Generative artificial intelligence (GenAI), achieving human-like capabilities in interpreting, summarising, creating, and predicting language, has sparked significant interest, leading to extensive exploration and discussion in educational applications. However, the frontline practice of education stakeholders or the conceptual discussion of theorists alone is not sufficient to deeply understand and reshape the application of GenAI in education, and rigorous empirical research and data-based evidence are essential. In the past two years, a large number of empirical studies on GenAI in education have emerged, but there is still a lack of systematic reviews to summarise and analyse the current empirical studies in this field to evaluate existing progress and inform future research. Therefore, this work systematically reviews and analyses 48 recent empirical studies on GenAI in education, detailing their general characteristics and empirical findings regarding promises and concerns, while also outlining current needs and future directions. Our findings highlight GenAI’s role as an assistant and facilitator in learning support, a subject expert and instructional designer in teaching support, and its contributions to diverse feedback methods and emerging assessment opportunities. The empirical studies also raise concerns such as the impact of GenAI imperfections on feedback quality, ethical dilemmas in complex task applications, and mismatches between artificial intelligence (AI)-enabled teaching and user competencies. Our review also summarises and elaborates on essential areas such as AI literacy and integration, the impact of GenAI on the efficiency of educational processes, collaborative dynamics between AI and teachers, the importance of addressing students’ metacognition with GenAI, and the potential for transformative assessments. These insights provide valuable guidelines for future empirical research on GenAI in education.

With the advent of the digital age, cultivating students’ digital media literacy has become an important educational mission. A standardized digital media literacy scale was developed to study the digital media literacy of primary school students in urban and rural areas. Through stratified random sampling, a total of 2,848 urban and rural primary school students participated in this research. Primary school students exhibited moderate proficiency in digital media literacy, with a notable deficiency in their ability to create and disseminate information effectively. Subsequent investigations revealed disparities in the level of digital media literacy between urban and rural students, which appeared in their proficiency to access and use digital tools, as well as their ability to understand and evaluate media messages. Parental education, parental mediation, and time spent using digital devices all have a substantial positive influence on students’ digital media literacy, with parental mediation having the greatest impact. Consequently, it is imperative to prioritize the development of higher-order digital media literacy skills in students. Efforts should also be directed toward enhancing the basic digital media literacy of rural primary school students while fostering parental engagement in students’ digital education. In this regard, the Chinese government, enterprises, and schools have launched measures to promote rural students’ digital media literacy and parents’ involvement in students’ digital education. Future research should prioritize investigations of the efficacy of these measures to ascertain their impact on the holistic development of digital media literacy among all students.

The digital transformation is driving profound changes in education and teaching, with immersive technologies such as virtual reality (VR), augmented reality (AR), and mixed reality (MR), representing the future form of the internet, set to lead a new round of innovation in primary and secondary education applications. This study first elaborates on the connotation and typical characteristics of immersive technologies, analyzes their potential applications in primary and secondary education scenarios such as space, resources, curriculum, models, and evaluation. It then points out the key issues in the application of immersive technologies in primary and secondary education from four aspects: educational system, innovative technology, application orientation, and privacy ethics. Finally, from the perspective of multi-party collaboration among government departments, educational entities, industries, and enterprises, we propose application strategies such as top-level planning and design, core technology research, typical case cultivation, and regulatory governance. Such strategies aim to promote the deep integration of immersive technologies and primary and secondary education, shaping a new educational climate that aligns with the talents demands of the digital era.

This study explores the complex dualities in digital education, focusing on the case study of Singapore. It highlights the ethical issues surrounding the integration of information and communication technology (ICT), especially artificial intelligence, in the education sector. The paper presents a theoretical framework to explore these dualities, examining how they have been navigated in Singapore’s policy reforms to enhance digital education. These dualities include centralisation vs. decentralisation of resource orientation; customisation vs. standardisation of curriculum, formal vs. informal learning with respect to pedagogical approaches; human agency vs. technological automation for data interpretation; and peaks of excellence vs. equity in achievement outcomes. These aspects significantly impact the outcomes of ICT-enabled reforms. The study draws upon Singapore’s longitudinal trajectory of integrating ICT in education, illustrating its efforts in reconciling these dualities. The findings underscore the importance of careful consideration and balance in integrating ICT in education, emphasising the need to transcend these dualities to build a more inclusive digital learning environment.

This paper proposes a novel approach to use artificial intelligence (AI), particularly large language models (LLMs) and other foundation models (FMs) in an educational environment. It emphasizes the integration of teams of teachable and self-learning LLMs agents that use neuro-symbolic cognitive architecture (NSCA) to provide dynamic personalized support to learners and educators within self-improving adaptive instructional systems (SIAIS). These systems host these agents and support dynamic sessions of engagement workflow. We have developed the never ending open learning adaptive framework (NEOLAF), an LLM-based neuro-symbolic architecture for self-learning AI agents, and the open learning adaptive framework (OLAF), the underlying platform to host the agents, manage agent sessions, and support agent workflows and integration. The NEOLAF and OLAF serve as concrete examples to illustrate the advanced AI implementation approach. We also discuss our proof of concept testing of the NEOLAF agent to develop math problem-solving capabilities and the evaluation test for deployed interactive agent in the learning environment.

Large language models (LLMs) have emerged as powerful tools in natural language processing (NLP), showing a promising future of artificial generated intelligence (AGI). Despite their notable performance in the general domain, LLMs have remained suboptimal in the field of education, owing to the unique challenges presented by this domain, such as the need for more specialized knowledge, the requirement for personalized learning experiences, and the necessity for concise explanations of complex concepts. To address these issues, this paper presents a novel LLM for education named WisdomBot, which combines the power of LLMs with educational theories, enabling their seamless integration into educational contexts. To be specific, we harness self-instructed knowledge concepts and instructions under the guidance of Bloom’s Taxonomy as training data. To further enhance the accuracy and professionalism of model’s response on factual questions, we introduce two key enhancements during inference, i.e., local knowledge base retrieval augmentation and search engine retrieval augmentation during inference. We substantiate the effectiveness of our approach by applying it to several Chinese LLMs, thereby showcasing that the fine-tuned models can generate more reliable and professional responses.

Higher education systems are under increasing pressure to embrace technology-enhanced learning as a meaningful step towards the digital transformation of education. Digital technologies in education promise optimal teaching and learning, but at the same time, they put a strain on education systems to adapt pedagogical strategies. Classical pedagogical frameworks such as Dewey, Piaget, and Vygotsky’s theories focused on student agency and are not specific to contemporary education with ubiquitous digital technologies. Hence, there is a need for a novel and innovative pedagogical framework that aligns with these emerging and advanced digital technologies. However, recent guidelines to incorporate emerging digital technologies in education have largely focused on ethical dimensions and assessment practices. The lack of an overarching pedagogical framework for teaching and learning practices in the digital era is a threat to quality education. The current study proposes a digital pedagogy for sustainable educational transformation (DP4SET) framework applicable to the new modes of teaching and learning powered by digital technologies. The DP4SET framework comprises four components that advocate for digital competence for accessing deep learning, evidence-based practice with quality digital resources, learning environments with applicable digital technology, and synergy between human teachers and trustworthy artificial intelligence (AI). A real-world application of the DP4SET framework in Chinese contexts proves that it promotes the effective use of technology and significantly reshapes teaching and learning in and beyond the classroom. The proposed digital pedagogy framework provides a foundation for modern education systems to accommodate advanced digital technologies for sustainable digital transformation of education.

After the Overall Plan for Deepening the Reform of Education Evaluation in the New Era has been released for over two years, the reform of education evaluation has achieved a good start and important phased outcomes. Promoting the digital transformation of education evaluation and developing Big Data-based education evaluation are the main measures of current evaluation reform. Based on the case study of the Minzu University of China, this paper systematically sorts out the relevant research, constructs the factor model and process model of Big Data-based education evaluation from the perspectives of factors and process of evaluation, puts forward the application idea of Big Data-based education evaluation from the perspectives of full business, full process and full factors, and puts forward the practical path of Big Data-based education evaluation from the aspects of application traction, teacher training and safe operation.

Amid the digital revolution, this research explores a groundbreaking topic—the potential impact of metaverse services on the future of computing and engineering education. The transformative potential of metaverse services in education is a beacon of the future, promising new learning modes in digital environments. This work poses two questions: Will metaverse services affect computing and engineering education learning? If so, to what extent has computing and engineering education adopted metaverse services in its curricula? To address these queries, the authors researched several metaverse activities affecting computing and engineering education. The new concepts of metaverse services, metaverse education services, and metaverse education service space are presented and analyzed. This research also discusses the influences of metaverse and services on computing and engineering education. The research showed a transformation toward metaverse service education in the evolving digital era. Academic and industry professionals must recognize the critical need to prepare students and graduates for the digital era adequately. The future is coming whereby metaverse, higher education, and services will generate a new destiny for computing and engineering education with new learning modes in digital environments. The transformative potential of metaverse services in education cannot be overstated, and the academic and industry communities must recognize and embrace this phenomenon.

The creation and application of massive open online courses, online and offline blended courses, and AI-empowered courses drive the reform in higher education. In this context, the establishment of grassroots teaching organisations should be increasingly promoted. By leading more schools and teachers to more efficiently develop courses and effectively implement teaching reforms through cross-regional and cross-university grassroots teaching organisations, virtual teaching and research section (VTRS) has emerged as a new means to explore the creation of such organisations in the “Internet +” era. This paper introduces the background of the VTRS proposal, analyses the connotations of three types of VTRS, and explains seven characteristics of VTRS. Next, it proposes a VTRS construction framework that involves team building, platform construction, mechanism construction, and content construction. Finally, using computational thinking virtual teaching and research section as an example, this paper introduces the construction cases and methods for VTRS. As a new model of collaborative teaching and research, VTRS will improve teaching skills and research engagements of university teachers and will enhance teaching management and professional development in universities.

New quality productive forces characterized by innovation and innovation-driven development are essentially talent-driven. The cultivation of engineering practice and innovation ability of science and engineering talents is closely related to the development of national science and technology. It serves as a decisive factor in seizing opportunities of the new round of scientific and technological revolution and industrial change. To align digital engineering graphics education with the evolving demands of industries driven by new quality productive forces and engineering practice, this paper proposes teaching methods based on digital teaching practices at Tianjin University. These methods aim to deepen students’ understanding of new quality productive forces in engineering practice. The knowledge map of new quality productive forces is designed to enhance students’ innovation ability based on cartographic knowledge. It achieves this by refining typical engineering application scenarios that address global scientific and technological issues, tackle economic challenges, meet national strategic needs, and improve people’s life and health. This approach aims to cultivate innovative engineering and technical talents with a solid theoretical foundation, comprehensive innovation abilities, and strong engineering practice ability.

Education digitalization is an inevitable trend of technological development. Based on the theories related to smart classrooms, this research constructs a “3 + 5” teaching model and implements a mixed methods research in Jinshan Elementary School in Chongqing. The “3” in the name refers to three stages of teaching, namely before, during, and after class. The “5” in the name refers to five links of teaching, namely prediction, fine-tuning, detailed explanation, intensive support, and extension. Through questionnaire and interview, it is found that most students and teachers are very satisfied with the “3 + 5” teaching model. The model based on the iFLYTEK smart classroom can accurately locate students’ learning situation, improve classroom efficiency, develop personalized learning plans, and provide data support for the digital transformation of primary school mathematics.

The teaching and research section is the fundamental organizational unit for teaching and research in a university, and the virtual teaching and research section (VTRS) is crucial for the exploration of the digital transformation of new basic teaching organization construction in the information age. However, this new type of organization transcends institutional and spatial boundaries, and motivating participants and sustaining their engagement are key challenges in VTRS implementation. The VTRS for database courses (VTRS-DB) proposes an open community-based operating model, founded on the core concepts of openness, dedication, competition, and orderliness. It establishes a hierarchical organizational structure and working group operation mechanism. After two years of practical exploration, a course knowledge graph and a wealth of teaching experiment cases have been developed. A series of distinctive teaching and research methods, such as collaborative course preparation, have been implemented, and the domestic database in the classroom brand activity has been established. The VTRS-DB has incubated several national and provincial level first-class courses and has won national and provincial level teaching achievement awards, achieving significant results.

This study investigates the application of a support vector machine (SVM)-based model for classifying students’ learning abilities in system modeling and simulation courses, aiming at enhancing personalized education. A small dataset, collected from a pre-course questionnaire, is augmented with integer data to improve model performance. The SVM model achieves an accuracy rate of 95.3%. This approach not only benefits courses at Guizhou Minzu University but also has potential for broader application in similar programs in other institutions. The research provides a foundation for creating personalized learning paths using AI technologies, such as AI-generated content, large language models, and knowledge graphs, offering insights for innovative educational practices.

Cultivating talents in robotics requires the integration of multiple disciplines, including mechanical engineering, electronics, computer science, and control engineering. The rapid expansion of the robotics industry in recent years has highlighted a significant talent gap and compelled universities to raise the standards of talent development in this field. This research examines the distinctive features of talent cultivation in robotics, draws on the practices of Wuhan University’s intelligent robotics program, and incorporates the concept of digital-intelligent education to propose an innovative talent cultivation framework termed system reconstruction and a fourfold integration education. This research emphasizes the importance of digital-intelligent interdisciplinarity and reports on the establishment of a progressive and comprehensive professional curriculum system. It also presents a supporting model that includes research-activated education, industry-driven education, competition-enhanced education, and interdisciplinary education, thereby creating a project-driven innovation practice platform and talent cultivation mechanism. Guided by systematic reconstruction and fourfold integration education mechanism, the digital-intelligent interdisciplinary curriculum and project-driven practice platform have significantly improved students’ professional knowledge, innovative ability, and sense of social responsibility. This mechanism has not only improved the quality of talent cultivation in intelligent robotics but has also increased the impact of academic competitions and garnered widespread acclaim from peers.

Empowered by the rapid advancement of digital technologies, including Big Data, artificial intelligence (AI), and virtual reality, human society has transformed from the era of information to the era of digital intelligence. Unlike previous social formations, the digital-intelligent society has disrupted many long-held consensus norms and introduced numerous difficult challenges. To cultivate adaptive talents with general literacy of digital intelligence and specific professional competences, psychology, as one of the foundations of social sciences, must launch a revolution in future-oriented education. In higher education, the two principal components, defined by their nature and objective, are knowledge-oriented and research-oriented teaching. The former is designed to provide an introduction to the fundamental principles and basic knowledge of psychology for freshmen and sophomores, while the latter is intended to equip junior and senior undergraduates with the skills necessary for conducting scientific research. First, it is both possible and necessary to integrate AI throughout the processes of knowledge-oriented teaching. In this article, we propose a “loop model” to demonstrate the applications of AI in the knowledge-oriented phase. Furthermore, to provide a reference criterion for nurturing innovative and research-oriented students, we present a theoretical framework of “chimeric research” to provide a comprehensive overview of psychology research in the era of AI. In conclusion, psychology education needs to be aligned with the demands of the modern society and embrace digital intelligence in both knowledge- and research-oriented teaching phases.

In the current era of rapid development of AI and Big Data, utilizing these emerging technologies to empower learning in specialized higher education courses in the electrical engineering discipline has become a hot topic among scholars. This paper constructs a ternary graph comprising knowledge, issue, and competency layers, based on knowledge graphs. Combining knowledge graphs with the instructional design of flipped classrooms and double closed-loop teaching designs, students’ learning enthusiasm and efficiency can be fully unleashed. In the practical teaching of fundamentals of electrical engineering course, students’ learning abilities, innovative thinking skills, and interpersonal coordination competencies significantly improved.

The Ministry of Education of the People’s Republic of China has proposed the deep integration of digital intelligence (DI) technologies into the higher education system to achieve a fundamental transformation. In response to the global imperative for digital transformation in higher education, the research investigates how Wuhan University systematically implements DI technologies across teaching, management, and service to cultivate innovative talents. With a focus on talent development, Wuhan University has built an integrated teaching platform and developed a DI education evaluation system. The research offers practical insights for higher education institutions navigating digital transitions and advancing global DI education practices. By fully integrating DI technologies and concepts into all aspects of teaching, management, and service, the reform aims to create a new synergy between DI technologies and the higher education system. This integration enhances the university’s abilities to seize opportunities and meet challenges in the DI era, thereby providing comprehensive support for cultivating top innovative talents.

As artificial intelligence (AI) technology continues to evolve in the digital era, developing AI literacy among college students has become a crucial educational priority. This study aims to establish a scientific AI literacy evaluation system and to empirically assess the AI literacy levels of undergraduate students at Wuhan University, with the findings providing data support and theoretical reference for future AI education policy-making and curriculum design in higher education institutions. In response to the demands of AI education and university talent cultivation objectives, this study develops an AI literacy evaluation system for college students, based on the KSAVE (knowledge, skill, attitude, value, and ethics) model and the UNESCO AI competency framework. The system includes 4 level-1 indicators (AI attitude, AI knowledge, AI capability, and AI ethics), 10 level-2 indicators, and 25 level-3 indicators. The Delphi method was used to determine indicator content, while the analytic hierarchy process was employed to calculate the weights for each level of indicators. Through large-scale questionnaire surveys and statistical analysis, the study empirically measured the AI literacy levels of 1,651 undergraduate students at Wuhan University and analyzed variations in AI literacy across factors including gender, academic year, academic discipline, and technical background. The results demonstrate that the constructed AI literacy evaluation system is scientifically sound and highly applicable, providing a comprehensive and objective measure of students’ AI literacy levels. Furthermore, notable differences were observed in AI literacy levels across different dimensions among Wuhan University undergraduates, with variables such as academic discipline, technical background, and participation in digital intelligence education programs significantly influencing students’ AI literacy, particularly in knowledge and capability dimensions.