Introduction

Recently, General Secretary Xi Jinping sent a letter to all faculty and students of four transportation universities, encouraging them to uphold the educational philosophy of “seeking practical knowledge and engaging in practical work,” inherit and promote the spirit of the Westward Movement, focus on major national strategic needs, strengthen independent technological innovation and talent cultivation, and achieve more breakthroughs in promoting deep integration of industry, academia, and research, contributing to building a strong educational, technological, and talent nation. Schools are deeply learning, promoting, and implementing the important spirit of Xi Jinping’s letter, firmly grasping the inherent consistency and mutual support of educational development, technological innovation, and talent cultivation, continuously transforming educational advantages, talent advantages, and innovation advantages into development advantages, competitive advantages, and strategic advantages.

Currently, artificial intelligence is reconstructing human society’s production and lifestyle at an unprecedented speed. Securing a leading position in global AI development has become an important support for China to build international competitive advantages and win in great power competition. Ultimately, technological competition is a competition for talent and education. In the face of this unprecedented transformation, we recognize that AI education is facing “three structural challenges,” but AI itself provides a new solution to break barriers and reconstruct paradigms. The “AI + Education Action Plan,” jointly issued by five departments including the Ministry of Education, clearly requires leveraging AI to empower educational transformation and proposes specific requirements such as “building AI learning communities and gathering open-source courses” and “conducting achievement certification to encourage faculty and students to participate in open-source ecosystem construction,” providing direction and deployment for AI education reform. Shanghai Jiao Tong University focuses on cultivating high-quality talent suitable for the intelligent era, seizing opportunities, and consistently using AI as a key lever to enhance educational capabilities, directly facing challenges, pushing for reforms, and promoting deep faculty and student participation in building the AI open-source ecosystem, creating a new path that integrates talent cultivation with ecosystem construction.

Facing the “Three Shackles” of the AI Era

In the context of rapid iterations of AI technology and continuous upgrades in industrial demand, universities, as the main battleground for talent cultivation, face multiple challenges at the levels of teaching, practice, and resources.

1. The Challenge of Knowledge Barriers: The development of disciplines lags behind technological leaps. Traditional disciplinary systems act like invisible walls, isolating knowledge transmission into separate fortresses, limiting students to a single disciplinary perspective, making it difficult to form innovative thinking that transcends domains. Moreover, the speed of knowledge updates in classrooms lags far behind the evolution of AI technology, leading to teaching content often failing to keep pace with the times, leaving students “holding old maps, unable to find new continents.” This rigid barrier severely restricts the emergence of interdisciplinary innovative talents.

2. The Pain of Supply-Demand Mismatch: Skills training is disconnected from industrial practice. As AI applications profoundly reshape the labor market, traditional skills reliant on mechanical repetition and rule-based operations face severe challenges of being replaced by “digital employees.” Currently, there is a significant gap between the talent supply from universities and the actual demands of the industry: on one hand, various industries urgently need AI applications; on the other hand, graduates generally lack real engineering practice experience, making it difficult to quickly translate theoretical knowledge into productivity for solving complex scenarios. This “disconnection between learning and application” not only weakens students’ employment competitiveness but also makes it hard for them to adapt to the rapid iterations of the intelligent era.

3. The Scarcity of Resources: Innovation exploration is constrained by computing power bottlenecks. Computing power is the “source of motivation” in the intelligent era, and cutting-edge courses heavily rely on AI innovation resources such as computing power, data, models, and tools. However, most universities struggle to bear the enormous investment required for intelligent computing clusters and lack the capacity to maintain professional operational teams. Constrained by shortcomings in AI training environments, high-level teaching and research exploration empowered by AI often become a source-less endeavor. The computing power gap has become the biggest bottleneck restricting faculty and students from deeply participating in AI ecosystem construction and producing original achievements. Without fertile “research soil,” it is challenging to cultivate innovative fruits that lead the future.

Reconstructing the Educational Ecosystem with Open Source Spirit

In the face of these challenges, merely relying on slight adjustments to traditional educational models is insufficient for breakthroughs. The open-source orientation clearly outlined in the Action Plan provides us with a way to break through—leading with the spirit of open source, breaking barriers, integrating resources, and collaborating on innovation to reconstruct an educational paradigm that adapts to the AI era, achieving synchronous resonance between talent cultivation and industrial development.

1. From “One-Way Knowledge Transmission” to “Open Source Collaborative Creation”: AI breaks the temporal and spatial barriers of knowledge acquisition, and the open-source spirit makes it possible to “innovate while standing on the shoulders of giants.” AI is not only a learning object but also the core engine empowering personalized, project-based learning. In the teaching paradigm of “AI + Human Intelligence (AI + HI),” by introducing multi-agent interaction mechanisms and integrating multi-domain expert models, we can reshape the human-machine collaborative learning ecosystem. Through “course open sourcing,” we establish a community-based sharing and feedback mechanism, rapidly transforming cutting-edge research results, frontline industrial practices, and immediate social needs into teaching content, ensuring that learning is no longer confined to the classroom and textbooks, achieving “zero time difference” in knowledge iteration.

2. From “Skill Executors” to “Human-Machine Collaborative Innovators”: The core competitiveness of future talent lies not in how much established knowledge they master, but in their ability to leverage AI to solve complex engineering problems. We must actively guide students to abandon the anxiety of being “replaced” and focus on cultivating “enhanced innovators” with human-machine collaboration capabilities. We should promote the establishment of a long-term mechanism for deep integration between schools and enterprises, bringing cutting-edge industrial practices into the classroom, transforming real pain points from enterprise R&D and applications into AI practical topics for universities, and implementing “real problems, real solutions.” In frontline practical tasks, we can hone students’ engineering capabilities, allowing them to “learn to swim in the waves of practical challenges,” and quantify and certify their contributions, forming a lifelong “digital capability passport,” truly realizing the transition from “degree-based” to “capability-based”.

3. From “Resource Islands” to “Inclusive Shared Computing Power Bases”: Leveraging national strategic strength and deep integration of industry and education, we must seize the historic opportunity to build an efficient collaborative computing power network. Promote domestic computing power into universities, classrooms, and research, achieving true “computing power equity” and “educational fairness.” Shanghai Jiao Tong University is building the “Zhiyuan No. 1” thousand-card intelligent computing cluster, promoting large-scale domestic computing power into campuses, not only addressing the shortage of training and inference resources but also lowering usage thresholds and stimulating faculty and student engagement. At the same time, by learning, developing, and innovating in a controllable software and hardware environment, we can fundamentally strengthen the security foundation of China’s AI ecosystem and promote the vigorous development of original innovation.

Building the “Qiwuy Learning Community” as a New Engine for Talent Cultivation

In the face of changes, Shanghai Jiao Tong University, based on the concept of reconstructing the open-source educational ecosystem and combining its own educational advantages, is transforming theoretical exploration into practical action by building a professional talent cultivation platform, integrating open-source spirit, computing resources, and industrial demand throughout the talent cultivation process, providing replicable and scalable practical samples for AI education reform.

We are actively planning to collaborate with high-level universities, research institutions, and leading technology enterprises to create a nationwide AI practice talent cultivation platform—the “Qiwuy Learning Community.” “Qi” aims to enlighten wisdom and open the door to innovation; “Wu” aims to comprehend rules and internalize engineering qualities. We will focus on cultivating high-quality talent for the intelligent era, gathering high-quality open-source courses, introducing advanced domestic computing power, and constructing a closed-loop of “theory—practice—innovation”.

We will gather thousands of high-quality open-source micro-courses, breaking down the “walls” between universities and enterprises, creating an immersive learning space that integrates theory and practice; introducing domestically produced, controllable large-scale advanced computing power, transforming it into an accessible innovation resource space for frontline faculty and students, solidifying the digital foundation for engineering practice; collaborating with leading enterprises to deepen the “challenge-answer” mechanism, practicing a new model of industry-education integration where “enterprises pose problems, universities tackle them, both answer the same questions, and jointly evaluate results”; constructing a diversified talent evaluation system, establishing classified and graded achievement certification standards, bridging the “last mile” of mutual recognition of achievements between universities; and connecting quality entrepreneurial resources to empower students for high-quality employment and cross-border innovation. Let the “Qiwuy Learning Community” truly become a “training ground” for the domestic computing power ecosystem and an “accelerator” for cultivating top innovative talents.

AI education is a systematic project that must fully leverage the advantages of the national system while stimulating market vitality. “One flower alone does not make spring; a hundred flowers in bloom fill the garden with spring.” The essence of open source is connection and symbiosis. We must gather innovative forces with the spirit of open source, solidify the digital foundation with independent computing power, and jointly compose a new chapter in the high-quality development of China’s AI education, allowing every innovative dream to take root and sprout in the fertile soil of open source, injecting continuous innovative momentum into the construction of a strong educational nation.