The global educational knowledge production has long been constrained by an unequal international landscape, manifesting in disparities of both “quantity” and “quality”. The former is manifested in the overwhelming advantage of developed countries and dominant languages in the number of articles published and cited in the international academic community, while the latter is reflected in the monopoly of Western research, paradigms and theories in research and application, forming an unequal division of labor in the educational knowledge production and obscuring the diversity of local educational knowledge. Behind this is the power structure inherent in language, compelling scholars from the Global South to conform to dominant languages and discourses at the expense of the precision and vibrancy of their native languages. The rise of large language models (LLMs) presents a technological opportunity to lower barriers and facilitate the participation of scholars from the Global South in knowledge production in their native languages. However, technical risks and challenges such as “non-neutrality” “incompleteness” “lack of depth” “ambiguity” and “errors” cannot be overlooked. In response, this study proposes the construction of an “Inclusive Community for Global Educational Knowledge Production” and the initiative of the “Speak in Tongues” project. By advocating for cross-cultural collaboration and data sharing to optimize LLM training, as well as emphasizing technological empowerment and critical reflection, the study seeks to achieve pluralistic, equitable, and sustainable development in global educational knowledge production within the process of decolonization.
Artificial intelligence is driving a transformation in knowledge production from traditional inductive-deductive models to data-driven and algorithm-generated systems. This shift profoundly alters the way knowledge is generated, organized, and validated, while also reshapes subject cognition and application logic. Although this transformation enhances the dynamism and interconnectedness of knowledge, it also poses new challenges in terms of reliability, ethical standards, and equity. Education systems must adapt to this trend, evolve from static knowledge transmission to dynamic competence development, promote human-machine collabo-rative teaching, increase algorithmic transparency, and safeguard data privacy to mitigate inequities in technology use. To support the sustainable development of intelligent education, this study proposes to make systematic adaptation across four dimensions: reconstructing value rationality, innovating instrumental rationality, optimizing distributive justice, and strengthening ethical governance, and build an educational ecology that integrates technology and humanities. Future research should focus on the evolution of knowledge verification mechanisms, the cultural adaptability of educational assessments, and the operationalization of ethical principles into practical guidelines, so as to promote the coordinated development of cognitive expansion and human well-being in the process of intelligent education.
Knowledge production is an important force driving the progress of human society, and the transformation of the knowledge production models has a profound impact on the development direction of education. From the perspective of historical materialism, the development of material technology is the fundamental driving force for the evolution of knowledge production. To this end, this study uses the transformation of technological social forms as the clue for analysis, with media technology and the industrial revolution as the main markers, systematically sorting out the historical context of the transformation of knowledge production models, analyzing the new phenomena of knowledge production in the information age, and revealing that the historical trend of the transformation of knowledge production models is from the emergence of individuals to the emergence of collective intelligence. On this basis, the study, based on the theory of complex systems, analyzes the five main characteristics of the emergence of collective intelligence: contextual complexity, spatial openness, subject diversity, mode coordination, and non-linear effects. Finally, the article summarizes the value and implications of the emergence of collective intelligence for educational reform and innovation at the micro-level of curriculum form, the meso-level of supply mode, and the macro-level of educational ecology, in the hope of providing theoretical reference for educational reform and innovation.
The digital transformation of education refers to the use of digital technology to empower education and shape a new form of education under the value of serving learning. The digital transformation of education implies the action metaphors of advancing the transformation work in problem-driven and demand-driven approaches, shaping the foundation of transformation through the two-way connection of technology integration and data empowerment, constructing the transformation focus through digital technology serving learners’ learning, and forming transformation safeguards through the synchronous organizational and cultural changes. The need to transcend the characteristics of the educational information era lies in changing the carrier of “reproduced knowledge” through “resource-teaching-teacher”. It involves breaking through “quality-equity-innovation” to update the cognition of “reproduced knowledge”, focusing on the instrumental application of information technology to strengthen the value orientation of “reproduced knowledge”, and shifting towards “reconstructing learning” supported by digital technology. “Reconstructing learning” takes improving learning quality as its value orientation and technology combination evolution and mutation creation as its theoretical principles. Its practical implications point to the application of digital technologies in high-quality learning. It can promote the concept of serving efficient knowledge dissemination to serving high-quality learning, construct new learning theories from “knowledge reproduction” to learning engineering solutions, shape new learning forms from teaching interaction to the integration of multiple subjects, establish new learning content generated from online learning content to digital learning ecosystem, and promote it from serving teaching and learning to serving students’ comfortable learning experience and upgrading digital governance.
The process of digitalization of education challenges the realization of the right to education of the digitally disadvantaged. In the collection, analysis and application of educational data, the inequalities embedded in the social structure and the characteristics of digital technology combine to limit the realization of the right to education of the digitally disadvantaged，which not only reduces their ability to participate in the process of digitalization of education，but also generates new discriminatory and disciplinary effects. The right to education is a fundamental right guaranteed by the Constitution, with the dual attributes of “subjective right” and “objective law”. Taking into account the characteristics of the digitalization of education, and oriented by the demands of the digitally disadvantaged in terms of equality and freedom of education, it is necessary to implement the State’s obligation to respect, protect and pay for the right to education of the digitally disadvantaged. The State should recognize the disadvantages of the digitally disadvantaged at the normative level, promote the digital transformation of education in a targeted manner, and construct a legal protection system in which legislation, administration and the judiciary converge to provide relief in an orderly manner, so as to ameliorate the disadvantages of the digitally disadvantaged in terms of their material resources, abilities and awareness through the State’s provision of assistance to the digitally disadvantaged.
China is currently in a critical period of digital transformation in education and the implementation of a new round of compulsory education curriculum reform. Research on teachers’ classroom teaching behavior in the digitalization process holds political, strategic and educational value attributes. This study systematically explains the origin of teachers’ classroom teaching behavior and the transformation of teachers’ classroom teaching behavior through technological evolution based on the historical field. Through literature research, content analysis, and in-depth interviews, the research reveals practical dilemmas in this field. In terms of theoretical construction, there is a scarcity of comprehensive improvement models oriented towards core competencies in technology-enhanced environments. In terms of content interpretation, there is a lack of in-depth analysis of teaching behaviors throughout the entire process. In terms of relationship characterization, there is insufficient coupling logic in systematically analyzing influencing factors. In terms of practical progress, there is poor universal effectiveness of systematic evidence-based practical strategies. In this regard, path selection should be made from four aspects: theoretical logic, relational logic, action logic, and transformation logic, in order to better promote the high-quality development of classroom teaching in the digital age.
Educational equity is one of the key and challenging issues in education. The traditional “one to many” teacher-student relationship often fails to provide disadvantaged students with optimal value-added development, which can not bridge educational gaps. As a new subject emerging in education, artificial intelligence (AI) technology holds the potential to provide learners with precise feedback through adaptive learning models, thereby empowering high-quality personalized learning processes and overcoming the impact of learners’ ascribed factors and foundational differences in learning conditions, enhancing the role of acquired factors, so as to promote educational equity. The realization of this vision depends on the full and balanced development of the three key subjects—teachers, students, and AI. However, in practical terms, issues such as the the big data models, computational power and algorithm differentiation of AI, variations in teachers’ TPACK levels and disparities in students’ feedback literacy can all lead to feedback imba-lances in personalized learning supported by adaptive learning models, which fails to meet the interests of the “least beneficiaries” and triggers a backlash against edu-cational equity. To this end, efforts should focus on four aspects: establishing a concept of inclusive technology supply, providing teachers with technical pedagogical training, emphasizing the improvement of students’ feedback literacy, and constructing supporting technology policy supervision systems, so as to prevent the risk of artificial intelligence technology backlashing against educational equity.
Seeking effective governance is the common goal of the shared governance of modern universities. In the information age, there are problems such as “isolated data-islands” and insufficient trust among governance bodies in the shared governance of universities. The fusion of “blockchain and block-data” is the mutual support of their technology and the cores of their concepts, showing a partially decentralized or multi-centered, interconnected, open and shared structure, which is similar to the shared governance of universities in structure and complements each other in practice. The fusion of blockchain and block-data can help solve the problem of university data sharing from the level of information technology, put the responsibilities of different governance bodies, departments or institutions of universities on the chain, make different governance bodies jointly participate in shared governance, enhance mutual trust, facilitate scientific evaluation, promote evidence-based decision-making, and improve the scientific nature of university decision-making. The results of decision-making also provide directions for newer or deeper data-sharing or data-aggregation, and responsibilities of multiple governance bodies on the chain, realizing a closed loop of “data sharing and responsibilities on the chain-information symmetry and mutual trust-scientific evaluation-evidence-based decision-making”, therefore improving the governance process of universities. While blockchain and block-data drive the effective shared governance of universities, they also face challenges such as the lack of relevant professional and technical talents, how to realize the good interaction between information technology and bureaucracy, and unknown data and information security issues.
