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Education Scenario

Building innovative platforms for AI teaching and practice in universities, delivering professional knowledge systems and practical training solutions

What does AI education include?

索引

  • AI Education System
    AI Education System
  • Challenges in AI Education
  • AI Teaching Solutions
What does AI education include?

AI Education System

The AI education system includes three independent yet closely connected parts: foundational teaching, applied teaching, and security teaching. In the foundational teaching stage, learners will encounter basic knowledge such as mathematics and computer science, as well as core AI theories like machine learning and deep learning, laying theoretical foundations for subsequent learning. In applied teaching, learners will learn to apply AI technologies to various fields such as healthcare, finance, and education through project practice and case analysis, practically improving their ability to solve real problems. At the same time, AI security teaching allows students to deeply understand AI security risks in areas such as algorithmic bias and data privacy, master response strategies, and guide students to always uphold safety and ethical standards in technology development and application processes.

Challenges in AI Education

In today's era of rapid technological development, cutting-edge AI technologies like DeepSeek are rising powerfully, reshaping industry landscapes with tremendous momentum. AI is no longer a distant concept but deeply integrated into all aspects of life and work, making adult learning of AI knowledge urgent. However, traditional AI teaching models show obvious deficiencies in many aspects.

Lagging Knowledge Updates

  • The AI field develops rapidly with new algorithmic models constantly emerging, such as StableDiffusion and GPT-5, which bring disruptive changes to creation and interaction methods. In sharp contrast, traditional teaching knowledge update speeds are extremely slow, still limited to old knowledge systems. This leads to knowledge learned in classrooms becoming outdated before students even graduate, making it difficult to create value in practical applications.

Teaching Scenarios Disconnected from Reality

  • Cases and exercises in traditional classrooms are mostly disconnected from real work scenarios. Although students learn in virtual environments, when facing actual problems such as enterprises using AI for precision marketing and supply chain management optimization, due to lack of real scenario training, they often cannot apply learned knowledge to projects and cannot quickly adapt and solve problems.

Disconnect Between Theory and Practice

  • Traditional teaching focuses on extensive abstract theoretical explanations, making students' understanding of AI technology remain only on paper, unable to master methods for transforming theory into actual technical operations. The result is that students often feel helpless when facing real code writing and model building tasks, having theoretical knowledge but unable to put it into practice.

AI Teaching Solutions

GenTel proposes AI teaching solutions, constructing a comprehensive teaching and training system with basic hardware as the foundation, knowledge systems as the focus, and platform capabilities as the core, covering general knowledge and application scenarios.

Building Complete Knowledge Systems

The curriculum system integrates cutting-edge research results and rich teaching experience from top domestic and international universities, real-time integration of the latest AI technologies and algorithms, and establishes dynamically updated teaching resource libraries to ensure students always have access to the latest industry knowledge, guarantee learned knowledge meets practical application needs, and improve knowledge timeliness and practicality.

Creating Immersive Scenario Experiences

Closely aligned with industry needs, cleverly integrating knowledge points into specific tasks, constructing highly realistic enterprise-level practical environments and simulating real business scenarios. Students repeatedly practice in simulated scenarios, completing rapid knowledge transfer and seamlessly connecting with enterprise needs. Example: In financial scenarios, driven by the task of 'using AI to build financial risk prediction models,' students collect financial market historical data, analyze various risk indicators, match algorithms to build prediction models and verify and optimize them, deeply understanding AI industry applications and quickly adapting to job requirements.

Precise Traceability Analysis

Building dedicated AI ranges, simulating various real and complex industry task scenarios, providing environments for students to operate AI frameworks (such as TensorFlow, PyTorch), learning while practicing through visualization tools, completing tasks such as developing intelligent customer service systems and building industrial equipment fault prediction models, achieving practical application of learning. Example: In intelligent customer service system tasks, the system automatically detects indicators such as customer service reply accuracy, response time, and problem-solving ability, providing precise scoring, with students' practical abilities improved through simulated combat.