AI-ENABLED
DISCIPLINARY CONSTRUCTION AND ASSESSMENT: DILEMMAS AND PATHWAYS
CONSTRUCCIÓN Y EVALUACIÓN DISCIPLINARIA HABILITADA PARA IA: DILEMAS Y VÍAS
I College of Education,
Beijing Institute of Technology, Beijing, China.
✉ wangzj1956@163.com, zhangzehui20192019@163.com
* Corresponding author: zhangzehui20192019@163.com
JEL
Classification: A23
DOI: https://doi.org/10.5281/zenodo.16955294
Received: 02/07/2025
Accepted: 04/08/2025
AI
has shown significant potential in enhancing the construction and high-quality
assessment of academic disciplines. As China transitions from an index-driven
to a high-quality development model in disciplinary construction, the focus has
shifted from using evaluation indicators to solidify disciplinary foundations,
to addressing societal concerns and improving overall standards through
assessment, and ultimately toward stimulating the internal motivation for
excellence. However, during this critical transformation period, AI-enabled disciplinary
construction and evaluation still confront three major challenges, namely
ideological inertia, technological limitations, and a lack of ecosystem
support. Meanwhile, the historic push toward building a strong nation has
accelerated the need for innovation in talent cultivation models, research
paradigms, and evaluation mechanisms. In response, this study proposes a
strategic path forward through deepening conceptual understanding and breaking
cognitive barriers to integrate AI more effectively into decision-making
process, building collaborative, open, and value-driven evaluation platforms to
foster multi-stakeholder engagement, and advancing human-AI collaboration to
drive adaptive transformation in assessment organizations through technological
innovation, thereby creating a governance ecosystem characterized by
human-machine complementarity and flexible responsiveness.
Keywords: AI-enabled;
disciplinary construction; disciplinary assessment; high-quality development.
Resumen
La
IA ha demostrado un potencial significativo para mejorar la construcción y la
evaluación de alta calidad de las disciplinas académicas. A medida que China
transita de un modelo de desarrollo basado en índices a uno de alta calidad en
la construcción disciplinaria, el enfoque se ha desplazado del uso de
indicadores de evaluación para consolidar las bases disciplinarias, a abordar
las preocupaciones sociales y mejorar los estándares generales mediante la
evaluación, y finalmente a estimular la motivación interna hacia la excelencia.
Sin embargo, durante este período crítico de transformación, la construcción y
la evaluación disciplinarias basadas en IA aún enfrentan tres desafíos
principales: la inercia ideológica, las limitaciones tecnológicas y la falta de
apoyo del ecosistema. Mientras tanto, el impulso histórico hacia la
construcción de una nación fuerte ha acelerado la necesidad de innovación en
modelos de desarrollo de talento, paradigmas de investigación y mecanismos de
evaluación. En respuesta, este estudio propone un camino estratégico a seguir a
través de la profundización de la comprensión conceptual y la ruptura de las
barreras cognitivas para integrar la IA de manera más efectiva en el proceso de
toma de decisiones, construir plataformas de evaluación colaborativas, abiertas
y basadas en valores para fomentar la participación de múltiples partes
interesadas y avanzar en la colaboración entre humanos e IA para impulsar la
transformación adaptativa en las organizaciones de evaluación a través de la innovación
tecnológica, creando así un ecosistema de gobernanza caracterizado por la
complementariedad hombre-máquina y una capacidad de respuesta flexible.
Palabras clave: IA habilitada; construcción disciplinaria; evaluación disciplinaria;
desarrollo de alta calidad.
Introduction
To meet the demand for high-level talent in
national modernization, the government has launched a series of
initiatives—such as the Key Discipline Development Program, the “211 Project”,
the “985 Project”, and the “Double First-Class” initiative—leading to the
formation of key talent training bases and hubs for scientific and
technological innovation.
In this context, disciplinary construction has
evolved from an index-oriented approach to one focused on high-quality
development, aiming to support scientific progress, national strategic goals,
and broader societal needs. This
article proposes a strategic path for conceptually deepening cognitive
breakthroughs to more effectively integrate AI into decision-making, build
collaborative, open, and value-based assessment platforms to foster
multi-stakeholder engagement, and advance human-AI collaboration to drive
adaptive transformation in assessment organizations through technological
innovation, thereby creating a governance ecosystem characterized by
human-machine complementarity and flexible responsiveness.
Laying a Solid Foundation
for Disciplinary Development through Evaluation-Oriented Approaches
In the 1980s, to better meet the country’s growing
demand for high-level talent during the reform and opening-up period, China
launched the key disciplinary construction program in the field of higher
education. The program aimed to establish benchmarks and explore effective
models for developing world-class disciplines. As a major initiative to
accelerate the development of Chinese universities, the program sought to
concentrate limited educational resources on selected universities and
disciplines, enabling them to take the lead and serve as national models.
In May 1983, the National Higher Education Work
Conference proposed the goal of “effectively developing a group of key
universities and disciplines to serve as the backbone of higher education and
as centers for education and scientific research”. In May 1985, the Decision of
the Central Committee of the Communist Party of China on the Reform of the
Educational System called for the “planned construction of a group of key
disciplines”. In 1988, the State Education Commission of the PRC organized a
panel of experts to select 416 key disciplines across 107 universities. In
2001, a second round of selection was launched, resulting in the identification
of 964 key disciplines.
When a discipline is designated as a key area of
construction, it gains access to significant forms of capital within the
academic field—including economic, cultural, social, and symbolic capital—and
becomes a crucial indicator of a university’s academic strength and
distinctiveness. The selection criteria for key disciplines serve as a critical
guide for discipline development. In pursuit of recognition and resource
support associated with key discipline status, universities align their
disciplinary development strategies with evaluation indicators to secure
competitive advantages in the national academic landscape. Over time, as
evaluation experience has accumulated and understanding of discipline
development has deepened, the design of evaluation indicators for national key
disciplines has become more scientific and objective. Nevertheless, these
evaluations remain fundamentally outcome-oriented.
In 1987, China introduced its first official
requirements for the selection of national key disciplines. First, the
selection and development of key disciplines had to be aligned with the
national demand for cultivating high-level talent in support of the Four
Modernizations, the trends in scientific and technological advancement, and the
availability of national financial resources. Second, key disciplines were to
be selected from eligible doctoral programs. Third, construction funding could
come from multiple sources, including national support, investment from
supervising authorities, and university self-financing. Emphasis was placed on
the initiative and vitality of universities and the disciplines themselves,
encouraging them to attract diverse funding and collaborative support by
producing high-quality talent and research outcomes. Construction was to be
carried out in phases as needed. Overall, universities were expected to have
strong foundations in disciplinary development direction, research teams,
teaching and research infrastructure, scientific equipment, and access to
library and reference resources.
In the 1980s, the conditions for disciplinary
construction in Chinese universities were relatively weak, and the overall
level was not high. The indicator-oriented selection and assessment system
served as an effective incentive mechanism. During the expansion of China’s
higher education system from small to large scale, this indicator-driven
approach played an important role. However, due to the limitations of the time,
the academic community’s understanding of evaluation indicators was still
relatively superficial. The blind pursuit of these indicators also laid hidden
risks for the development of some university disciplines. After the large-scale
expansion of higher education in China, the indicator-oriented selection system
began to show certain limitations.
Driving the Overall Improvement of Disciplinary
Construction by Stimulating Vitality
The 1990s was a period of profound transformation
in China’s social development. The dissolution of the Soviet Union and dramatic
changes in Eastern Europe sounded an alarm for China, while the rise of Four
Asian Tigers and Tiger Cub Economies in Asia became a source of motivation for
China’s development. The waves of economic globalization and regionalization
also prompted China to actively integrate into the global economic system, with
foreign trade gradually becoming an important pillar of economic growth. The 14th
National Congress of the Communist Party of China in 1992 set the goal of
establishing a socialist market economy, marking a significant milestone in
China’s economic reforms.
Under the influence of the socialist market
economy, the initiative for social development was activated, and the concepts
of performance and competition stimulated all sectors of society to pursue
change proactively. In disciplinary construction, the key to improving the
level of development lies in stimulating disciplinary vitality and responding
to the demands of the country and society for various types of high-level
talent. A level-based evaluation model centered on disciplinary competition
gradually took shape. Within the same disciplinary field, disciplines with
higher development levels were selected for key support to further leverage the
leading role of key disciplines, thereby enhancing the overall level of
disciplinary construction in Chinese universities. Level evaluation involves
designing an evaluation index system and setting evaluation standards based on
the evaluation’s objectives. Using these established standards, the evaluation
assesses the construction of degree-granting units and authorized discipline
and specialty programs, judging their compliance and capabilities, and
measuring their overall performance.
Horizontal
assessments generally involve ranking, categorizing, and grading the evaluated
units based on the assessment results.1 Such assessments are
sometimes also referred to as merit-based evaluations. One typical example is
the disciplinary assessment organized by the Academic Degrees Committee of the
State Council and the Ministry of Education, which represents a standard form
of horizontal evaluation. This assessment evaluates the overall quality of
first-level disciplines authorized to grant doctoral or master’s degrees,
following the Catalogue of Degree Awarding and Talent Training issued by the
Academic Degrees Committee of the State Council and the Ministry of Education.
Based on the evaluation results, the development status of each discipline is
analyzed and ranked accordingly.
In November 1995, approved by the State Council,
the State Planning Commission, the State Education Commission and the Ministry
of Finance jointly issued the “211 Project” overall construction plan, making
it clear that “the construction of key disciplines is the core, is an important
symbol of the level of teaching and scientific research, and is an effective
way to drive the improvement of the overall level of the university”. During
the “Ninth Five-Year Plan” period, 602 key disciplines construction projects
were arranged; during the “Tenth Five-Year Plan” period, 777 key disciplines
construction projects were deployed. 1999, the “985 Project” was officially
launched, requiring key disciplines to be constructed. In 1999, the “985
Project” was formally launched, requiring key construction universities to
focus on “supporting key disciplines, developing advantageous disciplines and
encouraging new disciplines” and building “special disciplinary zones”.
One of the objectives of the “211 Project” is to
enable a considerable number of key disciplines to become a base for training
high-level specialists, focusing on improving the teaching and scientific
research infrastructure conditions in colleges and universities with a high
concentration of key disciplines, so as to make significant improvements in the
quality of personnel training. The “985 Project” is a “strengthened version” of
the “211 Project”, and in 2004, the second phase of the “985 Project” was launched.
“In 2004, the second phase of the 985 Project was aimed at bringing a number of
disciplines up to or close to the level of first-class international
disciplines.
Discipline-level
assessment aims to measure the development level of academic disciplines and to
address key concerns from the state, universities, and society, such as: How
should the level of a discipline be recognized? How effective is disciplinary
construction?
How should such construction be carried out? This stage of discipline-level
evaluation represents a critical period of exploration, innovation, and
progress in China’s academic discipline development. It has played an important
role in advancing the overall quality of disciplinary
construction.
Despite the many challenges and issues it faces, discipline-level assessment
has generated valuable experience and laid a solid foundation for future
discipline development and evaluation efforts. It has also had a profound and
lasting impact on the pursuit of building world-class academic disciplines.
Promoting High-Quality Development of Disciplines
with a Focus on First-Class Advantages
In the 21st century, the factor-driven
economic era has been replaced by a new era characterized by innovation-driven
growth.2 The rapid iteration of modern information technologies has
accelerated the pace of societal development and intensified global
competition. Higher education, as the foundation of knowledge innovation and
the cultivation of innovative talent, is a key pillar of national
competitiveness.
The high-quality development of academic
disciplines forms the bedrock of building a strong higher education system.
Establishing disciplinary advantages is essential for transitioning from a
stage of “following” to one of “leading”. Amidst profound and unprecedented
global changes, a key challenge for building a strong higher education system
lies in identifying a scientific and contextually appropriate path for
discipline development that aligns with China’s national conditions.3
Achieving high-quality development of academic disciplines requires a paradigm
shift from an “indicator-driven” model to one centered on substantive,
high-quality outcomes.
Disciplinary characteristics represent the “gene
pool” of disciplinary culture, the driving force behind serving national
strategic needs, and the “moat” that safeguards the inheritance of academic
traditions. Characteristics reflect uniqueness—they are the essential traits
that distinguish one entity from another and form the basis and symbol of its
existence. They embody three levels of meaning: possessing what others do not,
excelling where others are comparable, and innovating beyond what others already
excel in[4]. The Ministry of Education clearly defines
“characteristics” in its Implementation Plan for the Evaluation of
Undergraduate Teaching in Higher Education Institutions as: “Characteristics
are the distinctive and high-quality features developed over the long course of
a university’s operation, which are unique to the institution and superior to
those of other universities”. The disciplinary development process of each
university reflects its deep integration with other disciplines, institutions, society,
and the nation, and highlights its significant role in specific fields.
At the stage of building China into a leading
country in higher education, the primary focus of discipline development has
shifted to the construction of world-class and globally competitive
disciplines. In 2015, the State Council issued the Overall Plan for Coordinated
Promotion of World-Class Universities and First-Class Disciplines, marking a
strategic transformation in China’s academic development—from key disciplinary
construction to first-class discipline development, and from a government-led,
selective model to a competitive, university-driven approach. Subsequently, in
2017, the Ministry of Education, the Ministry of Finance, and the National
Development and Reform Commission jointly released the Provisional
Implementation Measures for Coordinated Promotion of World-Class Universities
and First-Class Disciplines. The document emphasized building first-class
universities and disciplines with Chinese characteristics and global
excellence. It proposed aligning discipline development with the fundamental
mission of moral education, taking “first-class” as the goal, “disciplines” as
the foundation, “performance” as the leverage, and “reform” as the driving
force, in order to advance a number of high-level universities and disciplines
into the ranks of the world’s leading institutions.
As
China enters the second phase of the “Double First-Class” initiative, the
report of the 20th National Congress of the Communist Party of China proposed
for the first time to “accelerate the development of world-class universities
and advantageous disciplines with Chinese characteristics”. The concept of
advantageous disciplines builds upon the foundation of first-class discipline
development and carries specific conceptual and contemporary significance.
Advantageous disciplines refer to fields that exhibit clear strengths within
their respective domains and are irreplaceable in certain areas.
World-class
advantageous disciplines are those with notable comparative and competitive
advantages on the global stage, assessed against peer disciplines worldwide.
Such disciplines typically possess the following characteristics: academic
leaders and research teams with significant international influence;
internationally recognized research achievements in specific fields; a track
record of cultivating top-tier innovative talents who become core figures and
leaders in their disciplines; strong competitiveness in securing research
platforms, obtaining research funding, and attracting high-level talent;
irreplaceable value in advancing certain scientific or academic fields.
During this period, disciplinary assessment in
China evolved from condition- and level-based evaluations to a monitoring-based
model. Monitoring assessment refers to the continuous collection and in-depth
analysis of relevant data using modern information technology. It aims to
provide an objective basis for value judgments and scientific decision-making
by visually presenting the state of the system for multiple stakeholders. By
leveraging big data technologies, this model enables real-time and continuous monitoring,
and is thus characterized by regularity, objectivity, formative feedback, and
diverse evaluative perspectives[5].
The “Double First-Class” initiative represents a
key strategic deployment in China’s higher education development. Achieving its
goals requires a stronger focus on the substantive meaning of “first-class”. In
December 2020, the Ministry of Education, the Ministry of Finance, and the
National Development and Reform Commission issued the Provisional Guidelines
for Evaluating the Effectiveness of “Double First-Class” Construction. These
emphasized the need to establish a regular monitoring system focused on the
developmental progress of universities and disciplines, thereby forming an
integrated model of monitoring, improvement, and evaluation[6].
The
dynamic monitoring of the “Double First-Class” initiative serves as a key
mechanism for guiding its ongoing development in China. As the initiative has
deepened, education authorities have strengthened oversight of both the
construction process and its outcomes. Building upon the initial implementation
framework, dynamic monitoring and evaluation have enabled authorities to better
align strategies with the new stage of national development and to advance the
high-quality construction of “Double First-Class” institutions and disciplines.
This monitoring process emphasizes data-driven and evidence-based evaluation to
objectively reflect the current status of the initiative. The disciplinary
monitoring framework comprises five core dimensions:
1.
Progress in discipline
development,
2. Cultivation of top-tier innovative talent,
3. Building of world-class faculty,
4. Advancement in scientific research capacity,
5.
Contributions to society.
The
purpose of monitoring and assessment is threefold: to support government
agencies in overseeing the entire construction process and enabling timely
macro-level adjustments; to help universities provide real-time feedback and
accelerate internal development; and to inform the public and enhance
transparency and social oversight. By processing and analyzing large volumes of
data, the monitoring system transforms raw data into valuable information and
knowledge, effectively mapping the state of disciplinary development. This
feedback loop enables the academic community to make informed, value-based
decisions, encourages continuous improvement at all levels, and ultimately
fosters a data-driven cycle that supports the high-quality development of
disciplines.
Achieving the goal of building a strong higher
education system is essential to promoting the high-quality development of
academic disciplines. The driving force behind discipline development has
gradually shifted from externally imposed evaluation indicators to an internal
pursuit of excellence and quality. To achieve a qualitative leap from
“following” to “leading”, it is crucial to explore the inherent laws of
disciplinary development and to innovate disciplinary construction models. In
the era of digital intelligence, disciplinary assessment provides robust
technical support for high-quality development. By leveraging artificial
intelligence technologies, it enables evidence-based decision-making,
identifying key leverage points and growth areas through data-driven insights.
Challenges Facing
Discipline Development in the AI Era
The rapid iteration of AI technology has had a wide
impact on the development of disciplines. However, there is a noticeable lack
of motivation among practitioners to actively apply AI in solving problems
related to disciplinary construction. Currently, discipline development in the
AI era faces several key challenges: limited awareness and understanding of AI
among academic stakeholders; an immature integration of disciplinary
development and technological innovation; and the absence of a collaborative
ecosystem, which hinders the effective implementation of AI-powered
disciplinary construction.
(i) Ideological Constraints:
From “Practice” to “Concept”
Although AI is increasingly applied in the field of
education, many practitioners remain cautious when it comes to its use in
disciplinary evaluation—a core component of academic quality, talent
cultivation, and research performance. Skepticism and low acceptance of AI in
this context reveal a cognitive gap between technological innovation and
traditional approaches to discipline development. This gap is rooted in path
dependency and a culture of risk aversion, which together create a dual
constraint of cognitive inertia and institutional rigidity. As a result, the
application of AI remains largely theoretical, failing to a dilemma which is
“suspended implementation”.
At the cognitive level, the rigidity of traditional
disciplinary paradigms hinders the integration of AI methodologies. This is
evident in practitioners’ skepticism toward the interpretability of algorithmic
“black boxes” and their adherence to human judgment-based models of educational
management. Some discipline builders exhibit a “technical cognitive deficit”,
reflecting a structural conflict between the industrial-era model of knowledge
production and the evolving paradigm of intelligent civilization. This deficit
exposes an intergenerational disconnect in cognitive frameworks and reveals how
the mechanism of knowledge reproduction lags behind technological advancement.
Due to limited understanding of AI and insufficient
technical literacy, many practitioners question the feasibility and
effectiveness of using AI in discipline development. In addition, some regard
qualitative human experience as incompatible with quantitative algorithmic
analysis, and presuppose that “algorithmic analysis=dissolution of humanism”,
which makes practitioners of disciplinary construction panic about the
dissolution of meaning.
The internalized institutional defense mechanisms
within the system have systematically suppressed the innovative capacity of
disciplines. Excessive defensive behavior among disciplinary construction
practitioners has triggered an “Innovation Valley of Terror” effect. The
unchecked expansion of data security boundaries and the constant shifting of
responsibility have mired discipline development in stagnation, making it
difficult to clarify and implement responsibilities, rights, and interests.
Excessive risk aversion has given rise to a typical manifestation of the
“Innovation Valley of Terror”. Because AI-enabled disciplinary development
still lacks a mature operational paradigm, innovation inherently involves
risks. When data security boundaries are overstretched into an open-ended chain
of liability transfer, the willingness of stakeholders to engage in the process
is significantly weakened.
Concurrently, a sense of power transfer anxiety has
emerged, as some practitioners fear that their authority could be undermined or
even replaced by AI engineers. This deep-seated institutional anxiety stems
from a fundamental restructuring of the power dynamics over resource control.
As disciplinary development shifts from a manager-dominated model to a triadic
model involving administrators, disciplinary members, and AI technologies, the
traditional authority of academic managers over resource allocation is facing
dual challenges. At the operational level, AI engineers effectively control
resource coordination through algorithmic system design; at the discursive
level, the deep involvement of intelligent systems in academic decision-making
may trigger a “crisis of interpretive authority”.
(ii)
Technical Limitations in the Transition from
“Quantitative” to “Qualitative” Change
The core contradiction in AI-enabled discipline
development lies in the tension between the rich, multidimensional nature of
disciplinary construction and the insufficient explanatory capacity of current
technologies. This contradiction is primarily reflected in the insufficient
integration between technological mechanisms and the intrinsic logic of
disciplinary development, manifesting in the following three aspects.
First, the insufficient accumulation of
discipline-related data. The structuring of educational data is a fundamental
prerequisite for AI technologies to function effectively. However, current
research on disciplinary construction remains insufficient, and there is no
unified consensus on the key elements that should guide the development of
disciplines. As a result, process-related data lacks standardized collection
protocols. The prevalence of multi-source and heterogeneous data has led to the
emergence of serious “data silos”. Furthermore, concerns around data privacy
and limitations in data timeliness further hinder the practical performance of
AI technologies. The lag in disciplinary data has become a critical bottleneck
in AI-driven disciplinary construction, which fundamentally stems from a
systemic disconnect between the data supply side and the algorithmic demand
side.
Secondly, the technical workload involved in
constructing knowledge graphs for disciplinary development is immense.
Knowledge graphs are not merely storage containers for disciplinary knowledge;
they serve as cognitive intermediaries that reshape the modes of academic
production. The construction of knowledge graphs within disciplinary
development requires the academic community to deconstruct and reconstruct the
underlying technical logic. Essentially, this process represents a profound
dialogue between technological tools and disciplinary rationality. Therefore,
building knowledge graphs for disciplines is not a simple case of technology
transfer, but rather a digital reinterpretation of the connotations, functions,
and contemporary understanding of disciplinary development.
From the perspective of value theory, this points
to an innovation in the cognitive paradigms of the academic community. As a
digital foundation for disciplinary construction, the process of building
knowledge graphs essentially involves technically deconstructing and
reinterpreting the core of the discipline, requiring the academic community to
establish a dialectical and unified cognitive framework that reconciles
technological logic with disciplinary rationality. Ontologically, knowledge
graph engineering must overcome a fundamental paradigm shift—from disciplinary
ontology to digital representation.
The construction of disciplinary knowledge graphs
must balance completeness with feasibility. Although large language models such
as GPT-4 possess relatively reliable semantic interpretation and logical
reasoning capabilities, extensive participation from expert disciplinary
researchers and practitioners is still necessary during the training process to
validate knowledge and ensure accuracy. Evidently, the substantial training
demands present significant challenges to the advancement of AI-enabled disciplinary
development.
Third, technological limitations lead to increased
training costs. Disciplinary construction activities involve the systematic
allocation of key resources, namely personnel, funding, and materials. The
level of disciplinary development depends on the discipline’s ability to
deploy, integrate, and utilize these resources effectively. From an efficiency
perspective, disciplinary construction is a process that ultimately achieves
performance improvements through a series of inputs, including resource investment,
policy support, and management upgrades.
Current AI technologies still exhibit shortcomings
in understanding and representing complex systems, making it difficult to fully
replace the role of disciplinary construction managers, thus necessitating
further algorithm optimization. Moreover, from algorithm refinement to
practical application in disciplinary construction, AI technologies require
substantial hardware and software support, leading to high implementation costs
that some institutions or disciplines may find challenging to afford.
(iii) From “Monolithic” to
“Collaborative”: The Lack of an Ecological Synergy
In the current era, digital
technology, as a driving force behind the global scientific and technological
revolution and industrial transformation, is increasingly integrated into all
aspects and stages of economic and social development. It profoundly reshapes
production methods, lifestyles, and modes of social governance. This
integration presents both unprecedented challenges and opportunities for
education. Since China established its education informatization strategy, it
has implemented a series of policies aimed at building smart education
platforms and digitally empowering the higher education sector, thereby
advancing the digital transformation of education.
Table
1.
List of China’s Higher Education Digitalization Policies
|
Dimension |
Year |
Related Documents or Policies |
Key Deployments in Higher Education Digitalization |
|
Talent Training Methods |
2015 |
Opinions on Strengthening the
Construction, Application, and Management of Online Open Courses in Higher
Education Institutions |
Build a batch of high-quality online open courses represented by
massive open online courses, integrating course application and teaching
services. |
|
2018 |
Education Informatization 2.0 Action Plan |
By 2022, basically achieve teaching applications covering all
teachers, learning applications covering all eligible students, and digital
campus construction covering all schools; generally improve informatization
application levels and teacher-student digital competence; build a large
“Internet + Education” platform. |
|
|
2020 |
Beijing Declaration on MOOC
Development |
Established the World MOOC and Online Education Alliance and
launched 8 global open courses. |
|
|
2022 |
Launch of the National
Higher Education Smart Education Platform |
Create China’s “golden classroom” that is always online for
higher education. |
|
|
Operating Models |
2017 |
New Generation Artificial
Intelligence Development Plan |
Proposed “Intelligence Education”, emphasizing accelerating
reforms in talent cultivation models and blended teaching modes using
intelligent technologies; build a new education system including intelligent
and interactive learning. |
|
2018 |
Double Ten Thousand Plan for
First-class Course Construction |
Construct about 10,000 national-level first-class courses and
about 10,000 provincial-level first-class courses, including advanced,
innovative, and challenging courses in various formats such as online,
offline, blended, virtual simulation, and social practice. |
|
|
2021 |
The Outline of the 14th Five-Year
Plan (2021-2025) for National Economic and Social Development and Vision 2035
of the People’s Republic of China |
Promote inclusion of high-quality socialized online course
resources in public teaching systems; develop scenario-based, experiential
learning and intelligent education management and evaluation to expand
students’ digital information resources. |
|
|
Management System |
2018 |
Guidelines for the Construction and
Application of Network Learning Spaces |
Promote the “Internet +” initiative, accelerate education
informatization, support and lead education modernization, and serve the
construction of a strong education country. |
|
2021 |
5G Application “Setting Sail” Action
Plan (2021-2023) |
Vigorously promote the application of 5G in education
management, comprehensive student evaluation, and other scenarios. |
|
|
2021 |
Data Security Law of the People’s
Republic of China |
Establish a system for data classification and graded
protection. |
|
|
Support Mechanisms |
2022 |
Several Opinions on Strengthening the
Teaching Management of Online Open Courses in General Colleges and Universities |
Further clarify requirements for course quality, credit
recognition, examination norms, teacher teaching activities, and platform
supervision mechanisms for online open courses. |
Source:1
Embedding
digital technology in education governance is a strategic response to the
transformations of the times and society. In the era of digital intelligence,
educational governance is increasingly advancing toward a scientific and
intelligent development path[7]. In July 2021, the “Guiding
Opinions of the Ministry of Education and Five Other Departments on Promoting
the Construction of New Educational Infrastructure and Building a High-Quality
Educational Support System” identified information networks and digital
resources as key components of the new educational infrastructure. The Ministry
of Education of the People’s Republic of China successfully hosted two sessions
of the World Digital Education Conference in 2023 and 2024, releasing a series
of practical outcomes, including the establishment of the World Digital
Education Conference, the launch of a smart education platform, and the
publication of the Global Digital Education Development Index. The 2024
conference, held in Shanghai, represents not only a significant initiative in
the country’s education modernization efforts but also a concrete response to
the current needs of education reform. The phase of high-quality development in
higher education imposes higher demands on discipline development, with decisions
regarding discipline development determining the future direction and
ultimately the “fate” of academic disciplines.
However, in the process of transforming “ideas”
into “reality”, it is urgent to establish a multi-dimensional synergistic
ecosystem to promote the healthy development of AI-enabled disciplinary
construction. This ecosystem should include institutional synergy, resource
allocation synergy, and humanistic development synergy. First, based on a
dynamic governance framework, coordination among disciplinary construction
stakeholders is achieved through federated learning, flexible rule negotiation,
and multi-agent simulation optimization. Second, with data intelligence as the
core, integration of cross-modal cognitive computing and digital twin resources
serves as the foundation to realize multi-dimensional and precise mapping of
academic resources. Finally, paradigm integration acts as the connecting
thread, embedding disciplinary culture into every aspect of disciplinary
construction to guide personalized, precise, and intelligent discipline
development. However, these three dimensions have yet to form a truly synergistic
and healthy ecosystem for AI-enabled disciplinary construction.
Change the Mindset,
Embrace AI, and Innovate New Models for Discipline Development
General Secretary Xi Jinping emphasized in the
report of the 20th National Congress that “accelerating the construction of
world-class universities with Chinese characteristics and advantageous
disciplines” highlights the critical role these disciplines play in
implementing the strategy of strengthening the country through science and
education, as well as supporting modernization through talent development. In
February 2022, the Ministry of Education, the Ministry of Finance, and the
National Development and Reform Commission jointly issued the “Notice on the
List of Universities and Disciplines for the Second Stage of ‘Double
First-Class’ Construction” (Teaching and Research Letter [2022] No. 1),
announcing that the number of advantageous disciplines selected for this round
reached 433. In January 2025, the CPC Central Committee and the State Council
issued the “Outline of the Plan for Building a Strong Educational Nation
(2024–2035)”, which focuses on advantageous disciplines and calls for a
moderate expansion of the scope of the “Double First-Class” construction.
Furthermore, in July 2024, the CPC Central
Committee’s “Decision on Further Deepening Comprehensive Reforms and Promoting
Chinese-Style Modernization” stressed the need to establish a mechanism for
adjusting disciplinary settings driven by scientific and technological
development and national strategic demands, providing fundamental guidelines
for innovating discipline development models.
(i)
Service-oriented and Innovative New Model for Talent Training
Talent cultivation to serve
national strategic needs should fundamentally adhere to precisely aligning with
the demands of regional economic and social development, thereby achieving
synergistic quality improvement between the two. This reflects the concrete
manifestation of coordinated development between talent training and regional
economic and social progress. AI technology injects new vitality into
disciplinary construction. Enhanced computational power enables machines to
achieve intelligence, handle complex tasks, analyze complicated problems with
the help of algorithms, and understand relationships among elements of
disciplinary development through massive datasets.
By integrating and matching
diverse viewpoints, suggestions, and proposals, and combining historical
decision-making data, AI technology can accurately assess the current needs and
future directions of regional economic and social development. This enables a
dual function of presenting the status of disciplinary construction and
forecasting trends. Consequently, talent cultivation can be tightly aligned
with regional high-quality development, aiming to identify new directions for
talent training in emerging regional development trends. Within the strategic
framework integrating education, science and technology, and talent
development, the positioning of talent cultivation is clarified, maximizing the
value of talent services to regional development.
Closely integrating the needs of national strategic
development with talent cultivation, universities should dynamically establish
disciplines and majors aligned with regional high-quality development. They
should launch talent training programs that integrate multiple high-level
disciplines, guided by key development issues and major projects. Based on
this, universities can foster positive interaction between enrollment and
regional development by formulating employment-oriented admission policies and
implementing order-based talent training models. Universities should align the
direction and goals of cultivating regional innovative talents with their own
disciplinary characteristics and strengths, thereby clarifying the specific
content and methods of talent development. disciplinary construction must be
supported by training high-quality, innovative talents. AI technology should be
leveraged to ensure curriculum content closely matches regional development
needs.
Practical teaching methods such as internships and
field training should be utilized to enhance students’ practical skills and
professionalism, building a regional high-quality talent cultivation system.
The advantages of AI in identifying regional development trends should be fully
utilized. High-level talents play a key role in supporting regional
high-quality development; through establishing comprehensive regional
employment identification and guidance systems and strengthening career
counseling, graduates can receive more personalized employment services,
helping them better achieve independent employment and entrepreneurship. In
summary, disciplinary construction should rely on AI technology to establish an
integrated “enrollment–training–employment” talent development linkage model.
It should progressively adopt combined strategies, including building
advantageous disciplines, demand-driven adjustments, and information
optimization, to fully harness AI’s role in fostering innovative talent
cultivation and provide robust technical support for the systematic development
of regional innovative talents.
(ii)
Innovating a New Model for Scientific Research with the Goal of Overcoming
Challenges
First-class universities are
the main drivers of knowledge innovation. In the natural sciences and
engineering, the focus is on producing original innovations and achieving
breakthroughs in critical “bottleneck” problems, often described as moving from
“0 to 1”. In the humanities and social sciences, the primary task is to foster
theoretical innovation, establish a system of philosophy and social sciences
with Chinese characteristics, and construct an independent knowledge system for
China. Major national strategic needs are typically problem-oriented, requiring
interdisciplinary and cross-sector collaboration to tackle challenges in both
knowledge and technology. The key to building a strong higher education system
lies in focusing on national strategic demands and intensifying research
efforts on core technologies in critical fields.
AI-enabled research innovation employs a
data-driven and intelligent analysis approach to analyze unstructured data and
predict national strategic demands. It establishes a foundation for assessing
national strategic needs and clarifies the driving factors behind disciplinary
adjustments. The research mainly involves two components: demand decoding and
disciplinary response. Demand decoding transforms national strategic demands
from abstract concepts into measurable and trackable elements. This involves
constructing a data collection layer and enhancing multi-source data
acquisition. Using web crawlers and other techniques, extensive political,
economic, social, and technological information related to national strategy is
gathered to build a strategic demand prediction database. Models are programmed
to enable batch processing of current and historical data. National strategic
demands can be categorized as explicit or potential. Explicit demands are
documented and released by relevant government departments. However, each
discipline typically focuses only on documents issued by its corresponding
department, resulting in limited scope. For instance, the education sector
mainly monitors documents from the Ministry of Education but may lack
comprehensive interpretation of those from the Ministry of Industry and
Information Technology.
Therefore, a statistical platform for aggregating
and synthesizing these demands is necessary. Potential demands arise from
unforeseen events or sudden key needs, which can trigger chain reactions across
sectors. It is essential to statistically analyze these based on their
interrelations. Combining explicit and potential demands forms the foundational
dataset for national strategic demand analysis. Demand response utilizes
natural language processing (NLP) and sentiment analysis to extract keywords
from text, perform demand clustering, and clarify priority areas for national
development. Time series forecasting models such as ARIMA are applied to
predict the number of research outputs, research teams, and other relevant
metrics in these areas. Furthermore, deductive models are used to identify
disciplinary fields that the nation may need to develop to meet future
strategic demands.
AI-enabled scientific research aims to serve
disciplines by creating competitive advantages in research and addressing major
scientific challenges, focusing on identifying research gaps closely aligned
with national strategic needs. It transforms the scientific research outcomes
required in key national development areas into essential elements for
disciplinary development, thereby establishing a proactive state of
disciplinary construction that supports national strategic priorities.
(iii)
Shaping a New Model for Disciplinary Assessment Aimed at First-Class Excellence
The evaluation of academic
disciplines is closely tied to the development trajectory of higher education.
Therefore, the education evaluation system in the new era should incorporate
the contribution to regional economic and social development as a key criterion,
emphasize the application- and service-oriented nature of disciplinary
construction in meeting national strategic needs, and establish a robust
evaluation framework for building leading disciplines. This will sustainably
drive the effective enhancement of these disciplines’ capacity to support
high-quality regional economic and social development.
However,
as China advances into the stage of building a strong higher education system,
some inadequacies in disciplinary evaluation have gradually emerged. The
evaluation index system reflects a performance management mindset and overlooks
the intrinsic characteristics of disciplinary development. Although various
types of disciplinary assessments exist in China, most are
designed to cater to evaluations conducted by national education authorities
and to compete for resources. disciplinary evaluations inherently carry an official
background and hold considerable influence and authority in the public[8], compelling Chinese
universities to align their disciplinary construction efforts with the specific
requirements of these assessments. Consequently, universities primarily focus
on improving their discipline rankings, while issues such as respecting the
natural laws of disciplinary development and achieving major breakthroughs in
the discipline receive far less attention.9
Reforming
and innovating the evaluation system is the driving force and an important
pathway to accelerate the high-quality development of disciplines. The primary
purpose of evaluation is not to prove, but to improve[10]. Leveraging artificial
intelligence technology, monitoring and evaluation can guide the construction
of advantageous disciplines, accelerate the reform of education evaluation,
ensure universities maintain the correct direction, and steer disciplines toward
reasonable positioning. AI-enabled
disciplinary assessment serves as both an evaluation tool for the level of
disciplinary construction and a decision-making
tool for disciplinary development. It can formulate personalized development
strategies based on the unique characteristics of disciplines and their
development intentions.
This
real-time, online decision-making mechanism not only facilitates rapid access
to the latest information and data but also allows discipline developers to
flexibly adjust their actions, enabling bottom-up proactive adjustments to meet
the evolving needs of discipline development. In the digital intelligence era,
digital technologies realize a more efficient decision-making mechanism through
computation, greatly enhancing management efficiency. Large language models,
built upon digital and intelligent technologies, provide real-time data support
and development recommendations by understanding the relationships and
interactions among disciplines and prioritizing decision-making tasks.
Intelligent technologies far surpass traditional manual operations in
information processing speed, enabling disciplinary development decisions to
respond more swiftly to rapidly changing environments, improving decision
efficiency while reducing management costs.
AI-enabled “human-machine collaboration” in
disciplinary development decision-making can formulate personalized development
strategies based on the characteristics and development intentions of each
discipline. This real-time, online decision-making mechanism not only
facilitates rapid access to the latest information and data but also enables
university practitioners involved in discipline development to flexibly adjust
their actions. It supports bottom-up proactive adjustments to meet evolving
discipline needs, reduces management costs, and enhances management efficiency.
Conclusions
Prioritizing disciplinary construction has served
as the driving force behind China's rapid advancement in higher education.
China's approach to disciplinary development has followed a three-phase
evolution. Phase 1: index-driven consolidation-Establishing foundational
strength through evaluation metrics; Phase 2: Assessment-led
elevation-Enhancing overall quality via systematic appraisal; Phase 3:
Excellence-oriented transformation-Advancing high-quality development through
world-class initiatives.This trajectory reflects the evolving strategic focus
of disciplinary construction across different stages of China's higher
education development.
As the nation advances toward global higher
education leadership, the impetus for disciplinary construction is shifting
from externally imposed incentives (evaluation metrics) to internally generated
momentum (the pursuit of excellence). This marks China's transition into a new
era where disciplinary development progresses from index-driven to high-quality
development-oriented paradigms.
The primary barriers to AI-enabled disciplinary
construction and evaluation are ideological inertia, technological limitations,
and an underdeveloped ecosystem. Ideological inertia manifests in the rigid
closure of traditional disciplinary paradigms that impedes AI integration,
evidenced by practitioners' skepticism toward algorithmic "black box"
explainability in disciplinary development and persistent adherence to
human-experience-based models in educational administration.
Technological limitations arise from insufficient
disciplinary data accumulation, prohibitive engineering requirements for
knowledge graph construction, and elevated training costs due to technical
constraints—collectively creating financial burdens beyond the capacity of many
institutions. Ecosystem deficiencies stem from the absence of synergistic
environments across institutional coordination mechanisms, resource allocation
frameworks, and humanistic development support systems, which fail to establish
a favorable ecosystem for AI implementation.
Realizing AI-enabled disciplinary construction and
evaluation requires: first, dismantling cognitive barriers to leverage AI for
evidence-based decision-making; second, establishing multi-stakeholder
collaborative networks complemented by open resource platforms and quality
assessment mechanisms to foster integrated disciplinary development; and
ultimately, driving organizational transformation through AI-powered evaluation
frameworks that reshape assessment paradigms and cultivate new disciplinary
ecosystems. By fully harnessing artificial intelligence for monitoring and
evaluation—which steers educational assessment reform—institutions ensure
adherence to sound educational orientations while guiding disciplines toward
rational positioning.
As AI's technological dividends increasingly
permeate education, its pivotal role in talent cultivation, scientific
research, and disciplinary assessment will amplify—progressively enhancing
AI-enabled disciplinary construction and evaluation. Concurrently, greater
accessibility of disciplinary data and enhanced computational capacity will
reduce technical costs while elevating AI-driven decision-making precision,
thereby propelling high-quality disciplinary development.
Acknowledgments
Supported
by the Special Commissioned Project for the Strategic Support of Building China
into a Leading Country in Education by the Ministry of Education, titled
“Research on the Discipline Setup Adjustment Mechanism Driven by National
Strategic Needs” (24JYQG005).
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Conflict of interest:
The authors declare that they have no conflicts of interest. Authors Contribution
·
Zhanjun Wang: Conceptualization, Methodology, Project
Administration, Writing- original draft,
Writing - review & editing.
·
Zehui Zhang: Conceptualization, Methodology, Writing- original draft, Writing - review
& editing.