Jin Shi's Vision: AI as the New Quality Engine for Chinese Higher Education
National Committee of the Chinese People's Political Consultative Conference (CPPCC) Standing Committee member and Vice President of Southeast University, Jin Shi, has called for harnessing artificial intelligence (AI) to ignite higher education's 'new quality engine.' In a recent article published in People's Daily and Guangming Daily, Jin Shi reflects on his 2025 duties, emphasizing how AI is reshaping the human knowledge landscape and urging universities to adapt swiftly. As an expert in information and communications technology (ICT) and AI, his advocacy aligns with China's push toward new quality productive forces—a term referring to innovative, high-tech drivers of economic growth under Chinese-style modernization.
Jin Shi's message is clear: higher education must transcend superficial digital tools and fundamentally restructure disciplinary logics to bridge the gap between rapid AI advancements and traditional academic cycles. This vision positions AI not merely as a teaching aid but as a transformative force for talent cultivation, research, and industry alignment.
The Critical 'Time Lag' Between AI Innovation and Educational Reform
One of the most pressing issues highlighted by Jin Shi is the 'time lag' in higher education. AI algorithms in research labs evolve weekly, yet discipline construction and talent training operate on yearly cycles. This structural mismatch risks leaving graduates unprepared for industries powered by AI.
During 2025, Jin Shi surveyed numerous universities, high-tech firms, and institutes, observing firsthand how this lag hampers progress. He argues that true change requires AI's deep fusion with education, enabling organic integration of new technologies into teaching models. This step-by-step process involves: first, assessing current curricula for AI compatibility; second, redesigning syllabi to embed AI methodologies; third, piloting interdisciplinary projects; and fourth, scaling successful models nationwide.
In the broader context, China's Ministry of Education (MOE) recognizes this urgency. During the 14th Five-Year Plan (2021-2025), universities cultivated 55 million graduates, with over one-fifth of programs adjusted for strategic fields like AI.
Southeast University's Trailblazing AI4SEU Action Plan
Southeast University (SEU), under Jin Shi's leadership, launched the 'AI4SEU' action plan to inject AI into every traditional discipline via supply-side reforms. This initiative targets emerging frontiers like low-altitude technology (drones and urban air mobility), 6G communications, organ-on-a-chip biotechnology, and future robotics—fields where SEU has early-mover advantages.
Rather than teaching basic AI operations, AI4SEU emphasizes deep embedding in core areas such as full-chain system architectures and intelligent control applications. This empowers students and faculty to lead in technical-professional synergies. For professionals eyeing faculty positions or research roles in AI-driven fields, SEU's model offers a blueprint for career advancement in China's academic landscape.
The plan's success stems from breaking away from rote learning toward AI-enabled creativity, aligning with national goals for self-reliant innovation.
Vertical Domain Large Models: SEU's Strategic Edge
Jin Shi notes a pivotal shift from general large language models (LLMs) to vertical domain-specific large models (VDMs), where higher education can shine as a strategic pillar. SEU avoids 'one-size-fits-all' replication by leveraging its strengths to develop proprietary VDMs supporting industrial upgrades.
- Quantum computing software: 'Southeast · Yunxiao' for simulations.
- Concrete materials: 'Tuo Zhen Tuo Zhi' model for sustainable construction.
- Legal domain: 'Fa Heng' for intelligent jurisprudence analysis.
- Ethics: 'Wendao' for AI governance discussions.
These models exemplify 'professional depth meets AI breadth,' fostering asymmetric advantages in global competition. Explore similar innovations through academic career advice on our site.
Developing a VDM involves: data curation from domain experts, fine-tuning base models, validation against real-world benchmarks, and iterative deployment in teaching and research.
National Drive: MOE's 'AI + Higher Education' Typical Scenarios
The MOE has spearheaded 'AI + Higher Education' with three batches of typical application scenarios, totaling 80 cases as of October 2025. SEU features prominently alongside Peking University, Tsinghua University, Beihang University, and others. Examples include Peking University's 'Beida Wenxue' intelligent teaching platform and Tsinghua's AI-empowered pilots.
These scenarios cover intelligent tutoring, virtual simulations, and personalized learning, demonstrating scalable impacts. For instance, Chongqing University's 'Runxin' AI counselor enhances student governance. Such initiatives ensure AI boosts teaching quality across links from admission to graduation.
By 2030, MOE aims for universities as global AI innovation hubs.View MOE's second batch announcement.
Top Universities Leading China's AI Higher Education Revolution
Tsinghua University tops the 2026 CSRankings for AI, dominating with Peking University close behind. Tsinghua's generative AI summer school and vast patent portfolio (4,986 AI/ML patents by 2024) underscore its prowess. Peking University excels in AI publications across vision and machine learning.
Beijing boasts 36 undergraduate AI majors and 48 AI institutes. Fudan University integrates AI into general curricula. These efforts produced elite talents during the 14th Plan, with plans for interdisciplinary centers in the 15th Plan (2026-2030).
Professionals can find opportunities in postdoc positions or China-focused academic jobs.
Beijing's Benchmark Model and Widespread AI Adoption
Beijing designated 50 benchmark sites, including 8 universities, to pioneer generative AI in education. By late 2025, 87.7% of primary/secondary schools used AI, with universities expanding micro-majors (2,654 new ones enrolling 74,000 students).
- Benefits: Personalized learning, teacher upskilling, replicable frameworks.
- Risks: Equity gaps, over-reliance—mitigated by ethical guidelines.
Overcoming Challenges: Ethics, Silos, and Equity
Challenges persist: disciplinary silos, data privacy, ethical AI use. Jin Shi urges forward-looking research to preempt trends. Nationally, policies address biases and security, as in SEU's 'Wendao' model.
Solutions include cross-disciplinary teams, faculty training (e.g., MOE's AI literacy pushes), and inclusive access for rural students.
Photo by Logan Voss on Unsplash
Toward New Quality Productive Forces and Global Leadership
AI integration supports China's new quality productive forces by systematizing talent pipelines. By 2030, expect widespread VDMs, hybrid teaching, and AI-driven research hubs. This positions Chinese universities as self-reliant innovators amid global competition.
For educators and researchers, check professor jobs or rate your professors for insights.
Career Implications and Actionable Insights for Academics
AI boosts demand for hybrid experts. Action steps: Upskill via university AI courses, contribute to VDM projects, pursue higher ed jobs in AI. Platforms like AcademicJobs.com connect talents to opportunities in China and beyond.
Internal resources: career advice, resume templates.