• GPU parallel computing revolution
• Cloud computing lowering barriers
• Specialized AI chips emerging

• Internet content exponential growth
• Accelerated digitization process
• Mature data annotation techniques

• Transformer revolutionary breakthrough
• Attention mechanism innovation
• End-to-end learning paradigm

• Generative Pre-trained Transformer: Large language model based on Transformer architecture
• Autoregressive language model: Generates text by predicting the next word
• Unsupervised pre-training + Supervised fine-tuning: Typical representative of two-stage training paradigm

• Unidirectional attention mechanism: Can only see previous text, suitable for text generation tasks
• Large-scale parameters: From GPT-1's 117 million to GPT-4's hundreds of billions of parameters
• Powerful zero-shot and few-shot learning capabilities

• Multi-head self-attention: Captures long-range dependencies
• Positional encoding: Understands positional information in text sequences
• Residual connections + Layer normalization: Stabilizes training process

• Next word prediction: Learning language patterns from large-scale text corpora
• Causal masking: Ensures access only to previous words
• Large-scale data: Diverse data sources including internet text, books, news

• Instruction fine-tuning: Improves model's ability to follow instructions
• Reinforcement Learning from Human Feedback (RLHF): Aligns model outputs with human preferences
• Safety filtering: Reduces generation of harmful content

• Creative writing: Stories, poetry, script creation
• Technical documentation: API docs, user manuals, technical reports
• Marketing content: Ad copy, product descriptions, social media content

• Reading comprehension: Answering complex text-based questions
• Logical reasoning: Solving mathematical problems and logic puzzles
• Knowledge Q&A: Cross-domain encyclopedic knowledge queries

• Program writing: Generating code based on requirements
• Code explanation: Understanding and commenting existing code
• Debugging assistance: Finding and fixing code errors

• Strong generalization: One model handles multiple tasks
• In-context learning: Quickly adapts to new tasks through examples
• Creative output: Generates novel and useful content

• Hallucination issues: May generate seemingly reasonable but actually incorrect information
• Knowledge cutoff: Training data has time limitations
• High computational cost: Inference requires significant computational resources
• Poor interpretability: Difficult to understand model's decision-making process

Definition: Token is the smallest unit for model text processing, can be a single character, word, or even part of a word. Created by a tokenizer that splits raw text.

⌨️ Analogy: If you think of a sentence as a wall built with blocks, tokens are each individual block.

Definition: Dimension is the position or "length" of each data point in a vector or matrix. Commonly used to describe the vector space size in hidden layers.

📦 Analogy: If a token is a product, dimensions are the number of "feature labels" it has, like color, size, purpose, etc.

Definition: Parameters are the "knowledge" learned by the model during training. They include weights and biases in neural networks.

Scale: GPT-3 has 175 billion parameters, GPT-4 is estimated to have even more.

🧠 Analogy: Think of the model as a brain, parameters are the "memory connections" or "experiences" formed in that brain.

Learning input-output mappings from labeled data

Examples: Image classification, sentiment analysis

Optimizing strategies through trial and error with reward signals

Examples: Game AI, robot control

Multi-layer neural networks automatically extracting features

End-to-end learning of complex patterns

• AlphaFold: Solved protein folding, predicted 200M+ protein structures, saving ~1 billion years of research time
• MatterGen: "Reverse design" new materials, AI proposes 120K candidate structures in 1 hour
• Scientists evolve from "experimenters" to "hypothesis strategists" and "inquiry designers"

• AI Music (MuseNet): Assists composition, arrangement, generates emotion-specific music from text
• AI Art (Midjourney): Lowers visual expression barriers, becomes artists' "creative accelerator"
• Creator value shifts from "technical execution" to "conceptual planning" and "aesthetic judgment"

• Precision Medicine: Customized treatment plans based on individual genes and imaging data
• Customized Consumption: E-commerce and streaming create unique experiences for each user
• Adaptive Learning: Dynamically adjusts teaching content based on student progress, achieving "personalized education"

Core shift: From "jobs" to "tasks" - AI automates specific tasks (30%-70%) within jobs, not entire professions.

Emergence of "AI+X" professionals who combine domain expertise with AI capabilities, emphasizing lifelong learning for career resilience.

• Analytical thinking and creative thinking
• Meta-cognitive skills and learning strategies

• Leadership, collaboration, communication skills
• Empathy, persuasion, and motivation abilities

• Resilience, flexibility, agility, self-motivation
• AI and big data literacy

• Note-taking and external memory
• Calculators for computation
• Maps for navigation
• Preserves higher-order thinking

• Complex analysis and synthesis
• Creative content generation
• Decision-making support
• May impact skill development

• Paradox: AI is both a "cognitive gym" and potentially a "cognitive crutch"
• Key variable: Impact depends not on AI tools themselves, but on their "pedagogical context"
• Solution: Systematic teacher training to master instructional design that uses AI to promote higher-order thinking

• Human-centered mindset development
• AI ethics understanding
• AI techniques and applications mastery
• AI system design engagement

• Conscientious: Understanding AI capabilities and limitations
• Collaborative: Using AI as a learning partner
• Critical: Evaluating AI outputs critically
• Creative: Leveraging AI for creative purposes

• Generate diverse ideas and perspectives
• Challenge assumptions and biases
• Provide alternative viewpoints
• Support ideation and brainstorming

• Analytics on learning patterns
• Insights into cognitive processes
• Reflection support and guidance
• Strategy recommendation

• From "knowledge transmission" to "competency cultivation": When knowledge is readily available, education returns to "moral education" essence
• From "knowing" to "becoming": Goal is cultivating "lifelong learners" who can learn autonomously, solve complex problems, and make ethical judgments
• Core competency framework: Countries propose "four-in-one" competency models integrating "knowledge, thinking, values, practice"

• Pedagogical shift: Project-based learning (PBL) and inquiry-based learning as core
• Curriculum reconstruction: From subject-based to competency-based, emphasizing interdisciplinary integration and real-world problem solving
• Learning space transformation: From fixed classrooms to flexible learning environments, supporting personalized and collaborative learning

• Shift to competency-based and interdisciplinary learning
• Emphasis on "irreplaceable human skills"
• Integration of AI literacy as core curriculum
• Personalized and adaptive coursework

• Educators as "learning architects" and guides
• Learners as active co-creators and critical consumers
• New skills required: AI literacy, data analysis, ethical guidance

• AI-driven formative and summative assessment
• Personalized and adaptive evaluation
• Focus on authenticity, process, and continuous feedback
• Challenge to traditional assessment validity

• Role transformation: From "knowledge transmitter" to "learning experience facilitator, guide, and designer"
• Value enhancement: AI handles repetitive tasks (grading, Q&A), teachers focus on thought inspiration and emotional care
• Capability requirements: Must possess high-level AI literacy and instructional design abilities, teacher training is key

• Functional transformation: From "selection and ranking" to "diagnosis and empowerment"
• Method diversification: Growth portfolios, performance assessment, multi-stakeholder evaluation
• AI assistance: Generate "student digital portraits," dynamically track competency development, provide formative feedback

Humans and AI systems learn together, adapt mutually, and co-evolve through collaboration

• Shared mental models and common ground
• AI as team partner, not just tool
• Mutual learning and adaptation through interaction

Prioritizes human values, ethical application, and personalized feedback while leveraging AI capabilities

• AI enhances rather than replaces human capabilities
• Integration of technology, UX, and ethical considerations
• Emphasis on attribution and reflection in AI use

Focus on developing uniquely human capabilities that AI cannot replicate

• Critical thinking, creativity, emotional intelligence
• Ethical judgment and complex collaboration
• Human-centered innovation and value-driven outcomes

Understanding how knowledge is constructed, validated, and applied in a rapidly changing world

• Meta-cognitive skills and learning strategies
• Adaptability and lifelong learning capabilities
• Integration of programming, data science, complex systems

Achieve large-scale "personalized education"

AI assists lesson planning, question generation, scheduling, reducing teacher workload and increasing efficiency

VR/AR virtual experiments, historical scene recreation, enhancing learning engagement

Chat with PDF documents, summarize key points, organize notes

AI-assisted intelligent grouping, discussion guidance, enhancing team collaboration

Create and manage "beneficial cognitive friction" rather than pursuing convenience, preventing "cognitive outsourcing"

• Develop national-level "AI & Education" integration strategy with systematic planning
• Launch "Future Skills" national lifelong learning programs to support workforce transformation
• Establish agile AI education governance frameworks using "regulatory sandbox" adaptive models

• Place teacher professional development at strategic core, support teacher transformation to "learning designers"
• Promote systematic reconstruction of curriculum and assessment, shifting from "knowledge-based" to "competency-based"
• Adopt rigorous AI product ethical procurement frameworks, prioritizing educational values and ethics