Based on large-scale student survey data, this study explores the underlying mechanisms by which AI influences college students’innovative thinking within curricular environments characterized by varying levels of “challenge” and “support”. The findings reveal that learning engagement acts as a bridge in enabling AI to foster innovative thinking. Specifically, AI exerts its effects indirectly primarily by stimulating deep cognitive and emotional engagement, and its efficacy is shaped by the curricular environment. The curricular environment exhibits a dual moderating logic of “amplification” and “substitution”: a “high challenge–high support” environment strengthens the “amplification” pathway mediated by learning engagement, whereas environments deficient in either challenge or support highlight the “substitution” pathway. The educational value of AI is not an intrinsic attribute but is co-constructed by the curricular environment through the synergy of challenge and support.

While generative artificial intelligence accelerates educational transformation, it simultaneously precipitates urgent legal challenges and ethical dilemmas. The research findings reveal that college students exhibit significant behavioral tendencies toward the improper use of GenAI. This trend has precipitated a systemic crisis in traditional pedagogical models centered on knowledge dissemination, with courses such as ideological and political education and humanities general education experiencing the most significant erosion of their foundational values. The structural imbalance between technological openness and ethical governance, insufficient teacher support coupled with intensified peer competition, and escalating academic pressure alongside proliferating burnout emerge as pivotal drivers of GenAI improper use. Risk perception in technology usage can effectively mitigate opportunistic tendencies under stressful conditions, while substantially enhancing the intervention efficacy of teacher support. The risks associated with the use of GenAI can be mitigated through the following approaches: establishing a new educational model of human-machine collaboration, creating ethical constraints for technology applications, strengthening teachers’ AI-ready leadership, and promoting a paradigm shift in the educational evaluation system.

With the accelerated process of digital transformation in higher education, the tension between traditional university teaching evaluation models and the digitalized educational paradigm has become increasingly prominent, placing higher demands on the construction of teaching evaluation systems in universities. Against this backdrop, fully exploring the functional potential of artificial intelligence in university teaching evaluation has emerged as a crucial pathway for promoting evaluation reform. From the perspective of the “technology-education” relationship, the current application of artificial intelligence in teaching evaluation still faces numerous challenges. The root cause lies in the ineffective mutual construction between technology and education, which in turn limits the effectiveness of evaluation practices. Therefore, this study proposes a practical pathway of “technology-education” co-construction to enhance the practical efficacy of AI-based teaching evaluation in higher education. It seeks to build a process-oriented “teaching-learning-evaluation” system through the dimensions of coordination, synergy, and mutual regulation between technology and education, thereby facilitating the deep integration of artificial intelligence into university teaching evaluation and constructing a system that aligns with the demands of higher education in the new era.

Artificial intelligence technology poses fundamental challenges to the traditional teacher-student centered educational structure and urgently requires the construction of new educational models. The five element education model of “teacher-student-AI-environment-culture” proposes that the educational logic in the intelligent era should shift from traditional binary teaching between teachers and students to a multi element collaborative interactive network. The five element education model is student-centered with AI at its core. By reshaping learning methods, teaching methods, environmental ecology, and cultural atmosphere, it cultivates students’ core competencies of the “eleven abilities”—such as innovation and creativity—that enable them to master and surpass AI. Based on the previous exploration of Beijing Institute of Technology, this model provides an effective practical path for cultivating top-notch innovative talents in the era of intelligence.

Top innovative talents are the core force underpinning national original innovation capacity and high-level technological self-reliance. At present, China’s integrated cultivation of top innovative talents is mainly led by secondary schools, with limited exploration extending downward to primary education and insufficient mechanisms effectively linking upward to higher education. Universities generally remain in a “post-hoc receiving” role within the talent selection and cultivation chain. Taking the role transformation of universities in integrated cultivation as a point of departure, this study draws on the cases of the United States, Germany, and Singapore to systematically examine their institutional designs and operational mechanisms in early identification, curriculum articulation, research practice, and teacher development. Based on the practical challenges facing China, the study proposes targeted suggestions: universities should shift away from a “relay-style” logic of education and reshape their mission within the national talent ecosystem; they should build tiered cooperation mechanisms to enhance the capacity of ordinary secondary schools; they should take the lead in establishing professional support systems for teachers engaged in cultivating top innovative talents; and they should strengthen their hub function to construct cross-stage collaborative governance platforms for talent cultivation.

Against the backdrop of the deep advancement of education digitalization strategies, revealing the intrinsic mechanisms of the deep integration of artificial intelligence and higher education is essential for building a high-quality intelligent education ecosystem. Based on STS theory and the TOE framework, this study extracts five conditional variables: technological autonomy, leadership impetus, organizational synergy, application coverage, and ecological openness, while classifying integration levels according to the SAMR model. By employing fsQCA on 53 “AI + Higher Education” typical application scenarios recognized by the Ministry of Education, this paper explores the causal mechanisms driving the digital transformation of higher education. The findings indicate that the six configurations generally follow two modes: “Organization-Driven Transformation” and “Technology-Ecology Symbiosis”. Accordingly, it is proposed to establish a top-level strategy of mutual construction between organizational change and technological empowerment, build a borderless and open intelligent landscape, and precisely match transformation pathways based on resource endowments, so as to construct a high-quality intelligent education ecosystem featuring human-machine collaboration, co-creation, and sharing.

Research universities are an important component of the Carnegie Classification of Institutions of Higher Education in the United States, and its classification method has been continuously improved and refined in various updates. Before the 2021 edition, the classification of research universities was included in the basic category; the 2025 edition renamed the Basic Classification as the 2025 Institutional Classification, while adjusting the classification of research universities to a separate category of Research Activity Designations, with corresponding optimizations and adjustments to the classification methods and standards. The new classification, while retaining the two core indicators of “number of research doctorates awarded” and “research expenditure” to distinguish three types of research universities, has dozens of additional indicators, helping to accurately present the actual development status of each research university from multiple perspectives. It is recommended to scientifically plan the development scale of China’s research universities, thoroughly evaluate the feasibility of core classification indicators such as the number of doctoral degrees awarded and research expenditures, objectively establish basic standards for independent classification, and set up several observation indicators to build a multi-dimensional research university classification system with Chinese characteristics.

As a key area in the new round of institutional reform, institutional reform in universities serves as the fundamental focus for accelerating the modernization of university governance systems and capabilities. To reveal the internal mechanisms of higher education institution reform, this study adopts the grounded theory approach to analyze 40 typical cases of institutional reform initiatives in universities, thereby summarizing and refining the practical logic behind these reforms. The research findings indicate that such reforms are driven by practical issues such as operational inefficiency and excessive institutional expansion. They require systematic implementation through synchronized streamlining of administrative bodies and staffing, strengthening departmental restructuring and integration, centralized management, and digital transformation process redesign. This aims to reduce organizational structures and staffing quotas, achieve systematic and holistic governance, and prioritize risk mitigation, steady progress, job transfers, and faculty governance capacity enhancement as safeguard mechanisms. Consequently, it is necessary to further establish an overall action framework for institutional reform in universities, explore scientific decision-making foundations for institutional streamlining and optimization, and implement a relatively flexible and adaptive organizational reform model through supportive organizational culture and humanistic care.

The coping strategies of college students towards academic pressure shape the development pattern of their social emotional competence. Based on the survey data of 937 college students, this study explores the mechanism by which academic pressure affects social emotional competence. The research finds that academic pressure has a “double-edged sword” effect: while promoting the development of ability characteristics, it inhibits the improvement of ability tendencies; academic pressure drives college students to seek “digital intimacy”, but this path becomes an invisible barrier to the development of social emotional competence; “real companionship” strengthens the positive impact of academic pressure on ability characteristics, but has limited influence on ability tendencies; academic pressure reflects the “learner’s paradox” in higher education through the dual effects of “ability construction-value dissolution”. To enhance the social emotional competence of college students, it is necessary to reasonably regulate the sources of academic pressure for college students, guide them to use the Internet correctly to cope with academic pressure, strengthen their real interpersonal support systems, and construct a dual-track evaluation system for students under academic pressure.

In higher vocational colleges, teachers are the central human capital for training high-skill talent, and the level of their digital literacy is decisive in driving the digital overhaul of specialties and achieving high-quality growth. Based on Social Cognitive Theory, the article constructed a “personal-behavior-environment” triadic interactive framework of teachers’ digital literacy , analyzing the situation of teachers’ digital literacy in Zhejiang Higher Vocational Colleges through comprehensive qualitative interview, questionnaire survey, structural equation modeling (SEM), and fuzzy-set qualitative comparative analysis (fsQCA). The study found that practice behavior is the direct channel for the generation of teachers’ digital literacy; multi-condition collaboration is the key mechanism for the development of teachers’ digital literacy; and insufficient external support is the main obstacle to improving teachers’ digital literacy. Based on these findings, it is recommended to enhance teachers’ individual digital literacy self-efficacy to strengthen digital teaching behaviors; deepen school-enterprise collaborative training in higher vocational colleges to promote the realization of multi-condition collaboration; and perfect the policy and institutional guarantees for teachers’ digital literacy to amplify the effect of external support.

AI-generated theses are deemed the applicant’s independently completed work under copyright law but regarded as ghostwriting research misconduct in higher education, creating a coherence interpretation dilemma. Labeling prompt?based AI thesis writing as ghostwriting fails to satisfy its constituent elements and systematic interpretation under academic norms. The divergence in originality standard between copyright law and academic norms forms the logical basis for identifying research misconduct in AI-generated theses. A dual-layer framework of “content-behavior” should be adopted: at the content level, existing academic research misconduct categories should apply to risks of fabrication, falsification, and plagiarism, with human authors bearing strict liability; at the behavior level, new misconduct categories should be established based on mandatory AI usage disclosure obligations.

Teaching motivation and teaching engagement are two key factors influencing teachers’ professional development and students’ learning outcomes. Based on survey data from 7 996 teachers across 134 undergraduate institutions nationwide, this study applied latent profile analysis to examine the profiles, effects, and distribution characteristics of teaching motivation and engagement among Chinese university teachers. The results identified five distinct profiles: low motivation and low engagement, relatively low motivation and low engagement, high motivation but low engagement, low motivation but high engagement, and high motivation and high engagement. Each profile was influenced by environmental factors, and their distribution varied across demographic variables. The study deepens the understanding of teaching mechanisms among Chinese university teachers.

AI is increasingly demonstrating capabilities in academic writing that rival or even surpass those of humans. Consequently, to safeguard the originality and rigor of degree theses, some universities have established specific AIGC detection rates as a criterion for identifying academic misconduct. While this policy may temporarily curb the direct use of AIGC content, it is already presenting challenges such as false positives and a widening digital divide. In the long term, it could expose the inadequacy of current regulations in the face of technological progress, leading to a fundamental misalignment of regulatory boundaries. Therefore, navigating the AI era necessitates a paradigm shift guided by STIs—moving from formalistic judgment to value-based assessment, and from technological disciplining to capability development. By constructing multi-level evaluation systems, strengthening technical support, and establishing adaptive regulatory frameworks, we can accommodate the future paradigm of human-AI collaborative creation. This approach offers a new analytical framework for understanding academic innovation in the age of AIGC.

China prioritizes high-quality and full employment for graduates by deepening the industry-education integration. Using data from the 2024 annual quality reports of 1 003 higher vocational colleges, this study finds that such integration significantly improves graduates’ employment quality, with effect sizes following the order: job quality > wages > employment opportunities. Specifically, it demonstrates a clear “cumulative advantage effect” effect on overall employment quality and job quality, a distinct “compensatory effect” effect on employment opportunities, and a mixed effect on wages. Colleges that are in the central region, public, and out of the “Double High-Level Plan” benefit more in overall employment quality and job quality. Colleges that are in the western region, private, out of the science, engineering, agriculture, and medical fields, and out of the “Double High-Level Plan” benefit more in employment opportunities. Colleges that are in the eastern region, private, out of the science, engineering, agriculture, and medical fields, and in the “Double High-Level Plan” benefit more in wages. To effectively leverage the positive impact of industry-education integration, it is necessary to build a multi-collaborative promotion mechanism, establish a tiered and progressive practical model, and implement context-specific support policies.

Tracing the genesis of self-contained knowledge in China’s vocational pedagogy, its vitality, dissemination, and efficacy are fundamentally rooted in the autonomous practice of vocational education with Chinese characteristics. In the context of building a leading educational nation, the key to constructing a logical, systematic, and endogenous self-contained knowledge system for vocational pedagogy in China—and to leveraging its role in serving and supporting independent practices in vocational education—lies in strengthening the development of vocational pedagogy as a discipline in the new era. Self-contained knowledge serves as the logical core of this system. Accordingly, three epistemological frameworks have been established: one focusing on autonomous practice and knowledge in Chinese vocational education, another on the forms, content, and purposes of self-contained knowledge in vocational pedagogy, and a third on independent vocational education philosophies, theories, and knowledge points. Using these as the basis for knowledge production, a disciplinary framework for vocational pedagogy centered on the “production-dissemination-application” of self-contained knowledge has been developed. This paper further proposes systematic pathways through which the discipline of vocational education can empower the construction of an self-contained knowledge system for vocational pedagogy in China.

In the intelligent era, cultivating critical AI literacy is of great significance for college students to promote innovative behaviors. Based on survey data from 1?062 college students, this study empirically analyzes how critical AI literacy influences college students’ innovative behaviors from the perspective of knowledge management theory. The results show that critical AI literacy exerts a significant positive impact on college students’ innovative behaviors, and this conclusion remains valid after a series of robustness tests including the instrumental variable method. In terms of the mechanism of action, critical AI literacy improves the level of innovative behaviors through the chain path of reconstructing the quantity of reflective knowledge and then reconstructing the structure of reflective knowledge; it also affects innovative behaviors through the independent mediating role of reflective knowledge structure reconstruction. Moreover, knowledge sharing can further strengthen the positive effect of critical AI literacy on reflective knowledge structure reconstruction. On this basis, this study puts forward implications such as cultivating college students’ critical AI literacy, encouraging them to reflect on and reconstruct their own knowledge with the help of AI, and building intelligent knowledge sharing platforms.

Entering the 21st century, to address global challenges, build regional innovation ecosystems, and resolve crises in university development, the fourth-generation university has emerged as a new paradigm. The fourth-generation university is distinguished by multi-actor collaborative innovation for regional and societal value creation, challenge-based learning that cultivates creative and socially responsible change-makers, cross-disciplinary research that integrates knowledge to address complex societal challenges, and open, tightly coupled partnerships that support a mutually beneficial innovation ecosystem. The fourth-generation university signals the future direction of global higher education and denotes a shift from academic excellence to the co-creation of societal value. By integrating education and research, deepening localized studies, and mapping collaborative geographies, these initiatives provide actionable guidance for universities to integrate into regional innovation.

Classification-based development of higher education institutions is an important indicator of the maturation of a higher education system. In the process of the formation and evolution of the classification system for university in China, three structural features have become deeply intertwined: a vertical hierarchy based on institutional levels, a horizontal classification rooted in industrial sectors and academic disciplines, and a resource allocation pattern centered on key construction projects. Their interplay has caused institutional classification to gradually devolve into hierarchical ranking in practice. This predicament is jointly shaped by cultural, fiscal, and governance factors: a monolithic standard of success has eroded institutional type identity; a fiscal logic that privileges selectivity over baseline support has undermined equity across types; and homogenized evaluation systems have rendered classification rules persistently ineffective. To make classification operational, fiscal restructuring should serve as the entry point, establishing category-specific funding guarantees and a differentiated appropriation system while advancing type-based performance evaluation and recalibrating governance instruments, so that institutional reform creates a practical foundation for cultural transition and gradually builds a higher education classification system that accommodates diversity and ensures baseline equity.

As a vital component of China’s higher education system, local high-level universities shoulder multiple missions, including serving regional economic and social development, cultivating high-quality applied talents, and facilitating the transformation of scientific and technological achievements. This paper begins with an exploration of the evolution of China’s higher education management system, analyzing the origins of development issues faced by local universities. It then examines the current status and challenges encountered by local high-level universities. Based on case studies, the paper proposes strategies and recommendations for their construction and development. The research indicates that, compared to centrally administered universities, local high-level universities lag in policy support, talent attraction, and resource aggregation; however, they possess distinct advantages in creating regional characteristics and serving local development. To build local high-level universities, it is essential to clarify their strategic positioning and proactively align with regional development needs; dynamically adjust disciplinary and professional structures to establish mechanisms closely linked with industrial chains; develop collaborative innovation models integrating government, industry, academia, research, and application to achieve resource sharing and mutual benefits; balance the relationship between serving local interests and pursuing excellence, thereby forming comparative advantages in distinctive fields. Ultimately, local high-level universities should strive for leapfrog development through regional service and realize their value within national strategies and regional growth.

Interdisciplinary integration has emerged as a critical imperative for technological innovation and talent cultivation. Grounded in the dual logic of knowledge boundaries and control inherent in disciplines, this study employs Basil Bernstein’s concepts of classification and framing to deconstruct the dynamics of integration. The analysis reveals that interdisciplinary practice entails a structural shift from “strong” to “weak” classification—signifying increased permeability across disciplinary borders—and a transition from “strong” to “weak” framing, reflecting a diversification of control over knowledge production. Based on these dimensions, four ideal types are proposed: institutionalized multidisciplinary coordination, spontaneous multidisciplinary cooperation, institutionalized fusion innovation, and emergent frontier exploration. It reveals the evolutionary logic of Interdisciplinary integration, moving from a simple combination to a deep collaborative creation. The study concludes that advancing interdisciplinary integration requires not the dissolution of existing disciplines, but the navigation of the embedded agency paradox, thereby sustaining a productive tension between institutional stability and epistemological innovation.

The university teaching and learning system and pedagogical practices are continuously challenged by the iterative development of artificial intelligence. The advancement of AI imposes new demands on various dimensions of higher education, including its philosophy, institutional frameworks, and methodologies. “Decentralization” can be interpreted as a process of distributing power, information, or resources, characterized by high autonomy, inclusiveness of innovation, efficient connectivity, and equity. It aims to enhance the overall efficiency of the system, increase transparency, and strengthen risk resilience by reducing dependence on central authority. Decentralization offers a novel conceptual model for addressing the challenges posed by AI technologies in university talent cultivation. It can strengthen students’ individualized development, promote their value realization and innovative capacities, and safeguard educational equity. Furthermore, it should be emphasized that the decentralized model ought to augment rather than replace traditional teaching approaches, while paying close attention to potential risks such as knowledge fragmentation, quality degradation, blurred authority, and value nihilism.

Referencing an evaluation framework for a powerful education system comprising 15 indicators across four dimensions.The findings indicate that China leads in 3 indicators, is approaching America level in 3 indicators, and shows potential for further improvement to narrow the gap in 6 indicators. Based on dimensionless calculation, China’s comprehensive Education Power Index has reached 79.8% of that of America. This demonstrates the historic achievements and transformative progress in China’s educational development since the reform and opening-up, particularly since the 18th National Congress of the Communist Party of China. It is recommended to deepen comprehensive educational reform, coordinate reforms in the systems of science and technology, and talent development, integrate higher, continuing, and vocational education, and fully leverage the leading role of higher education, thereby achieving new breakthroughs in accelerating the construction of a powerful education system.

Against the macro backdrop of building an educational powerhouse and advancing Chinese modernization, the interaction between universities and regions has been endowed with new significance in the current era. Based on national strategic needs and the current state of higher education development, this article systematically analyzes the issues of regional “imbalanced and inadequate development” in China’s higher education. It elucidates the functional positioning of universities as the pivotal hubs for “cultivating talent, driving innovation, and leading cultural development”. The article proposes that universities need to deepen their symbiotic relationship with regions through three major pathways: “structural reform, transformational change in models, and systemic reform”. This involves further optimizing educational evaluation and internal governance to stimulate endogenous motivation. Looking ahead, more universities should progress from?“grounding locally while aspiring globally”?to?“trailblazing uncharted territories”,?serving national strategies through deep integration into regional development and contributing the wisdom and strength of Chinese higher education to the world.

Organized interdisciplinarity is an important approach for research universities to effectively address national strategic needs and foster innovative knowledge production. However, there exists a tension between efficiency and vitality, which constrains the outcomes of interdisciplinary knowledge innovation and integration. This study selects three research universities—Zhejiang University, Shanghai Jiao Tong University, and ETH Zurich—as case studies, employing a “micro-analysis” technique to deconstruct the specific processes of organized interdisciplinarity. The findings reveal that: Building on the disciplinary foundation of research universities, the “organized approach” in interdisciplinary efforts should be regarded as an efficiency pathway rather than a meaningful goal, and both organized and free exploration paths should serve to construct a disciplinary ecosystem; Research universities primarily advance organized interdisciplinarity through practical strategies such as resource support, organizational adaptation, and cultural guidance; Multiple mechanisms, including advantage activation, resource balancing, and ecological empowerment, contribute to achieving a dynamic balance between organizational efficiency and ecological vitality in organized interdisciplinarity in research universities.

Based on panel data from 2012-2022, this study investigates the impact of higher education development on the agglomeration of scientific and technological(S&T) talents. The results indicate that higher education development drives S&T talent agglomeration, with “innovation ecosystem optimization-talent attraction enhancement” serving as a key transmission mechanism. This driving effect exhibits a threshold characteristic of increasing marginal benefits both intrinsically and through government support, and it spreads to geographically adjacent regions via spatial spillover effects. The driving effect is significant in eastern China, insignificant in central China, and exhibits a “failure paradox” in western China.. Accordingly, a gradient optimization strategy for “input-process-output” should be implemented to fully unleash the marginal benefits of higher education; the allocation system of regional innovation factors should be improved with higher education as the hub to promote the transition of S&T talent agglomeration from “scale-driven” to “ecosystem-absorptive”; and spatial linkage awareness should be strengthened, adopting a “flying geese pattern” to guide regionally differentiated development.

Currently, cutting-edge technologies represented by artificial intelligence are advancing rapidly. The innovation-driven development of the AI industry has become a pivotal point in global future industrial competition, urgently requiring a large supply of high-quality talent. Based on data mining of recruitment information from AI enterprises, this paper analyzes the talent demand characteristics of the AI industry from dimensions such as academic qualifications, majors, work experience, professional skills, and interdisciplinary research capabilities. By examining the current state of talent cultivation in AI-related majors at universities, it reveals mismatches in talent supply and demand. Addressing the talent needs of AI industry innovation, the study proposes optimizing AI talent training pathways in higher education, offering insights for universities to refine AI-related disciplines or major setups and improve talent training models.

The classified development and evaluation of higher education institutions represent the top-level design for high-quality development in China’s higher education sector, as well as a major initiative in the reform of the governance system and mechanisms. The classification-based evaluation of higher education institutions exerts a strong guiding influence on their classified development, necessitating a logical congruence between the two. This article begins by interpreting the concepts of classified development and classification-based evaluation of higher education institutions and introducing their policy background. It then analyzes the logical congruence between classified development and classification-based evaluation, supplemented by a case study of practical experiences in the United States. By elaborating on the value logic and open logic of the reform in classification-based evaluation, and through discussions on the effectiveness evaluation of “Double First-Class” construction universities and the evaluation of general higher education institutions, this article proposes key development directions for the reform of classification-based evaluation that are conducive to the classified development of higher education institutions in the new era. These directions include value guidance, framework construction, theoretical innovation, and the refinement of evaluation indicators.

Cultivating top-notch innovative talent in foundational disciplines is a strategic task in building a strong education nation, and academic aspiration is vital to top-notch students’ development. Using Q methodology, this study examines the aspiration types and their formation among 23 top-notch students. We identify three types: an interest-first, intrinsically driven type; a reality-oriented, goal-integrative type; and a hesitant, passively sustained type. They conrrespond to the veritists, pragmatists, and strugglers. Differences in program-application logic, academic and research experiences, and attitudes toward external evaluation and competition jointly shape these patterns. Accordingly, tailored support is needed to help students sustain growth that serves both long-term personal development and national strategic needs.

Research on the global trends of higher education development helps to profoundly grasp the logic of transformation and future directions of higher education. An analytical framework is constructed from the three dimensions of dynamics, priorities, and stakeholders in higher education to analyze the development trends in major countries. This study identifies several key tendencies of higher education: a trend of talent aggregation driven by both market and international dimensions; a trend of technological competition under the dual impetus of strategy and excellence; a trend of collaborative innovation through the integration of education and industry chains; and a trend of negotiated advancement based on both internal and external logics. At present, China’s higher education development has completed the phase of “quantitative” accumulation and entered a new stage. It should focus on enhancing the capabilities of innovation development, high-quality development, and balanced development, and give full play to its leading role in building a strong education country.

Educational evaluation is pivotal to building a strong nation in education, and classification framework holds strategic significance for comprehensive reform. China’s graduate education classification evaluation has undertaken valuable explorations but still faces challenges such as blurred classification ontologies and mismatched functions. This study constructs an “Adaptive-Ecological Classification”(AEC) Framework for Evaluation, grounded in systems thinking and the foundational logic of “ontology-driven, goal-oriented, and institutionally coordinated” classification evaluation. It proposes a practical pathway comprising macro-level ecological planning, meso-level value-oriented guidance, and micro-level standard system construction, providing theoretical underpinnings and practical guidance for building an autonomous classification evaluation ecosystem.

Western China faces dual challenges of talent outflow and declining momentum. Through policy text analysis, binary Logit regression, and Tobit regression, this study investigates characteristics of talent policy instruments in Western China from 2012 to 2022 and their impact on the mobility of graduates from “Double First-Class” construction universities. It is found that the policy instruments exhibit three imbalances: between “hard input and soft support” on the supply side, “macro strategies and micro-implementation” on the environmental side, and “internal cooperation and external leverage” on the demand side. Among these instruments, infrastructure, talent development, regulatory control, tax incentives, and industry-academia-research-government cooperation are strongly predict of graduates staying to work in their hometowns, while government procurement, public services, and financial support instruments significantly attract talents. Ahead, the supply side should optimize educational resource allocation and the development of digital platforms; the environmental side should strengthen legal safeguards and the targeting of strategic measures; and the demand side should establish an industry-academia-research-government collaborative innovation system and a targeted government procurement mechanism. A dynamic policy closed-loop of “digital-intelligent optimal allocation, effective implementation, demand chain traction”will promote rational talent flow and regional innovation interaction, resolving Western China’s talent development dilemma.

Whether performance pressure generated by the tenure-track system can effectively promote the academic ability development of young faculty constitutes an important criterion for evaluating the effectiveness of its implementation. To clarify the logic and mechanisms involved, this study conducted in-depth interviews and grounded theory analysis with 25 pre-tenured young faculty members from 20 universities across 11 provinces (municipalities) in China. The findings reveal that pre-tenured young faculty’s cognitive appraisal and coping strategies are critical transmission factors in the process by which performance pressure affects academic ability development. Faculty who emphasize challenge opportunities, driven by growth needs and promotion motivation, tend to adopt promotion-focused job crafting strategies to pursue positive gains, thereby achieving refinement and advancement in academic ability development. In contrast, faculty who focus on potential threats, driven by security needs and avoidance motivation, tend to adopt prevention-focused job crafting strategies to avoid punitive consequences; however, such strategies often neglect and impede substantive improvements in academic ability development. The incentive effectiveness of the tenure-track system therefore needs to be supported by performance rules grounded in scientific judgment. Excessively intensive performance requirements that disregard the stress tolerance and career growth patterns of young faculty are likely to induce risks of academic utilitarianism.

Against the backdrop of digital transformation in vocational education, the explicitation of tacit knowledge has emerged as a crucial issue for enhancing the quality of skilled talent cultivation. The essence of tacit knowledge explicitation lies in achieving multiple transformations: from tacit knowledge to formalized knowledge, from experiential knowledge to conceptual understanding, and from implicit information to explicit encoding. In practice, tacit knowledge explicitation faces numerous challenges, including epistemological misunderstandings, technical limitations and integration difficulties at the implementation level, fragmentation of meaning construction at the pedagogical transformation level, and institutional and mechanistic barriers at the environmental level. In this regard, it is necessary to construct a digitally-supported multidimensional framework for knowledge identification and extraction, innovate tacit knowledge representation strategies in digital teaching material design, and establish quality assurance and continuous updating mechanisms for digital teaching material development.

As the core combination point of technology as the primary productive force, talent as the primary resource, and innovation as the primary driving force, universities are deeply embedded in the overall national strategy and serve as a key hub supporting the construction of an educational powerhouse. The “15th Five-Year Plan” is a crucial period for building a strong education country. The development strategy of universities should anchor the goals of building a strong country and national rejuvenation, deeply rooted in the century-old gene of “saving the country, rejuvenating the country, and building a strong country”. Through the symbiotic evolution of historical position and the mission of the times, the dynamic balance of law cognition and strategic adaptation, the integration and innovation of digital intelligence empowerment and the essence of education, the development paradigm should be systematically reconstructed. The development of universities requires interdisciplinary integration to solve the “bottleneck” technical problems and build an independent knowledge system; Guided by cultural values, deepen mutual learning among civilizations and enhance international discourse power; Guided by social demand, improve the precision service mechanism and enhance service efficiency. The university will achieve a strategic leap from “adapting to the environment” to “leading transformation” by serving the self-reliance and self-improvement of science and technology, fostering new quality productive forces, and building an innovative country, thereby providing core strategic support for Chinese-style modernization.

The rise of Artificial Intelligence (AI) has exposed a certain predicament of the traditional university education model. Although many universities have introduced AI-related courses, and some have made them compulsory, these courses are still often taught through standardized pedagogies inherited from the industrial age. As a result, knowledge marked by generation, openness, and nonlinearity is delivered through an industrial framework shaped by linearity, uniformity, and control. This contradiction may be described as the paradox of AI education, or, metaphorically, as “horse-drawn carriages pulling steam engines.” Student silence, disengagement, and absenteeism are outward signs of this deeper problem. What higher education now needs is not simply more AI instruction, but a new paradigm of human–AI coexistence: one in which students are recognized as agents of learning, teachers become designers of future learning environments, and education moves from “teaching AI” to “learning with AI.” Only through such a shift can higher education respond to technological change while preserving its humanistic purpose. While AI will undoubtedly transform educational concepts, teaching methods, and assessment, the fundamental mission of the university education remains unchanged. Only is this way can university education truly fulfill its fundamental mission in the AI era.

Teacher mobility is a continuous hot topic in the research of the teaching staff in colleges and universities. Based on the background of the personnel system reform in colleges and universities with the tenure and tenure-track system as the main orientation, this study focuses on the relationship between the changes in the appointment system and the mobility of college teachers, and attempts to analyze the new characteristics of the mobility of college teachers using new evidence. The study employed the resume analysis method and took 26 346 teachers from 27 universities as samples. Through empirical research, it was found that the overall scale of teacher mobility in China’s universities has been on the rise; In the stage of the tenure and tenure-track system reform, the frequency of teacher mobility has reached a new high, the number of mobility for young teachers has increased, the mobility cycle of young teachers was significantly shortened, and the average mobility cycle of teachers was highly consistent with the appointment and assessment cycle. Based on this, it is also found that the reform of the pre-appointment - tenure-track system presents the characteristics of high competition and strong screening. The regulatory role of the appointment and assessment cycle on the mobility cycle of teachers; There is a conflict between the stability and long-term nature of teachers’academic careers and the increasing trend of teachers’mobility.

Artificial intelligence is a key field for the country to drive the development of interdisciplinary disciplines and emerging engineering disciplines. By applying social network analysis to the data of AI-related emerging engineering disciplines independently established by universities from 2019 to 2025, it is found that: the overall network evolution presents the characteristics of “scale expansion and structural stability”, with the network scale growing continuously and indicators such as network density fluctuating stably; the node evolution features “stable core and adjusted periphery”, where supporting disciplines like Computer Science and Technology have always been the network hubs; the core-periphery structure evolution shows the characteristics of “core expansion and dynamic replacement”, with six disciplines remaining in the core area and some disciplines moving into or out of the core; the network evolution is mainly driven by the joint efforts of AI supporting disciplines and scenario-based disciplines under government regulation and market adjustment. In the future, it is necessary to promote a new scenario-driven paradigm for discipline development, strengthen the leading role of core disciplines and central disciplines, enhance the construction of AI infrastructure, and explore new models for the development of interdisciplinary disciplines that go beyond disciplinary institutions.