the-art models, large datasets, and computational resources. Students gain research readiness, data literacy, and experience with real-world constraints, while industry gains innovative prototypes, extended research capacity, and a prepared talent pipeline. Drawing on qualitative interviews and surveys, the study identifies challenges in such collaborations, proposes a concise three-stage model for effective student-industry AI collaboration, and proposes validation through a successful student-industry project.

This paper focuses on the core contradiction of the global educational informatization industry: the rapid iteration of technology and the shortage of composite talents. According to the Global Educational Technology Talent Development Report 2023, China faces a gap of over 300,000 professionals who are proficient in technology, understand international rules, and are skilled in cross-cultural collaboration. The existing talent training model, which relies solely on universities, suffers from issues such as the disconnection between theory and industry (with less than 20% corporate participation in talent training) and weak international collaboration (with overseas practice coverage below 15%). These shortcomings make it difficult to meet the demands of the “Belt and Road” educational initiatives. Based on stakeholder theory and collaborative governance theory, this paper constructs a quadruple synergy system involving universities, international enterprises, research institutions, and overseas application scenarios. Through literature research, the Delphi method (three rounds of argumentation with 15 experts), and case analysis (three typical cases), this paper clarifies the logic of quadruple synergy and sets three-dimensional capability objectives: technology research and development, cross-cultural project operation, and international rule adaptation. It also designs a full-chain pathway of “curriculum co-construction – overseas practice – achievement transformation”. The results show that this system can increase students’ participation rate in international projects by 40% and improve their cross-cultural operation capability scores by 35%. This study is the first to incorporate overseas application scenarios into the “industry-university-research-application” framework, filling the gap in international talent capability standards for educational informatization and providing references for policy-making, university practices, and corporate initiatives.

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The graduate education system for environmental engineering in the United States, particularly within the STEM fields, has been instrumental in supporting the nation's science and engineering sectors. This study focuses on the organizational strategies that foster the development of senior environmental engineering professionals in the southern United States, with a specific look at the Georgia higher education system. We perform a comparative organizational analysis between two leading institutions: the Georgia Institute of Technology and the University of Georgia. This research examines organizational culture, structure, and arrangements, aiming to uncover the dynamics that drive innovation and the cultivation of expertise within this field. The study reveals both institutions maintain stringent admission and graduation policies to ensure graduate quality, with Georgia Tech integrating its programs with interdisciplinary studies and UGA offering a deep dive into civil engineering knowledge.

With the rapid advancement of artificial intelligence (AI) technologies, there is a growing demand in graduate education for highly skilled, application-oriented professionals equipped with both technical expertise and strong ethical values. However, traditional AI curricula often lack effective integration of practical training and values-based education.This study describes the design and implementation of an integrated curriculum for AI master's students, combining systematic industry needs assessment, curricular reform, and pedagogical innovation. The program emphasizes the cultivation of practical skills alongside values-oriented education—specifically professional responsibility, ethical awareness, and societal engagement—through course planning, case-based learning, project-driven instruction, and interdisciplinary collaboration. Multiple evaluation tools were employed, including student and faculty surveys, focus group interviews, and analysis of performance metrics such as project outcomes and academic competition results.Evaluation results demonstrate that the integrated curriculum significantly enhances students' initiative, critical thinking ability, and sense of social responsibility. Students not only achieved higher technical competence but also exhibited improved holistic qualities, as reflected in project achievements and external competition performance.The core innovation of this model lies in the seamless integration of ethical and societal considerations into technical training. Although the case study is limited to a single institution, the model provides valuable insights for AI education reform and high-quality talent cultivation. These findings establish a theoretical foundation for the adaptation and application of such integrative approaches across diverse educational settings.

Abstrac-This paper aims to establish a comprehensive interdisciplinary graduate training model and improve the model in three stages: enrollment, training, and degree granting. The Analytic Hierarchy Process is used to establish a comprehensive evaluation index system for interdisciplinary graduate innovation and practical capabilities, and a specific quantitative evaluation method is proposed. Taking the School of Civil Engineering at Shenyang Jianzhu University as an example, a questionnaire survey is conducted to analyze the reasons for students' choice of interdisciplinary studies, and the quantitative evaluation system is applied to evaluate the learning, innovation, and practical capabilities of interdisciplinary graduate students.

With the rapid advancement of technology, artificial intelligence (AI) has become an indispensable part of various industries. To keep pace with this trend, higher education institutions must cultivate AI professionals with interdisciplinary thinking and research capabilities. This paper, based on the “Double First-Class” program construction in Chinese universities, explores an AI course education model within the context of interdisciplinary integration. By analyzing existing issues in current AI course education, we propose a research-oriented perspective to build a comprehensive interdisciplinary knowledge integration system and establish a collaborative platform that bridges both in-class and external connections. This approach is intended to enhance students’ innovative and problem-solving abilities. By combining theory with practice, this paper advocates for deep reforms in AI courses for graduate students, aiming to improve their overall quality and cultivate “passionate, technology-driven, high-quality” talent for society.

 This article examines the challenges facing computer-related professional graduate programs amid rapid technological evolution and workforce demand shifts. By adopting a policy-informed and socially inclusive approach, the study develops a curriculum system that not only addresses industrial needs—such as algorithm design and systems development—but also fosters interdisciplinary innovation and employability. The proposed modular framework, shaped by enterprise engagement and dynamic adaptability, enhances students’ readiness for the labor market while contributing to broader goals of social equity and higher education modernization. The findings offer a practical model for bridging the gap between academic training and real-world socio-industrial challenges.

Incorporating creativity, innovation, and entrepreneurship education in the curriculum of university level programs of health sciences will enable the graduates to tackle complex healthcare and biomedical problems. However, the traditional educational models are insufficient to address these needs. For effective problem solving and innovation, educational systems with integrative and interdisciplinary components are necessary. This paper introduces an interdisciplinary education module tailored for graduate students of health sciences students. The module combines four disciplines - physiology, electronics, programming, and mathematics, to impart education through a problem-based, hands-on approach. The interdisciplinary module will enable students from diverse academic backgrounds to collaboratively work to design and develop an electrocardiogram (ECG) signal acquisition system and interpret ECG data. We have also provided methods to evaluate the learning outcomes by assessing creativity, innovation, problem-solving, and entrepreneurship through methods such as creating open-ended designs, real-world problem-solving challenges, entrepreneurship pitches, collaborative peer reviews, and writing reflective reports. This integrated hands-on learning framework inspires interdisciplinary collaboration and entrepreneurial thinking among graduate level students of health sciences to develop practical and novel solutions to healthcare challenges. By imparting foundational theoretical knowledge along with the necessary skills and competencies, this module will contribute to the advancement of education and entrepreneurship in health sciences.

The incorporation of generative artificial intelligence (AI) in doctoral supervision signifies a transformative evolution in higher education. This has been significant, particularly within intricate and emotionally complex research such as sexuality studies. This reflective, collaborative autoethnographic study investigates the experiences of a doctoral student and her supervisor. They explored AI generative tools to enhance research processes, quality of supervision and intellectual inquiry. Anchored in Kolb’s Experiential Learning Theory and reconceptualised through an augmented experiential learning framework, the study elucidates how AI tools like ChatGPT encourage critical thinking. These tools were also used to foster methodological innovation and facilitate ethical reflexivity. Through iterative engagements, AI supported the formulation of sophisticated research questions and bolstered academic writing. It also aided emotional resilience in traversing heteronormative and interdisciplinary landscapes. The study highlights the evolving role of supervisors, not as gatekeepers but as collaborators in AI-informed learning. Significant emphasis was placed on prompt engineering, scholarly scrutiny and academic integrity. Ethical guidelines and rigorous documentation practices ensured a responsible AI application without sacrificing originality. Contribution: The findings reveal that AI-augmented supervision promotes deeper theoretical engagement and enhances self-directed learning. It also introduces new pedagogical possibilities for complex research endeavours. Nonetheless, the study also underscores the challenges of bias, overreliance and contextual insensitivity inherent in AI outputs. By suggesting actionable strategies for ethical integration, this paper contributes to emerging global discussions on AI in higher education. It presents a framework for inclusive, transformative and contextually aware supervision practices.

There is a growing emphasis to educate STEM students about accessibility, so that they can become accessibility advocates. We introduce a community-based accessibility training program that brings together graduate students in STEM and related fields, called the Research and Education in Accessibility, Design, and Innovation (READi). Going beyond academic degree training, this program includes five training components: (1) a graduate course on accessibility and inclusive design, (2) an Action Team Project (ATP), (3) a Retreat, (4) Workshops, and (5) a Symposium. As our initial program assessment, we analyzed 22 students’ written program reflection and found three themes that highlight what students learned about accessibility and professional skills (Theme 1: Learning Outcomes), what students planned on doing after the training (Theme 2: Future Endeavors), and how students want the program to improve (Theme 3: Program Improvement). We advance accessibility education by introducing an innovative training that embraces collaboration among local community, faculty, and multidisciplinary cohorts of graduate students.

: This study takes the national first-class logistics management program at Nanjing University of Finance and Economics as its research object, adopts industry–education integration as its core concept, and conducts collaborative research with JD Logistics. This study focuses on the training of digital talent in the logistics management major, clarifying three key objectives: (1) establishing a scientific education system that integrates industry and education, (2) enhancing graduate competitiveness to support industrial transformation, and (3) developing distinctive program features to provide a replicable model. It also explores specific training pathways and models across five dimensions: formulation of training objectives, optimization of the curriculum system, innovation in teaching methodologies, co-construction of experimental and practical training systems, and refinement of quality assessment mechanisms. The ultimate aim is to cultivate high-quality, application-oriented, and interdisciplinary talent capable of meeting the digital transformation needs of the logistics industry, thereby offering a feasible solution for local universities seeking to enhance digital talent development in logistics management.

Collaborative learning in higher education, such as the emerging makerspaces, has contributed to research on innovation and participant expertise. However, there is little research on knowledge management in makerspaces or how learners transfer their individual tacit knowledge in the collaborative space. In addition, the functionality of maker education to promote individualized and personalized learning still needs to be explored. This study is based on Chinese STEM graduate students’ experiences with project-based learning in a Maker Education environment to test how AIGC tools help to acquire and transfer students’ individual tacit knowledge. The sample was formed from 266 MPhil students taking the "Design Thinking and Effective Academic Communication" course in their first academic year at the world’s first interdisciplinary university in southern China. The AIGC teaching intervention was based on the four-phase knowledge management model. In a cycle of socialization-externalization-combination-internalization,learners first explore expertise in a research area in project teams and formulate their research interest. Then, storytelling, analogy, and metaphor data were first used to assess students’ abilities to communicate their research topic in text and images on an A4 paper. The subsequent AIGC-supported instructional intervention consisted of 20-minute instructional Sprints with two instructors from Computational Media Arts and Academic Communication over two consecutive weeks. The students’ work shows that combining AIGC tools allows for a higher quality of visualization of tacit knowledge in the combination phase -.

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This poster aims at providing an interdisciplinary course (Medical Mechatronics and Control/ ME5053 in National Taiwan University/ Senior Undergraduate/ Graduate Students) designed to encourage the mechanical engineering (ME) students to learn about machine learning (ML) through project-based learning (PBL). Our syllabus design involves the electromyography (EMG) data collection, datasets and ML, and robotic manipulator control through recognizing EMG patterns via ML. Thus, students in ME become more attracted to learning computer science ML techniques through participation in robot-related courses via recognizing and classifying real EMG data.

In the age of globalization, economic growth and the welfare of nations decisively depend on basic innovations. Therefore, education and knowledge is an important advantage of competition in highly developed countries with high standards of salaries, but raw material shortage. In the twenty-first century, innovations will arise from problem-oriented research, crossing over traditional faculties and disciplines. Therefore, we need platforms of interdisciplinary dialogue to choose transdisciplinary problems (e.g., environment, energy, information, health, welfare) and to cluster new portfolios of technologies. The clusters of research during the excellence initiative at German universities are examples of converging sciences. The integration of natural and engineering sciences as well as medicine can only be realized if the research training programs (e.g., graduate schools) generate a considerable added value in terms of multidisciplinary experience, international networking, scientific and entrepreneurial know-how, and personality development. The Carl von Linde-Academy is presented as an example of an interdisciplinary center of research and teaching at the Technical University of Munich.RésuméA l’époque de la globalisation, la croissance économique et le bien-être des nations dépendent de manière décisive de leur potentiel d’innovation. L’éducation, la connaissance et les capacités sont des avantages compétitifs décisifs dans des sociétés hautement développées ayant des salaires élevés mais peu de ressources naturelles. Au 21ième siècle des innovations naissent de plus en plus de la recherche axée sur des problèmes, au-delà des frontières traditionnelles des matières et des facultés universitaires. Nous avons de ce fait besoin de plateformes interdisciplinaires pour pouvoir choisir des problèmes transdisciplinaires (par exemple l’environnement, le développement durable, l’énergie, l’information, la santé, l’aide sociale) et les développer dans des portfolios de technologies appropriés. Les groupes de recherche sur les initiatives d’excellence au sein des universités allemandes (Forschungscluster der Exzellenzinitiative an deutschen Universitäten) donnent des exemples de convergences de technologie et de science. L’intégration des sciences naturelles et d’ingénierie ainsi que de la médecine ne peut être réalisée que si les programmes de formation des jeunes scientifiques (par exemple, des écoles d’études avancées) réunissent des investissements considérables supplémentaires dans des expériences multidisciplinaires, des réseaux internationaux, le développement des connaissances et de la personnalité de l’entrepreneuriat. L’Académie Carl von Linde à l’Université Technique de Munich est un exemple d’un centre de recherche et de formation interdisciplinaire qui s’occupe de l’intégration de ces investissements.ZusammenfassungIm Zeitalter der Globalisierung hängen ökonomisches Wachstum und die Wohlfahrt der Nationen entscheidend von ihrem Innovationspotential ab. Bildung, Wissen und Können sind entscheidende Wettbewerbsvorteile in hoch entwickelten Gesellschaften mit hohen Lohnkosten, aber geringen natürlichen Ressourcen. Im 21. Jahrhundert entstehen Innovationen zunehmend in problemorientierter Forschung, jenseits der traditionellen Grenzen von Fächern und Fakultäten. Daher benötigen wir interdisziplinäre Plattformen, um transdisziplinäre Probleme (z.B. Umwelt, Nachhaltigkeit, Energie, Information, Gesundheit, Wohlfahrt) auszuwählen und in passenden Portfolios von Technologien weiterzuentwickeln. Die Forschungscluster der Exzellenzinitiative an deutschen Universitäten liefern Beispiele für konvergierende Wissenschaft und Technologie. Die Integration von Natur- und Ingenieurwissenschaften ebenso wie der Medizin kann nur realisiert werden, wenn die Trainingsprogramme des wissenschaftlichen Nachwuchses (z.B. Graduiertenschulen) beträchtliche zusätzliche Investitionen für multidisziplinäre Erfahrung, internationale Netzwerke, unternehmerisches Know-how und Persönlichkeitsentwicklung aufbringen. Die Carl von Linde-Akademie wird als Beispiel eines interdisziplinären Zentrums der Forschung und Lehre an der Technischen Universität München vorgestellt, das sich um die Integration dieser Investitionen kümmert.

 In the digital age, artificial intelligence has deeply integrated into the governance of graduate education, providing new impetus for optimizing training models. However, in practice, challenges such as data governance barriers, technical ethical risks, and insufficient adaptability of stakeholders are encountered. For instance, data silos hinder precise training, and technological dependence undermines educational value. This study, grounded in constructivist learning theory, technology acceptance model, collaborative governance theory, and risk society theory, constructs an analytical framework and proposes innovative pathways: driving scenario innovation through technology integration, building adaptive platforms and data middleware; reconstructing a multi-stakeholder collaborative governance model to strengthen digital literacy and ethical norms; creating a data-driven governance loop. Future efforts should balance technological empowerment with humanistic care, promoting the transition of governance towards "intelligent coexistence," thereby facilitating high-quality educational development.

As educators, we are witnessing and experiencing the rapid proliferation and development of generative artificial intelligence (GenAI) tools, which are raising urgent questions about authorship, ethics, and instruction in higher education (cf. Eaton, 2023). Amid uncertainty and reactive policy responses, we designed a graduate seminar titled “Generative AI—Emerging Implications for Teaching, Learning, Language, and Research: ChatGPT,” offered in Spring 2024 at the University of Rochester. We aimed to immerse students in GenAI environments and offer opportunities to explore both the “what” and the “how” of generative AI, including tools such as ChatGPT and their implications for teaching, learning, language, and research across K–12 and higher education contexts. Following extensive discussions with department leadership, faculty members, and prospective graduate students, we developed this course between February 2023 and January 2024 and implemented it with students during the Spring 2024 semester.

With the continuous growth in the scale of postgraduate admissions, monitoring the quality of postgraduate education has become an urgent issue that needs to be addressed. To this end, this paper establishes an information platform for monitoring the quality of postgraduate education from three aspects: structural design, functional design, and permission design. Based on the data analysis of this platform, data mining methods are used to study the learning behaviour and learning effectiveness of postgraduates. Using an improved K-means algorithm, graduate students are categorised into four types, with knowledge-exploration-oriented and marginally passive-oriented types being the most common, accounting for 37.99% and 27.60%, respectively. Multivariate regression analysis is then used to construct a multiple linear regression equation linking graduate students' learning outcomes to their learning behaviour characteristics. The regression coefficients for the number of knowledge tests and the module completion rate are 0.259 and 0.217, respectively, making them the primary factors influencing graduate students' academic performance. Utilising big data to analyse and predict graduate students' learning behaviours and learning outcomes facilitates monitoring the quality of graduate education and comprehensively improving the management level of graduate education

The coordinated development of postgraduate education between the Guangdong-Hong Kong-Macao Greater Bay Area and ASEAN is an important practice serving the national strategy and promoting the modernization of Chinese education. Both sides have formed a "competitive and complementary" pattern in key areas such as artificial intelligence, tropical agriculture, and traditional Chinese medicine through joint laboratory construction and innovative joint training mechanisms, such as the Digital Silk Road Laboratory of South China University of Technology and Universiti Teknologi Malaysia, and the "Dual Campus + Dual Mentor" model of Sun Yat-sen University and the National University of Singapore. Institutional innovation has broken through barriers such as credit recognition and joint degrees, and a "three-in-one" guarantee system has been established. In terms of cultural exchange, mutual learning is promoted through heritage revitalization and curriculum integration to enhance people-to-people ties. Guangdong Province supports the deepening of cooperation through special funds and risk prevention and control, providing a "Chinese solution" for the internationalization of education in developing countries and contributing to the building of a community with a shared future for mankind.

The current challenges at the forefront of data-enabled science and engineering require interdisciplinary solutions. Yet most traditional doctoral programs are not structured to support successful interdisciplinary research. Here we describe the design of and students’ experiences in the COMBINE (Computation and Mathematics for Biological Networks) interdisciplinary graduate program at the University of Maryland. COMBINE focuses on the development and application of network science methods to biological systems for students from three primary domains: life sciences, computational/engineering sciences, and mathematical/physical sciences. The program integrates three established models (T-shaped, pi-shaped and shield-shaped) for interdisciplinary training. The program components largely fall into three categories: (1) core coursework that provides content expertise, communication, and technical skills, (2) discipline-bridging elective courses in the two COMBINE domains that complement the student’s home domain, (3) broadening activities such as workshops, symposiums, and formal peer-mentoring groups. Beyond these components, the program builds community through both formal and informal networking and social events. In addition to the interactions with other program participants, students engage with faculty in several ways beyond the conventional adviser framework, such as the requirement to select a second out-of-field advisor, listening to guest speakers, and networking with faculty through workshops. We collected data through post-program surveys, interviews and focus groups with students, alumni and faculty advisors. Overall, COMBINE students and alumni reported feeling that the program components supported their growth in the three program objectives of Network Science & Interdisciplinarity, Communication, and Career Preparation, but also recommended ways to improve the program. The value of the program can be seen not only through the student reports, but also through the students’ research products in network science which include multiple publications and presentations. We believe that COMBINE offers an effective model for integrated interdisciplinary training that can be readily applied in other fields.

: With the rapid development of artificial intelligence (AI) technology, AI-enabled blended teaching models have gradually been introduced into higher medical education. By deeply integrating online and offline learning and utilizing intelligent teaching methods, this model provides a new pathway for improving education quality and enhancing students' overall literacy. This approach not only demonstrates advantages in teaching resource allocation and personalized learning support, but also offers new support for cultivating the research thinking and practical skills of public health graduate students. In this context, this paper focuses on public health graduate students, reviewing the current status and issues in the cultivation of research innovation and practical abilities. It explores the mechanism of AI-enabled blended teaching and aims to provide theoretical references and practical pathways for improving the quality of public health graduate education under the background of the new medical disciplines

Digital transformation profoundly impacts higher education through increased quality and integration between disciplines, but its its specific mechanisms, particularly the role of interdisciplinary integration in mediating digital innovation and educational quality, remain underexplored. In developing a theoretical model for explaining how digital innovation raises educational quality, this study employs a mixed-methods approach combining literature review, survey, and in-depth interviews with university students and instructors. It concludes that digital innovation maximizes resource utilization, enhances instructional efficiency, promotes equitable knowledge dissemination, and strengthens innovation capacities. Integration between disciplines strengthens the impact of digital innovation, generating a positive feedback loop that continues to enhance educational quality. This work enriches theoretical and practical insights into digital education and integration between disciplines and can serve as a guideline for universities to promote digital and integration instruction. It can also inform policymakers in supporting educational digital transformation. There is a potential for future studies to expand data collection in universities worldwide and explore new technology such as AI, big data, and blockchain in driving smarter and fairer higher education. These findings offer actionable strategies for institutions to bridge resource gaps and foster equitable digital transformation.

The rapid development of artificial intelligence is driving the transformation of higher education paradigms toward intelligence, integration, and innovation. As a core indicator for cultivating interdisciplinary talents, interdisciplinary innovation competence requires the establishment of an educational model that aligns with the learning characteristics and competency demands of the AI era. Based on innovation competence development theory, interdisciplinary integration theory, and educational digital transformation theory, this study proposes a pathway mechanism for AI-empowered interdisciplinary innovation competence cultivation in higher education. Accordingly, an AI enabled interdisciplinary innovation competence cultivation model with "discipline-oriented, innovation practice, and outcome evaluation" is constructed. The results indicate that this model enables AI to significantly enhance students' interdisciplinary design thinking, complex problem-solving ability, and quality of innovative expression, while improving learning initiative and collaboration depth, boosting teaching efficiency, and facilitating the formation of a multi-agent collaborative teaching and learning mechanism among humans and machines. The research provides theoretical support and practical reference for the reconstruction of innovation-oriented interdisciplinary talent cultivation models and intelligent teaching reform in higher education.

With the widespread application of big data, artificial intelligence, and machine learning technologies in the medical field, a new paradigm of data-intensive clinical research is emerging as a key force driving medical advancement. This new paradigm presents unprecedented challenges for graduate education in clinical professions, encompassing multidisciplinary integration needs, high-quality faculty shortages, learning method transformations, assessment system updates, and ethical concerns. Future healthcare professionals will need not only to possess traditional medical knowledge and clinical skills, but also to master interdisciplinary skills such as data analysis, programming, and statistics. In response, this paper proposes a series of countermeasures, including curriculum reconstruction, faculty development, developing and sharing resources, updating the evaluation and assessment system, and strengthening ethics education. These initiatives aim to help clinical graduate education better adapt to this new paradigm, ultimately cultivating interdisciplinary talents in medical-computer integration.

This paper proposes to carry out the quality evaluation of graduate education based on big data technology and construct the quality evaluation index system of graduate education based on big data. It also proposes to use modern information technology to continuously collect and deeply analyze the relevant data in the graduate education system, intuitively present the state of graduate education, and provide objective basis for multi subject value judgment and scientific decision-making.

A quality evaluation system of full-time profession degree graduate education is proposed on the basis of CDIO (Conceive, Design, Implementing & Operate) engineering education and control theory in the paper. With the characteristic analysis of full-time profession degree graduate education, the cultivating core process of full-time profession degree graduate education is deduced to provide theoretical support for the training structure of full-time profession degree graduate education based on CDIO.

Graduate education quality evaluation is an important part of higher education evaluation. This paper designs an evaluation system that can fully reflect the quality of graduate education, and then combination weighting method and comprehensive fuzzy model were used to verify it. Combining subjective analytic hierarchy process with objective entropy, 10 indexes are weighted and a reasonable weight coefficient vector is obtained. Taking Tianjin University as an example, the comprehensive ranking of postgraduate education quality in 20 colleges of Tianjin University is calculated by using the comprehensive fuzzy evaluation model. The study found that the matriculate quality, scientific research environment, graduate research level and employment ability are the main factors affecting the evaluation of graduate education quality whose contribution rate is as high as 85% and the quality of postgraduate education in science and engineering colleges is significantly higher than that in humanities colleges.

: In the era of strengthening the country through education, the cultivation of high-level innovative talents is of paramount importance. Graduate education, as a crucial pathway for nurturing high-quality talents, has garnered significant attention regarding its quality. Conducting research on its quality monitoring system is an inevitable requirement to meet the needs of high-quality development and enhance national competitiveness. This study establishes a scientific and reasonable quality monitoring index system, objectively and fairly determines the weights of the indices through algorithms, and elaborates on standardized and feasible quality monitoring standards and procedures. In particular, the combined application of the Analytic Hierarchy Process (AHP) and Fuzzy Comprehensive Evaluation Method further enhances the reliability, accuracy, and objectivity of the comprehensive evaluation within the quality monitoring system for master's degree programs in electronic information engineering.

Addressing the long‐standing issue that “existing indicators are outdated and misaligned with social demand” in evaluating talent cultivation quality through industry–education integration, this study develops an indicator system within the CIPP (Content, Input, Process, Product) framework. The system includes 4 primary and 11 secondary indicators. It is operationalized using a hybrid entropy‐like weight cloud‐model method, which transforms qualitative indicators into normalized cloud drops for quantitative analysis. Taking a petroleum university as an example, we illustrate the complete workflow of indicator deployment and model application and compare the proposed method with the conventional analytic hierarchy process (AHP). Results show that: (1) the indicator system closely reflects current social expectations for graduate competence and (2) the entropy‐like weight cloud‐model approach offers stronger practicality and operability. This study provides higher education institutions with a replicable indicator system and an extensible evaluation protocol for self‐assessment and continuous quality improvement of industry–education integrated talent cultivation.

With the increase in the total number of postgraduates, improving the quality of postgraduates has become the main task of the current higher education reform. Major universities and related departments have begun to attach importance to the construction of interdisciplinary subjects. Promoting vigorously the cultivation of interdisciplinary talents has become a new driving force for postgraduate education reform. This article first establishes corresponding evaluation indicators and proposes the APCE indicator system from five aspects: curriculum, teaching resources, research results, graduate students' ability and mentor team. APCE first adopts the analytic hierarchy process (AHP) to calculate the weight value of each evaluation index system. Then use the two-level fuzzy comprehensive operation to obtain the final evaluation grade, according to the principle of maximum membership degree of fuzzy comprehensive evaluation. The empirical test on the cross-disciplinary "Cultural Resources and Cultural Industry" conducted by the National Cultural Industry Research Center of Central China Normal University shows that the APCE indicator effectively analyzes data from both quantitative and qualitative perspectives, thereby providing a valuable evaluation for each cross-disciplinary reference.

Graduate students are an important engine of national and regional development and the dominant force of contemporary scientific and technological innovation. It has become the historical mission of high‐level research universities to provide strong talent support for the construction of innovative powerful countries with human resources. This investigation performed a systematic and comparative analysis of international postgraduate training models. The whole process of multidimensional training countermeasures and suggestions were proposed. Those specifically included updating the training concept, optimizing the curriculum structure, innovating teaching methods, strengthening practical training, strengthening the responsibility of the tutor, and expanding the international vision. After investigation and analysis, this research pointed out that the conditions of establishing and perfecting the examination system, evaluation mechanism, and incentive measures related to postgraduate education were guaranteed. Multiple measures to improve the quality of graduate students have certain reference significance for the current graduate education.

Personnel training is the basic function of colleges and universities. Postgraduate education is also an important part of personnel training, and is an important task of colleges and universities. In view of the current situation and shortcomings of the quality evaluation of professional postgraduate training in China, this paper puts forward the evaluation model of graduate training quality based on analytic hierarchy process and utility theory. By constructing the graduate training quality evaluation system, using the analytic hierarchy process to determine the weight of the index, the utility theory is used to calculate the utility value. The training mode and quality of the disabled graduate students.

The quality of graduate students is one of the most important aspects that affect the quality of graduate education. This research builds a system of quality evaluation for graduate students by using Pearson Correlation Coefficient. The indicators are empowered by basing on the entropy weight method, and the candidates are ranked by according to the TOPSIS and RSR model. The difference between the two models is not noticeable, and the sorting outcomes are superior, by comparing the sorting results. We apply these models to study the quality of graduate students in Changchun University, and we obtain the conclusion that the quality of students in literature, law and economics are the best among all admissions majors.

With the advancement of AIGC and related technologies, medical education is undergoing a profound digital transformation. However, medical graduate supervisors face multiple challenges in this process, including insufficient technical proficiency, low utilization of teaching resources, outdated pedagogical models, and an underdeveloped evaluation system. To address these issues, this study proposes a multidimensional framework for enhancing supervisors’ capabilities. Key strategies include strengthening technical training, establishing a mechanism for sharing high-quality resources, encouraging innovation in teaching methods, and implementing a dynamic evaluation system. These measures aim to improve the effectiveness of digital teaching and lay a solid foundation for cultivating healthcare professionals who meet the demands of the new era.

The study aimed to examine the impact of a gamified distance learning system on enhancing digital creativity in distance education. The research involved 36 graduate students from Sukhothai Thammathirat Open University enrolled in the Public Administration Innovation course. Over a six-week experimental period, the learning system incorporated gamification elements, including missions, challenges, and collaborative tasks, to promote engagement and skill development. Data collection tools comprised 1) a digital creativity assessment form, 2) an evaluation rubric for learners’ digital innovation projects, and 3) a satisfaction survey regarding the gamified learning system. The data were analyzed using t-test dependent statistics to compare digital creativity scores before and after the intervention, rubric criteria for project quality evaluation, and descriptive statistics for satisfaction levels. Results showed a significant increase in digital creativity, with post-test scores (M = 4.49, SD = 0.21) significantly higher than pre-test scores (M = 3.19, SD = 0.19) at the 0.05 significance level. Rubric-based evaluations indicated that learners’ digital innovation projects were rated between good and very good. Additionally, learners expressed high satisfaction with the gamified system, with an average score of 4.35 (SD = 0.69). This study provides evidence of the effectiveness of gamification in fostering digital creativity, addressing a notable gap in traditional educational methods. The integration of game-based elements within distance education supports increased interaction, motivation, and collaboration among learners. The findings underscore the potential of gamified learning systems as a strategic tool for enhancing essential digital skills and creativity in higher education. The study highlights practical implications for curriculum design and offers innovative approaches to address the challenges of distance learning in the digital age. Further exploration is recommended to optimize gamification designs for diverse educational contexts and learner needs.

This research highlights the creation and assessment of an AI-powered system for thesis advisor consultations, aiming to enhance the academic advisory process for graduate students. The system delivers efficient, accessible, customized support in essential areas such as research design, literature review, research tool development, and statistical analysis. Evaluation results revealed that the system achieved a “Very Good” rating across key metrics, including functionality, accuracy, usability, performance, and security, with an overall average score of 4.76. Quantitative assessments of thesis progress among 42 graduate students demonstrated significant improvements, particularly in research tool development and quality assessment, with 88–95% achieving “Good” progress. Qualitative feedback highlighted the system’s strengths in clarifying research objectives, guiding hypothesis formulation, and facilitating data analysis. Furthermore, a strong positive correlation (r = 0.608, p < 0.001) between research knowledge and thesis progress underscores the system’s effectiveness in fostering academic growth. While no significant differences were observed in research knowledge among students with varying experience levels, the system showcased its ability to provide equitable support. These findings highlight the transformative potential of AI in academic advisory processes, offering reliable and accessible consultation services. Future development efforts will aim to enhance the system’s capabilities in statistical analysis to meet advanced research needs better.

: Emphasizing the development and application of big educational data technology in the field of postgraduate education and teaching is an effective way to achieve the goal of modernization of postgraduate education in my country. This paper aims at the reform of graduate course education and teaching, from the aspects of teaching concepts, teaching organization and guidance, teaching methods, teaching management and evaluation, etc., focusing on promoting the reform of postgraduate training mechanism, innovation of training mode, and construction of quality assurance system is the key to comprehensively deepening reform, emphasizing coordination and overall advancement are important considerations for comprehensively deepening reforms, and improving the construction of institutional systems is the fundamental guarantee for comprehensively deepening reforms.

Based on the analysis of the current training mode and development status of master's degree in electronic information, this article focuses on how to build a training mode for master's degree personnel in electronic information with a deep integration of "production, education and research", aiming at the problems on campus, such as lack of mentor project experience, lack of guidance experience for enterprise mentors, lack of system support for practice base construction and imperfect evaluation system. In this paper, a talent training mode which is suitable for the training objectives of electronic information major graduate students in local universities has been established from the aspects of building a team of double tutors, perfecting the management mechanism of practice base and establishing the quality supervision system.

No abstract available

Abstract In the context of fierce competition for talents in colleges and universities, cultivating and introducing outstanding young talents are crucial to the construction and sustainable development of talents in colleges and universities. The article utilizes the IPO model to construct an evaluation system for the quality of cultivation of top talents and the implementation effect of talent introduction policy in colleges and universities. The study takes eight colleges and universities in the university city of Province P as examples, uses the EWM method to determine the weights of the indicators, and carries out a comprehensive evaluation through the improved TOPSIS method and the Data Envelopment Analysis Model (DEA). The results show that in talent cultivation, the weight of the result quality is 18.09% higher than the essential quality. In contrast, in implementing the talent introduction policy, the maximum difference of each weight value is only 1.24%.The effective decision-making unit of the DEA reaches the optimal value of 1.00 in the comprehensive technical efficiency, pure technical efficiency and scale efficiency. The study reveals the shortcomings of implementing talent cultivation and introduction policy in universities. It guides the universities to improve the mechanism of cultivation and introduction of top talents, to enhance the core competitiveness.

Higher education has entered a new era of high-quality development in China. It integrates into the new social development pattern in all aspects, and gradually strengthens the structural reform of talent training to meet the country's demand for innovative top-notch talents. Practical innovation ability is the core content of higher education personnel training, which is directly related to the implementation of the strategy of strengthening the country through talents. This paper explains the connotation and necessity of the practical innovation ability of top-notch talents from the perspective of practical needs and the law of talent development, analyzes the important role of collaborative education theory on the cultivation of practical innovation ability, and discusses the collaborative education mechanism of practical innovation ability in an industry-specific universities. Finally, the paper gives some thoughts on improving the cultivation of top-notch talents' practical innovation ability in universities with industry characteristics.

On the basis of combing the research results of traditional mode and " AI +" enabling education, this paper points out that there are some problems such as the lack of students' ability to innovate and apply, the loose combination of data modeling and actual decision-making, and the limited intelligent penetration of the curriculum system. Furthermore, this paper proposes a "four-stage and three-step" progressive training system of "knowledge-data and skills-intelligent and application-innovation ability", explores the dual mode of "AI-assisted competition-Practice Courses" and "AI-driven projects-school enterprise alliance", and promotes the collaborative improvement of students' professional cognition, interdisciplinary integration and innovation and entrepreneurship ability. This model can effectively break the limitations of the traditional application-oriented talent training path, and provide experience for the economic statistics major to explore the new paradigm of "AI-empowerment-innovation and entrepreneurship driven" top-notch talent training.

The cultivation of top talents has always been valued by many countries around the world. China has also elevated it to the level of a major national strategic deployment. The cultivation of top talents in the field of cyberspace security must reflect the characteristics of putting the country first, being innovative and enterprising, and winning by surprise. In view of the urgent problems existing in the training of top talents in cyberspace security, such as imperfect selection mechanisms and insufficient innovative talent training methods, this article explores the talent selection

The National Science Foundation has promoted the development of education in the U.S., and its establishment reflects the trend of education development. This study collects the data on 2360 NSF educational projects over the last three years to answer two research questions: What are the major research topics of NSF educational projects? What are the key projects doing? Through Latent Dirichlet allocation topic modelling, content analysis is carried out on the titles and abstracts of the 2360 projects, and eight topics are obtained from them, including top-notch innovative talent cultivation, STEM education for low-income groups, undergraduate education, vocational education, cutting-edge technology education, big data-driven technology, artificial intelligence, and teacher development.

Against the backdrop of the in-depth promotion of the "Belt and Road" Initiative and the vigorous development of the digital economy, the cultivation of artificial intelligence (AI) talents has become a focal point of regional competition. Guangxi, with its unique geographical, policy, and cultural advantages, holds an irreplaceable strategic position in promoting AI industry-education integration cooperation with ASEAN. Based on an overview of international cooperation theory and industry-education integration theory, this paper systematically analyzes the practical foundation, exploratory practices, and existing issues of Guangxi-ASEAN AI industry-education integration cooperation. It finds that the current cooperation faces structural challenges such as the absence of top-level design, poor coordination mechanisms, and difficulties in standard alignment. In response to these issues, this paper proposes a four-in-one cooperation mechanism framework guided by the government, with universities as the main body, enterprises participating, and projects driving. It designs three core operational mechanisms: resource sharing, mutual recognition of standards, and industrial collaboration. Furthermore, it proposes optimization paths and policy suggestions from dimensions such as strengthening top-level design, building cooperation platforms, and innovating cooperation models.

The development of Multimodal Large Language Model (MLLMs) offers new technological support for cultivating design thinking and innovation capability in medical education. However, the current training of medical professionals remains predominantly centered on knowledge memorization and one-way didactic instruction. The systematic integration of artificial intelligence and innovation methodologies is still insufficient, while challenges such as limited interdisciplinary integration and inefficient teaching iteration have constrained the cultivation of innovative literacy. To address these challenges, this study constructs the “MLLM+EDIPT” integration framework, which deeply couples the design thinking model from Stanford University's D.school with MLLM technology. It systematically elucidates the cognitive support mechanisms of MLLM across the stages of empathy, definition, ideation, prototyping, and testing. Targeting diverse stakeholders, including hospitals, universities, educators, and students, this study proposes a phased cultivation strategy and competency framework based on school-clinician collaboration. This framework emphasizes the full integration of the “human-centered” philosophy, leveraging AI to enhance situational awareness, feedback timeliness, and methodological rigor, thereby driving the transformation of teaching models from experience-driven to intelligent collaboration. Ultimately, this research aims to provide a theoretically grounded and practically viable pathway reference for the cultivation of top-tier innovative medical talents in the AI era.

The goal of new engineering education is to cultivate innovative and outstanding engineering talents with sustainable competitiveness with new ideas and new models. Based on the concept of new engineering, by grasping the development trend and industrial policy of new industry and combining with the training goal of software engineering talents, the paper puts forward five core abilities that software engineering talents of new engineering should possess. Three “trinity” application- oriented cultivating talents modes are constructed, namely, the trinity education subject of “ school, enterprise and research institution”, “general education course, professional course and career course” and the trinity cultivate mode of “ learning, using and creating”. The teaching mode of software engineering talents facing new engineering is innovated from the top-level design.

This paper innovatively applies computational biomechanical models to the field of human capital flow research, establishing a novel analytical framework. By introducing the potential field concept from biomechanics to describe economic development dynamics, employing continuum mechanics methods to characterize talent flow patterns, and integrating numerical computation techniques, we achieved systematic simulation of the relationship between human capital flow and economic growth. The research reveals that human capital flow promotes economic growth through three primary mechanisms: knowledge accumulation effect, innovation-driven effect, and industrial upgrading effect. In the short term, human capital flow can contribute to a 1.35 percentage point increase in GDP growth within one year; in the long term, its total contribution to economic growth rises from 3.19% to 7.42% over a decade. The study identifies four flow patterns: agglomeration, gradient, network, and circular, with agglomeration-type flow showing the most significant economic effect, contributing 42.5% to economic growth. Policy simulation results indicate that innovation-driven strategies can drive GDP growth by 2.85 percentage points, industrial upgrading strategies contribute 2.42 percentage points, talent incentive strategies achieve 2.15 percentage points growth, while comprehensive optimization strategies can realize a 3.65 percentage point growth effect. Based on these findings, we propose policy recommendations including building a multi-level talent support system, implementing a “gradient cultivation, collaborative development” regional development strategy, and following the principle of “top-level design, phased implementation, key breakthrough.” This research not only achieves methodological innovation but also provides a theoretical foundation and practical guidance for formulating scientific talent policies.

In the context of the deepening development of the digital economy, the traditional university-enterprise collaborative innovation and entrepreneurship education model in the field of higher education is facing structural contradictions such as inefficient resource integration, loose collaborative mechanisms, and the virtualization of practical teaching. This study reveals the enabling mechanism of digital technology on school-enterprise collaboration and proposes a four-dimensional implementation path of “curriculum system–practice platform–education reform–evaluation system.” Digital technology builds a two-way circulation mechanism of knowledge flow between schools and enterprises through the data middle platform, deepens collaborative education by relying on the virtual co-research platform, innovates the ability cultivation paradigm with technologies such as VR/AR, and constructs a dynamic evaluation system using all-dimensional data. Research shows that digital empowerment can break through the temporal and spatial barriers and information asymmetry of the traditional model, improve the efficiency of resource allocation between schools and enterprises, and increase the conversion rate of students’ practical projects. In the future, it is necessary to further expand the application of technology in the top-level design of talent cultivation, improve the cross-departmental policy coordination and ethical review mechanism, and provide theoretical and practical paradigms for cultivating high-quality compound talents with data thinking, innovation ability, and industry adaptability in the digital economy era.