Core Majors in Artificial Intelligence: Avoid Common Mistakes

Many parents and students mistakenly believe that AI is a single major. In reality, it encompasses engineering and interdisciplinary fields with vastly different career paths. Understanding these distinctions is crucial to avoid wasting five years.

Key Undergraduate Majors (4 Main Disciplines)

1. Artificial Intelligence (080717T)
   The flagship major in AI, focusing on algorithms, large models, deep learning, and intelligent systems development. It is the preferred choice for core positions in internet companies and AI enterprises, with the highest career ceiling and score requirements.

2. Intelligent Science and Technology (080907T)
   This major emphasizes intelligent perception, robotics, intelligent control, and embedded AI, combining hardware and software. It is suitable for students interested in robotics, autonomous driving, and smart hardware, aligning with military, intelligent manufacturing, and automotive sectors.

3. Data Science and Big Data Technology (080910T)
   Known as the “fuel major” for AI, it focuses on data collection, cleaning, modeling, and analysis, serving as the foundation for AI applications. It has the broadest employment opportunities across various industries, with relatively friendly score requirements.

4. Computer Science and Technology (080901)
   This foundational major covers computer systems, software development, and algorithm basics. It is the safest choice for students transitioning to AI or pursuing further studies in AI, offering strong versatility and resilience against AI trends.

Graduate Specializations (Directly Linked to Employment and Salary)

Master’s programs can be categorized into six core areas:

• Fundamental Theory: Machine learning, deep learning, reinforcement learning, AI logic.
• Computer Vision (CV): Image recognition, autonomous driving, security monitoring, medical imaging.
• Natural Language Processing (NLP): Large models, intelligent Q&A, machine translation, content generation.
• Robotics and Intelligent Control: Industrial robots, service robots, unmanned systems, intelligent equipment.
• Big Data and Data Mining: Data governance, quantitative analysis, business intelligence, user profiling.
• Cross-Application Areas: AI in healthcare, finance, education, intelligent manufacturing, and government.

Comprehensive Undergraduate Curriculum: Mathematics, Programming, and AI Core

AI is not just about “playing with computers”; it is one of the most challenging engineering disciplines, requiring strong mathematical and programming skills. The four-year curriculum is divided into three major modules:

1. Mathematical Foundations (Crucial)
   Courses include advanced mathematics, linear algebra, probability theory and statistics, discrete mathematics, optimization methods, numerical analysis, and complex functions. This foundational knowledge is essential for understanding algorithms and models.

2. Programming and Computer Fundamentals (Core Skills)
   Courses include Python programming, C/C++, data structures and algorithms, computer organization, operating systems, computer networks, database systems, Linux applications, and software engineering. Python is the core programming language for AI, and data structures and algorithms are key topics for exams and interviews.

3. Core AI Modules (Specialization Tracks)
   Courses include introduction to AI, machine learning, deep learning, pattern recognition, knowledge engineering, data mining, computer vision, natural language processing, digital image processing, IoT technology, intelligent robotics, large model technologies and applications, and AI safety and ethics. Students will choose specialization tracks in their third year based on their career interests.

University Recommendations by Score Ranges

Based on the fifth round of subject evaluations and the 2026 admission scores, universities are categorized into five tiers, covering top-tier institutions to public colleges, tailored to various score ranges.

Tier 1: Top A+ Institutions (Ranked 1-1000, 650+ points)

• Recommended Universities: Tsinghua University, Peking University, Zhejiang University, Shanghai Jiao Tong University, University of Science and Technology of China, Nanjing University, Southeast University, University of Electronic Science and Technology of China, Xi’an University of Electronic Science and Technology.
• Discipline Evaluation: Computer Science and Technology A+, Intelligent Science and Technology top-tier in China.
• Core Advantages: Academy teams, national AI laboratories, direct recruitment from major companies, over 30% graduate school recommendation rate, starting salary of 400,000+.

Tier 2: Strong 985/211 Universities (Ranked 1000-5000, 620-650 points)

• Recommended Universities: Beihang University, Beijing Institute of Technology, Harbin Institute of Technology, Huazhong University of Science and Technology, Xi’an Jiaotong University, Wuhan University, Sun Yat-sen University, Beijing University of Posts and Telecommunications.
• Discipline Evaluation: Computer A, distinctive AI programs.
• Core Advantages: High industry recognition, direct access to state-owned enterprises and major internet companies, high graduate school admission rates, starting salary of 300,000-400,000.

Tier 3: Double First-Class/Provincial Key B+ Institutions (Ranked 5000-20000, 580-620 points)

• Recommended Universities: Nanjing University of Science and Technology, Nanjing University of Aeronautics and Astronautics, South China University of Technology, Chongqing University, Dalian University of Technology, Northwestern Polytechnical University, Soochow University, Zhengzhou University.
• Discipline Evaluation: Computer B+ and above, mature AI program development.
• Core Advantages: Cost-effective, moderate scores, strong local employment opportunities, advantages in public service and state-owned enterprises.

Tier 4: Public Undergraduate B Institutions (Ranked 20000-50000, 520-580 points)

• Recommended Universities: Hangzhou Dianzi University, Chongqing University of Posts and Telecommunications, Nanjing University of Posts and Telecommunications, Guangdong University of Technology, Zhejiang University of Technology, Shandong University of Science and Technology, Chengdu University of Information Technology.
• Discipline Evaluation: Computer B and above, strong IT programs.
• Core Advantages: Practicality in AI application development, high local employment rates over 90%, starting salary of 200,000-300,000.

Tier 5: Private/Public Undergraduate Safety Net Institutions (Ranked 50000+, 450-520 points)

• Recommended Universities: Public: Guilin University of Electronic Technology, Nanchang Institute of Technology, Liaoning University of Science and Technology; Private: Qilu University of Technology, Wuhan Donghu University, Guangzhou Nanfang College.
• Core Advantages: Low score thresholds, focusing on AI practical skills and big data applications, suitable for students aiming to enter the AI field with lower scores.

Graduate School Preparation: Exam Subjects, Study Content, Direction Selection, and Employment Prospects

The employment ceiling for AI undergraduates is limited, with 80% of core algorithm positions requiring a master’s degree or higher. Graduate school is essential for AI students, and the latest 2026 exam rules, subjects, and directions are thoroughly analyzed.

1. Choosing Between Academic and Professional Master’s Programs

   • Academic Master’s (Intelligent Science and Technology, Computer Application Technology)
     • Exam Subjects: Politics, English I, Mathematics I, Computer Science Professional Foundation (408)/Self-Designed.
     • Study Content: Focus on AI foundational theory, cutting-edge research, and paper publication, duration of 3 years.
     • Suitable for: Those aiming for a PhD, positions in universities, research institutes, or R&D roles in state-owned enterprises.
   • Professional Master’s (Artificial Intelligence, Electronic Information - AI Direction)
     • Exam Subjects: Politics, English II, Mathematics II, Data Structures and Algorithms/AI Fundamentals.
     • Study Content: Focus on project practice, industry application, and engineering development, duration of 2-3 years.
     • Suitable for: Those wanting quick employment, high salaries, and most candidates’ first choice.

2. Core Graduate Exam Subjects

   • Public Courses: Politics (100 points), English (English I/II, 100 points), Mathematics (150 points), with Mathematics being the key scoring element.
   • Professional Courses (150 points): Mainstream subjects include 408 Computer Science Professional Foundation (data structures, computer organization, operating systems, computer networks); some universities have self-designed subjects: AI fundamentals, machine learning, data structures, and algorithms.

3. Graduate School Directions and Future Employment Trends

   • Computer Vision (CV)
     • Employment Directions: Autonomous driving, security monitoring, medical imaging, industrial quality inspection, metaverse.
     • Employment Prospects: High demand from automotive companies, tech giants, and AI firms, with a talent gap exceeding 500,000, starting salary of 350,000+ for master’s graduates.
   • Natural Language Processing (NLP)
     • Employment Directions: Large model development, intelligent customer service, machine translation, content generation, financial research.
     • Employment Prospects: Highly sought after in the wave of large models, with companies like Baidu, ByteDance, Alibaba, and OpenAI competing for talent, starting salary of 400,000+ for master’s graduates.
   • Machine Learning and Algorithms
     • Employment Directions: Recommendation algorithms, advertising algorithms, quantitative trading, risk control algorithms, AI model optimization.
     • Employment Prospects: Core positions in major internet companies, with the highest salary ceilings, starting salary of 400,000-600,000 for master’s graduates.
   • Big Data and Data Intelligence
     • Employment Directions: Data analysts, data developers, business intelligence, data governance, quantitative analysis.
     • Employment Prospects: Broad employment opportunities across all industries, with support for digital economy and data marketization, starting salary of 250,000-350,000 for master’s graduates.
   • Robotics and Intelligent Control
     • Employment Directions: Industrial robots, service robots, unmanned systems, military intelligent equipment.
     • Employment Prospects: Core direction of China Manufacturing 2025, with significant policy support, starting salary of 300,000-400,000 for master’s graduates.
   • Graduate School Policy Benefits
     • The state is expanding admissions for AI professional master’s programs, increasing enrollment quotas annually.
     • Cross-disciplinary support for graduate studies, allowing students from computer, electronics, automation, and mathematics to apply.
     • Double first-class universities are establishing special AI plans, prioritizing recommendations and exam quotas for AI directions.

Future Employment Trends and National Policy Analysis: Is It Worth Pursuing?

1. Employment Salaries and Industry Distribution (Real Data, No Exaggeration)

   • Salary Levels: Bachelor’s graduates earn 150,000-250,000, master’s graduates earn 250,000-600,000, and doctoral graduates earn 800,000+ with no upper limit. AI has been the top engineering salary major for the past decade.
   • Industry Distribution: Internet giants (ByteDance, Alibaba, Tencent, Baidu) account for 35%; AI tech companies (SenseTime, Megvii, iFlytek) account for 20%; automotive/smart manufacturing account for 25%; finance/state-owned enterprises/public institutions account for 15%; research institutes/universities account for 5%.
   • Employment Cities: Beijing, Shanghai, Shenzhen, Hangzhou, Guangzhou, Chengdu, Nanjing, Xi’an, where the AI industry is concentrated, offering numerous high-paying jobs.

2. Comprehensive National Policy Support, Long-Term Opportunities Without Bubbles

   • National Strategy: AI is a core component of the new generation of information technology, included in the 14th Five-Year Plan, aiming for world-leading status by 2030, representing a long-term supported track.
   • Industry Policies: Various regions are introducing AI talent recruitment subsidies, tax reductions for enterprises, and research funding support, with Beijing, Shanghai, Shenzhen, and Hangzhou creating AI industry clusters, leading to continuous talent demand.
   • Employment Guarantees: AI majors are included in the national directory of urgently needed talents, with special recruitment for public service and state-owned enterprises, prioritizing residency and housing subsidies.
   • Risk Alerts: Low-end AI development and basic operation positions may be replaced by AI, while high-end algorithms, research, and cross-application positions will always be scarce. Education and skills remain the core competitive advantages.

The Deep Impact of Artificial Intelligence on Society: Opportunities and Risks

1. Positive Impacts: Restructuring Society and Advancing Human Civilization

   • Industrial Upgrades: Promoting the intelligentization of manufacturing, agriculture, finance, healthcare, and education, reducing costs and increasing efficiency, transforming China from manufacturing to intelligent manufacturing.
   • Life Changes: The proliferation of smart homes, autonomous driving, intelligent healthcare, and online education enhances life efficiency and addresses issues of resource imbalance in elderly care, healthcare, and education.
   • Technological Breakthroughs: Supporting breakthroughs in aerospace, pharmaceuticals, energy, and materials research, AI predicts protein structures and simulates controllable nuclear fusion, tackling unsolved human challenges.
   • Employment Innovations: Eliminating low-end repetitive labor while creating new positions in AI training, algorithm optimization, AI products, and AI ethics, driving labor towards high-end services and technology industries.

2. Potential Risks: Social Issues to Watch Out For

   • Employment Impact: Low-end positions such as assembly line workers, basic customer service, clerical work, simple programming, and content copying may be massively replaced, exacerbating short-term structural unemployment.
   • Ethical Risks: Data privacy breaches, algorithmic discrimination, deepfakes, and AI misuse threaten personal rights and social security.
   • Wealth Disparity: Groups and industries mastering AI technology and data resources may accelerate wealth accumulation, potentially widening wealth and regional development gaps.
   • Security Risks: AI applications in military, cyberattacks, and autonomous decision-making systems pose risks to national security and global governance.

3. Future Trends: Human-Machine Collaboration Rather Than Opposition
   The future society will not see AI replacing humans but rather human-machine collaboration and division of labor. Humans will focus on innovation, emotions, decision-making, and ethics, while AI will handle repetitive, computational, and execution tasks. AI is a tool, and those who master the tool are the core.

Artificial intelligence is not a fleeting trend but a core track for the next 30 years. Choosing the right major, mastering mathematics, improving programming skills, attending the right universities, and pursuing graduate studies will allow you to seize the opportunities of this era.

Final Reminder: The AI major is challenging and suitable for students with strong mathematical skills, a passion for programming, and resilience. Those with insufficient scores or weak mathematical backgrounds should consider data science or computer science majors as a more stable entry into the AI field.