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.

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.

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.

Mathematical reasoning is a fundamental aspect of intelligence, encompassing a spectrum from basic arithmetic to intricate problem-solving. Recent investigations into the mathematical abilities of large language models (LLMs) have yielded inconsistent and incomplete assessments. In response, we introduce MathEval, a comprehensive benchmark designed to methodically evaluate the mathematical problem-solving proficiency of LLMs in various contexts, adaptation strategies, and evaluation metrics. MathEval consolidates 22 distinct datasets, encompassing a broad spectrum of mathematical disciplines, languages (including English and Chinese), and problem categories (ranging from arithmetic and competitive mathematics to higher mathematics), with varying degrees of difficulty from elementary to advanced. To address the complexity of mathematical reasoning outputs and adapt to diverse models and prompts, we employ GPT-4 as an automated pipeline for answer extraction and comparison. Additionally, we trained a publicly available DeepSeek-LLM-7B-Base model using GPT-4 results, enabling precise answer validation without requiring GPT-4 access. To mitigate potential test data contamination and truly gauge progress, MathEval incorporates an annually refreshed set of problems from the latest Chinese National College Entrance Examination (Gaokao-2023, Gaokao-2024), thereby benchmarking genuine advancements in mathematical problem solving skills.

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 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.

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.

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.

This study examined the application of artificial intelligence (AI) technology in design education and its broader impact on the design industry. It analyzed the potential of AI in design and creative processes, emphasizing the importance of cultivating digital intelligence design talent. Through case studies and teaching experiments, the research revealed how AI tools can enhance design efficiency, democratize design processes, and stimulate creativity. The study addressed the limitations and challenges of AI tools in design education and offered future research directions, highlighting the importance of human-centered design, lifelong learning, and the role of higher education in integrating AI technology within design curricula.

In the era of the digital economy, the Internet marketing course, a core component of the marketing curriculum in higher education, has become increasingly critical. However, the traditional teaching practices of the course have faced challenges, including insufficient depth in theoretical understanding, limited flexibility in translating theory into practice, inadequate alignment with contemporary trends, and a lack of adaptability to rapidly evolving environments. To address these issues, the teaching team at Wuhan University redesigned the course across five dimensions, including depth, rigor, intensity, breadth, and resilience. Leveraging a “four-in-one” teaching resource system, the course adopted an innovative teaching methodology grounded in the motivation, opportunity, and ability (MOA) framework. This method stimulated students’ intrinsic learning motivation, fostered collaborative creativity, and promoted mutual growth. It empowers students to develop self-management capabilities and establishes a student-centered learning paradigm characterized by shared responsibility, co-creation, and collective ownership. The teaching model ultimately seeks to cultivate high-quality and interdisciplinary talents in online marketing who are equipped with the entrepreneurial, innovative, and creative competencies necessary to meet the demands of the digital economy.

The rapid advancement of AI technology has significantly impacted higher education, presenting both opportunities and challenges for teaching and learning. Although students can benefit from AI, many remain unaware of its potential utility. Moreover, concerns regarding the accuracy and reliability of AI complicate its proper use. This study incorporated an AI teaching assistant, called Blueink, into a biochemistry course at a medical university in China. The researchers assessed the alterations in individuals’ knowledge, AI use, and critical thinking skills by conducting a review before and after the training. This was done to furnish valuable information for academics and specialists. The findings presented that the participating college students perceived AI as increasingly essential for contemporary learning and excelled at discovering significant facts using AI techniques. However, their confidence in AI responses and their habits and preferences for posing inquiries remained unchanged after the training. The study indicates that AI tools not only enhance students’ skill acquisition but require greater clarity and proficiency. Collaborating with diverse specialists can yield superior AI tools for education.

Open-source large language models (LLMs) research has made significant progress, but most studies predominantly focus on general-purpose English data, which poses challenges for LLM research in Chinese education. To address this, this research first reviewed and synthesized the core technologies of representative open-source LLMs, and designed an advanced 1.5B-parameter LLM tailored for the Chinese education field. Chinese education large language model (CELLM) is trained from scratch, involving two stages, namely, pre-training and instruction fine-tuning. In the pre-training phase, an open-source dataset is utilized for the Chinese education domain. During the instruction fine-tuning stage, the Chinese instruction dataset is developed and open-sourced, comprising over 258,000 data entries. Finally, the results and analysis of CELLM across multiple evaluation datasets are presented, which provides a reference baseline performance for future research. All of the models, data, and codes are open-source to foster community research on LLMs in the Chinese education domain.

As a data-driven analysis and decision-making tool, student portraits have gained significant attention in education management and personalized instruction. This research systematically explores the construction process of student portraits by integrating knowledge graph technology with advanced data analytics, including clustering, predictive modelling, and natural language processing. It then examines the portraits’ applications in personalized learning, such as student-centric adaptation of content and paths, and personalized teaching, especially the educator-driven instructional adjustments. Through case studies and quantitative analysis of multimodal datasets, including structured academic records, unstructured behavioural logs, and socio-emotional assessments, the research demonstrates how student portraits enable academic early warnings, adaptive learning path design, and equitable resource allocation. The findings provide actionable insights and technical frameworks for implementing precision education.

With the rapid development of online education, cognitive diagnosis has become a key task in intelligent education, particularly for student ability assessments and resource recommendations. However, existing cognitive diagnosis models face the diagnostic system cold-start problem, whereby there are no response logs in new domains, making accurate student diagnosis challenging. This research defines this task as zero-shot cross-domain cognitive diagnosis (ZCCD), which aims to diagnose students’ cognitive abilities in the target domain using only the response logs from the source domain without prior interaction data. To address this, a novel paradigm, large language model (LLM)-guided cognitive state transfer (LCST) is proposed, which leverages the powerful capabilities of LLMs to bridge the gap between the source and target domains. By modelling cognitive states as natural language tasks, LLMs act as intermediaries to transfer students’ cognitive states across domains. The research uses advanced LLMs to analyze the relationships between knowledge concepts and diagnose students’ mastery of the target domain. The experimental results on real-world datasets shows that the LCST significantly improves cognitive diagnostic performance, which highlights the potential of LLMs as educational experts in this context. This approach provides a promising direction for solving the ZCCD challenge and advancing the application of LLMs in intelligent education.

The rapid development of artificial intelligence technology has propelled the automated, humanized, and personalized learning services to become a core topic in the transformation of education. Generative artificial intelligence (GenAI), represented by large language models (LLMs), has provided opportunities for reshaping the methods for setting personalized learning objectives, learning patterns, construction of learning resources, and evaluation systems. However, it still faces significant limitations in understanding the differences in individual static characteristics, dynamic learning processes, and students’ literacy goals, as well as in actively differentiating and adapting to these differences. The study has clarified the technical strategies and application services of GenAI-empowered personalized learning, and analyzed the challenges in areas such as the lag in theoretical foundations and lack of practical guidance, weak autonomy and controllability of key technologies, insufficient understanding of the learning process, lack of mechanisms for enhancing higher-order literacy, and deficiencies in safety and ethical regulations. It has proposed implementation paths around interdisciplinary theoretical innovation, development of LLMs, enhancement of personalized basic services, improvement of higher-order literacy, optimization of long-term evidence-based effects, and establishment of a safety and ethical value regulation system, aiming to promote the realization of safe, efficient, and sustainable personalized learning.

Generative artificial intelligence (GenAI) models, such as ChatGPT, have rapidly gained popularity. Despite this widespread usage, there is still a limited understanding of how this emerging technology impacts different stakeholders in higher education. While extensive research exists on the general opportunities and risks in education, there is often a lack of specificity regarding the target audience—namely, students, educators, and institutions—and concrete solution strategies and recommendations are typically absent. Our goal is to address the perspectives of students and educators separately and offer tailored solutions for each of these two stakeholder groups. This study employs a mixed-method approach that integrates a detailed online questionnaire of 188 students with a scenario analysis to examine potential benefits and drawbacks introduced by GenAI. The findings indicate that students utilize the technology for tasks such as assignment writing and exam preparation, seeing it as an effective tool for achieving academic goals. Subsequent the scenario analysis provided insights into possible future scenarios, highlighting both opportunities and challenges of integrating GenAI within higher education for students as well as educators. The primary aim is to offer a clear and precise understanding of the potential implications for students and educators separately while providing recommendations and solution strategies. The results suggest that irresponsible and excessive use of the technology could pose significant challenges. Therefore, educators need to establish clear policies, reevaluate learning objectives, enhance AI skills, update curricula, and reconsider examination methods.

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.