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.
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.
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.
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.
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.
Data acquisition and preprocessing is a core course on digital intelligence at Wuhan University that is designed to cultivate students’ understanding of data sources and improve preprocessing methods. The course aims at fostering digital thinking and literacy and enhancing intelligent computing skills. This study examined digital intelligence education and reform practices integrated into the data acquisition and preprocessing course, which covered web data, social sensing data, remote sensing data, sensor network data, unmanned aerial vehicle data, and 3D data. Moreover, the study explored the development and implementation of the course’s teaching platform, which was based on the open geospatial engine.
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.
This study provides a comprehensive bibliometric analysis of the development and current status of digital and intelligence education in medicine over the past decade, with a focus on the integration of digital technologies in professional training. Using bibliometric methods, we analyzed publications between 2015 and 2024, identifying key research themes, emerging technologies, and the contributions of leading institutions and countries. The results show a steady increase in publications, particularly from 2022 to 2024, reflecting a growing global interest in digital and intelligence education in medicine, driven by technological advancements and the COVID-19 pandemic. Key themes identified include artificial intelligence-powered personalization, virtual reality in training, deep learning for medical imaging, and the use of language models for interactive teaching. However, challenges such as disparities in global research capacity, data privacy concerns, ethical issues, and resource inequality are also highlighted. Notably, the integration of intelligent digital platforms in education has been found to be transformative, particularly in clinical training, adaptive learning, and medical diagnostics simulation. The study concludes that while digital and intelligent technologies have the potential to revolutionize medical education, addressing ethical, technical, and resource-based challenges is crucial for equitable global implementation. Future research should focus on fostering international collaboration, developing standardized frameworks, and creating inclusive, low-cost digital tools to democratize medical education, thereby improving healthcare outcomes worldwide.
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.
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.
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.
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.
