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  • REVIEW ARTICLE
    Xin Zhang, Peng Zhang, Yuan Shen, Min Liu, Qiong Wang, Dragan Gašević, Yizhou Fan
    Frontiers of Digital Education, 2024, 1(3): 223-245. https://doi.org/10.1007/s44366-024-0028-5

    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.

  • CASE REPORT
    Dan Wu, Xin Jiang, Shaobo Liang, Fei Tang, Chao Qiu, Chi Yu, Peihui Yan
    Frontiers of Digital Education, 2025, 2(1): 3. https://doi.org/10.1007/s44366-025-0042-2

    To address common challenges, such as improving teaching quality, enhancing student engagement, streamlining administrative processes, and developing more effective assessment and evaluation methods, Wuhan University has developed and deployed the “AI +” professional knowledge graph using AI, neural network, and natural language processing, thus creating a benchmark case. The implementation of the “AI +” professional knowledge graph has resulted in more refined teaching designs, more autonomous learning pathways for students, more specialized and digitalized teaching management platforms, and more scientific and standardized full-chain evaluation. The implementation provides a panoramic and dynamic representation of the development of all academic disciplines at the university, making the Luojia Online AI Intelligent Teaching Center more systematic and more intelligent. Moreover, it has accelerated the development of digital intelligence education at the university and created a comprehensive architecture of “six tiers, five dimensions, four profiles, three graphs, two achievements, and one center”, pioneering a distinctive Wuhan University model for the cultivation of top-notch innovative talents.

  • RESEARCH ARTICLE
    Ronghuai Huang, Michael Agyemang Adarkwah, Mengyu Liu, Ying Hu, Rongxia Zhuang, Tingwen Chang
    Frontiers of Digital Education, 2024, 1(4): 279-294. https://doi.org/10.1007/s44366-024-0031-x

    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.

  • RESEARCH ARTICLE
    Pingwen Zhang
    Frontiers of Digital Education, 2025, 2(1): 2. https://doi.org/10.1007/s44366-025-0041-3

    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.

  • REVIEW ARTICLE
    Yunsheng Feng
    Frontiers of Digital Education, 2024, 1(3): 215-222. https://doi.org/10.1007/s44366-024-0027-6

    The integration of digital intelligence can significantly empower and drive innovation in educational publishing. Therefore, it is important to explore comprehensive transformation strategies that address every stage and aspect of educational publishing and instructional services. China Education Publishing & Media Group Ltd. (CEPMG) has been at the forefront of exploring the application of artificial intelligence (AI) within this field, yielding notable results. As a case study of the CEPMG’s efforts in promoting digital transformation and employing next-generation AI to empower educational publishing and media, this paper summarizes practical achievements, analyzes current situation, and outlines strategic directions to provide valuable insights for advancing digital transformation in the education sector.

  • RESEARCH ARTICLE
    Dan Wu, Xinjue Sun, Shaobo Liang, Chao Qiu, Ziyi Wei
    Frontiers of Digital Education, 2025, 2(1): 6. https://doi.org/10.1007/s44366-025-0039-x

    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.

  • EDITORIAL
    Fangzheng Tan
    Frontiers of Digital Education, 2024, 1(3): 267-271. https://doi.org/10.1007/s44366-024-0016-9

    With the rapid growth of information technology, digital textbooks, as a crucial component of education digitalization, are gradually emerging as a key tool for teaching in higher education institutions. They transcend the limitations of traditional textbooks, providing a broader scope and a wealth of resources for teaching. Digital textbooks present knowledge through diverse formats, including multimedia content and interactive sessions, thereby enhancing student’s engagement and interest in learning. Furthermore, they can be updated at any time to keep abreast of the development in various disciplines and to meet the needs of the times, ensuring that students can access the latest and accurate information. In addition, digital textbooks can help promote educational equity by making quality educational resources accessible to a broader range of students. As Yang (2024) points out, integrating artificial intelligence (AI)-related courses into teaching can cultivate students’ computational thinking, data analysis, and algorithmic application skills, as well as their sense of innovation and a spirit of exploration through solving practical problems. At the same time, AI technology and Big Data can be utilized to analyze students’ learning data and provide customized learning paths and resources. Therefore, it is evident that digital textbooks in higher education play a vital and irreplaceable role in improving teaching quality and cultivating innovative talent.   By introducing the efforts and progress made by Higher Education Press (HEP) in the development of digital textbooks and resources, as well as the specific actions and initiatives in applying AI to develop digital textbooks, strengthening copyright management and protection, and pushing forward the implementation of standards for digital textbooks, this paper discusses the current status, challenges, and future directions of developing digital textbooks in higher education.

  • RESEARCH ARTICLE
    Dechen Zhan, Xue Li, Lanshun Nie, Songlin Gu, Long Zhang
    Frontiers of Digital Education, 2024, 1(3): 254-266. https://doi.org/10.1007/s44366-024-0030-y

    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.

  • SHORT COMMUNICATION
    Tianyi Sui, Jianbin Liu, Shan Jiang, Jian Xu
    Frontiers of Digital Education, 2024, 1(3): 274-278. https://doi.org/10.1007/s44366-024-0015-x

    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.

  • RESEARCH ARTICLE
    Qinfeng Xu, Yanling Li
    Frontiers of Digital Education, 2024, 1(4): 308-330. https://doi.org/10.1007/s44366-024-0036-5

    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.

  • BOOK REVIEW
    Hua Sun, Fei Feng
    Frontiers of Digital Education, 2025, 2(1): 5. https://doi.org/10.1007/s44366-025-0038-y
  • EDITORIAL
    Pingwen Zhang
    Frontiers of Digital Education, 2025, 2(1): 1. https://doi.org/10.1007/s44366-025-0050-2
  • RESEARCH ARTICLE
    Yumei Li, Yali Zou
    Frontiers of Digital Education, 2024, 1(3): 246-253. https://doi.org/10.1007/s44366-024-0029-4

    This study examined how a dialogic approach in an online media literacy class at a university in China helped to develop college students’ global awareness when the world was disrupted by the coronavirus disease (COVID-19). Using writing exemplars from students’ online dialogues and reflective journals, this article demonstrates the potentialities of an online dialogic approach to guide a sense of togetherness and critical solidarity. The digital dialogical approach provides an expanded space for students to converse with multiple voices, meditate on tensions, and rethink their own stances as citizens of their country and the world. The article also underscores the role of higher education in cultivating a sense of global community among the younger generation and bridging the ideological divide in society.

  • BOOK REVIEW
    Fei Wu
    Frontiers of Digital Education, 2024, 1(3): 272-273. https://doi.org/10.1007/s44366-024-0014-y
  • RESEARCH ARTICLE
    Xiaoyong Du, Jing Wang, Jinchuan Chen, Wei Lu, Hong Chen
    Frontiers of Digital Education, 2024, 1(4): 331-340. https://doi.org/10.1007/s44366-024-0037-4

    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.

  • RESEARCH ARTICLE
    Feng Yu, Yijun Zhao, Liying Xu, Kaiping Peng
    Frontiers of Digital Education, 2025, 2(1): 4. https://doi.org/10.1007/s44366-025-0043-1

    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.

  • RESEARCH ARTICLE
    Yaxin Tu, Jili Chen, Changqin Huang
    Frontiers of Digital Education, 2025, 2(2): 19. https://doi.org/10.1007/s44366-025-0056-9

    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.

  • RESEARCH ARTICLE
    Chao Liu, Shengyi Yang
    Frontiers of Digital Education, 2024, 1(4): 295-307. https://doi.org/10.1007/s44366-024-0035-6

    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.

  • CASE REPORT
    Fei Tang, Mo Chen, Jian Xu, Chao Qiu, Yuan Wang
    Frontiers of Digital Education, 2025, 2(1): 7. https://doi.org/10.1007/s44366-025-0040-4

    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.

  • EDITORIAL
    Frontiers of Digital Education, 2024, 1(4): 345-346. https://doi.org/10.1007/s44366-024-0034-7
  • COMMENTARY
    Xuenan Wang
    Frontiers of Digital Education, 2025, 2(2): 15. https://doi.org/10.1007/s44366-025-0052-0
  • RESEARCH ARTICLE
    Wentao Liu, Hao Hao, Aimin Zhou
    Frontiers of Digital Education, 2025, 2(2): 23. https://doi.org/10.1007/s44366-025-0060-0

    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.

  • EDITORIAL
    Frontiers of Digital Education, 2024, 1(4): 341-342. https://doi.org/10.1007/s44366-024-0032-9
  • RESEARCH ARTICLE
    Zhiang Dong, Jingyuan Chen, Fei Wu
    Frontiers of Digital Education, 2025, 2(2): 20. https://doi.org/10.1007/s44366-025-0057-8

    With the development of the Internet and intelligent education systems, the significance of cognitive diagnosis has become increasingly acknowledged. Cognitive diagnosis models (CDMs) aim to characterize learners’ cognitive states based on their responses to a series of exercises. However, conventional CDMs often struggle with less frequently observed learners and items, primarily due to limited prior knowledge. Recent advancements in large language models (LLMs) offer a promising avenue for infusing rich domain information into CDMs. However, integrating LLMs directly into CDMs poses significant challenges. While LLMs excel in semantic comprehension, they are less adept at capturing the fine-grained and interactive behaviours central to cognitive diagnosis. Moreover, the inherent difference between LLMs’ semantic representations and CDMs’ behavioural feature spaces hinders their seamless integration. To address these issues, this research proposes a model-agnostic framework to enhance the knowledge of CDMs through LLMs extensive knowledge. It enhances various CDM architectures by leveraging LLM-derived domain knowledge and the structure of observed learning outcomes taxonomy. It operates in two stages: first, LLM diagnosis, which simultaneously assesses learners via educational techniques to establish a richer and a more comprehensive knowledge representation; second, cognitive level alignment, which reconciles the LLM’s semantic space with the CDM’s behavioural domain through contrastive learning and mask-reconstruction learning. Empirical evaluations on multiple real-world datasets demonstrate that the proposed framework significantly improves diagnostic accuracy and underscoring the value of integrating LLM-driven semantic knowledge into traditional cognitive diagnosis paradigms.

  • RESEARCH ARTICLE
    Stefanie Krause, Bhumi Hitesh Panchal, Nikhil Ubhe
    Frontiers of Digital Education, 2025, 2(2): 21. https://doi.org/10.1007/s44366-025-0058-7

    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.

  • REVIEW ARTICLE
    Shaopeng Li, Weiliang Fan, Yu Zhou
    Frontiers of Digital Education, 2025, 2(1): 14. https://doi.org/10.1007/s44366-025-0051-1

    Large language models (LLMs) have transformed natural language processing with their improved performance compared with previous methods and have shown great potential to be adopted in other fields. The sequential nature of genomics data, such as deoxyribonucleic acid (DNA), ribonucleic acid (RNA), and proteins, makes it akin to human natural language, supporting the application of LLMs. Currently, LLMs have only been applied to genomic research for about four years but have already achieved significant advances in many challenging and important problems. This review summarizes the recent progress of applying LLMs in genomic research, including developing biological foundation models for protein, DNA, and RNA, as well as specialized models for interaction prediction, single-cell analysis, and structure prediction. The review discusses the challenges and potentials of adopting new advancements in LLMs for genomic applications and proposes several practical projects for integrating LLMs into genomics teaching and learning.

  • CASE REPORT
    Jun Deng, Yimeng Zhang, Tin-Man Lau, Shuhan Huang
    Frontiers of Digital Education, 2025, 2(1): 9. https://doi.org/10.1007/s44366-025-0045-z

    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.

  • RESEARCH ARTICLE
    Tianqiao Liu, Zui Chen, Zhensheng Fang, Weiqi Luo, Mi Tian, Zitao Liu
    Frontiers of Digital Education, 2025, 2(2): 16. https://doi.org/10.1007/s44366-025-0053-z

    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.

  • RESEARCH ARTICLE
    Xiaohui Xiao, Yiying Zhu, Zhao Guo, Yanzhao Ma, Zhiqiang Zhang, Like Cao, Zhao Feng, Wei Wang
    Frontiers of Digital Education, 2025, 2(1): 12. https://doi.org/10.1007/s44366-025-0048-9

    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.

  • EDITORIAL
    Frontiers of Digital Education, 2024, 1(4): 343-344. https://doi.org/10.1007/s44366-024-0033-8