Primary School
A.1Integrated Stage Edition
A.1.1Textbook Contents
This appendix is based on the primary-school textbook in the Tsinghua Artificial Intelligence General Education Series for Primary, Secondary, and University Education.
| No. | Chapter | Lesson |
|---|---|---|
| 1 | Chapter 1 From Dream to Reality | The Story of Yan Shi |
| 2 | Chapter 1 From Dream to Reality | Al-Jazari’s Mechanical Band |
| 3 | Chapter 1 From Dream to Reality | Artificial Intelligence in Film |
| 4 | Chapter 1 From Dream to Reality | What Is Artificial Intelligence? |
| 5 | Chapter 2 Artificial Intelligence Around Us | High-Speed-Rail Ticket Inspection |
| 6 | Chapter 2 Artificial Intelligence Around Us | Electronic Traffic Police |
| 7 | Chapter 2 Artificial Intelligence Around Us | Beauty Cameras |
| 8 | Chapter 2 Artificial Intelligence Around Us | Robot Vacuum Cleaners |
| 9 | Chapter 2 Artificial Intelligence Around Us | Autonomous Driving |
| 10 | Chapter 2 Artificial Intelligence Around Us | Recommender Systems |
| 11 | Chapter 3 Frontiers of Artificial Intelligence | The AI Painter |
| 12 | Chapter 3 Frontiers of Artificial Intelligence | The AI Composer |
| 13 | Chapter 3 Frontiers of Artificial Intelligence | The AI Poet |
| 14 | Chapter 3 Frontiers of Artificial Intelligence | The Story of AlphaGo |
| 15 | Chapter 3 Frontiers of Artificial Intelligence | OpenAI and ChatGPT |
| 16 | Chapter 3 Frontiers of Artificial Intelligence | The Story of Sora |
| 17 | Chapter 3 Frontiers of Artificial Intelligence | The AI Weather Forecaster |
| 18 | Chapter 4 Origins of Artificial Intelligence | The Story of Aristotle |
| 19 | Chapter 4 Origins of Artificial Intelligence | The Story of George Boole |
| 20 | Chapter 4 Origins of Artificial Intelligence | Turing and the Turing Machine |
| 21 | Chapter 4 Origins of Artificial Intelligence | The Birth of the Computer |
| 22 | Chapter 4 Origins of Artificial Intelligence | Early Conceptions of Machine Intelligence |
| 23 | Chapter 4 Origins of Artificial Intelligence | The Dartmouth Conference |
| 24 | Chapter 5 History of Artificial Intelligence | The Story of Wu Wenjun |
| 25 | Chapter 5 History of Artificial Intelligence | Feigenbaum’s Expert Systems |
| 26 | Chapter 5 History of Artificial Intelligence | Deep Blue: A Crowning Achievement |
| 27 | Chapter 5 History of Artificial Intelligence | The Rise of Deep Learning |
| 28 | Chapter 5 History of Artificial Intelligence | The Era of Large Models |
| 29 | Chapter 5 History of Artificial Intelligence | Toward the Future |
| 30 | Chapter 6 Foundations of Artificial Intelligence | Understanding Computers |
| 31 | Chapter 6 Foundations of Artificial Intelligence | Understanding Computer Programs |
| 32 | Chapter 6 Foundations of Artificial Intelligence | What Is an Algorithm? |
| 33 | Chapter 6 Foundations of Artificial Intelligence | Knowledge and Intelligence |
| 34 | Chapter 6 Foundations of Artificial Intelligence | A Good Machine Must Learn |
| 35 | Chapter 7 The Era of Deep Learning | Pitts and His Neuron Model |
| 36 | Chapter 7 The Era of Deep Learning | The Perceptron: A Neural Network That Learns |
| 37 | Chapter 7 The Era of Deep Learning | The Story of Geoffrey Hinton |
| 38 | Chapter 7 The Era of Deep Learning | Fei-Fei Li and the ImageNet Dataset |
| 39 | Chapter 7 The Era of Deep Learning | GPUs: From Games to Artificial Intelligence |
| 40 | Chapter 7 The Era of Deep Learning | Understanding AlphaGo |
| 41 | Chapter 7 The Era of Deep Learning | Exploring Large Language Models |
| 42 | Chapter 7 The Era of Deep Learning | Deep-Learning Challenge: Intelligence That Is Hard to Understand |
| 43 | Chapter 7 The Era of Deep Learning | Deep-Learning Challenge: Adversarial Examples |
| 44 | Chapter 7 The Era of Deep Learning | Deep-Learning Challenge: Super-Agents |
A.1.2Practice Manual Contents
This appendix is based on the primary-school practice manual in the series, Bo’ai Xinhua Primary School Edition. The activities are organized by chapter as in the original manual.
| No. | Chapter | Activity Type | Activity |
|---|---|---|---|
| 1.1 | Chapter 1 | Survey | A Thousand-Year Dream of Intelligent Machines |
| 1.2 | Chapter 1 | Survey | Finding AI Around Us |
| 2.1 | Chapter 2 | Scenario Drama | The Station of Time |
| 2.2 | Chapter 2 | Experiential Activity | The Modern Magic Mirror: Does It Really Make You More Beautiful? |
| 2.3 | Chapter 2 | Essay | AI-Enabled Robots |
| 3.1 | Chapter 3 | Experiential Activity | An Illustrated Guide to Seasonal Sprites |
| 3.2 | Chapter 3 | Experiential Activity | Enriching Class Culture: Designing a Class Song |
| 3.3 | Chapter 3 | Experiential Activity | A Four-Seasons Poetry Gathering |
| 3.4 | Chapter 3 | Experiential Activity | An AI Fairy Tale |
| 4.1 | Chapter 4 | Hands-On Computer-Birth Timeline | The Tunnel of Computer Evolution |
| 5.1 | Chapter 5 | Essay | AI Takes You into the Future |
| 6.1 | Chapter 6 | Essay | A Journey Through Computer History |
| 6.2 | Chapter 6 | AI Mini-Theater | The Growth of Little AI Zhi |
| 7.1 | Chapter 7 | Debate | Will Future Super-Agents Be Humanity’s Friends or Enemies? |
A.2Grade-Level Volumes
This appendix is based on the Grade 3 through Grade 6 textbooks in the series and lists units and sections by semester.
A.2.1Grade 3, First Semester
| No. | Unit | Section |
|---|---|---|
| 1.1 | From Dream to Reality | The Story of Yan Shi’s Artificial Man |
| 1.2 | From Dream to Reality | Al-Jazari’s Mechanical Band |
| 1.3 | From Dream to Reality | Artificial Intelligence in Film |
| 1.4 | From Dream to Reality | The Story of a Rice Cooker: What Is Artificial Intelligence? |
| 2.1 | Artificial Intelligence Around Us (1) | High-Speed-Rail Ticket Inspection |
| 2.2 | Artificial Intelligence Around Us (1) | Electronic Traffic Police |
| 2.3 | Artificial Intelligence Around Us (1) | Beauty Cameras |
| 3.1 | Artificial Intelligence Around Us (2) | Robot Vacuum Cleaners |
| 3.2 | Artificial Intelligence Around Us (2) | Autonomous Driving |
| 3.3 | Artificial Intelligence Around Us (2) | Recommender Systems |
| 4.1 | Frontiers of Artificial Intelligence | The Story of AlphaGo |
| 4.2 | Frontiers of Artificial Intelligence | OpenAI and Large Language Models |
| 4.3 | Frontiers of Artificial Intelligence | The Wonders of AI-Generated Video |
A.2.2Grade 3, Second Semester
| No. | Unit | Section |
|---|---|---|
| 1.1 | Human Intelligence | Forms of Human Intelligence |
| 1.2 | Human Intelligence | IQ and Its Measurement |
| 1.3 | Human Intelligence | Emotional Intelligence: Wisdom Beyond Reason |
| 2.1 | Origins of Artificial Intelligence (1) | Aristotle and the Syllogism |
| 2.2 | Origins of Artificial Intelligence (1) | The Story of George Boole |
| 2.3 | Origins of Artificial Intelligence (1) | Turing and the Turing Machine |
| 2.4 | Origins of Artificial Intelligence (1) | The Birth of the Computer |
| 3.1 | Origins of Artificial Intelligence (2) | Early Conceptions of Machine Intelligence |
| 3.2 | Origins of Artificial Intelligence (2) | The Turing Test |
| 3.3 | Origins of Artificial Intelligence (2) | The Turing Award |
| 4.1 | The Birth of Artificial Intelligence | On the Eve of Artificial Intelligence |
| 4.2 | The Birth of Artificial Intelligence | The Dartmouth Conference |
A.2.3Grade 4, First Semester
| No. | Unit | Section |
|---|---|---|
| 1.1 | The Golden Decade | The Success of ELIZA |
| 1.2 | The Golden Decade | The Story of Wu Wenjun |
| 1.3 | The Golden Decade | The Beginning of Machine Translation |
| 1.4 | The Golden Decade | The End of the Boom: AI’s First Winter |
| 2.1 | The Second Boom | Edward Feigenbaum and Expert Systems |
| 2.2 | The Second Boom | A Dream Fades: Japan’s Fifth-Generation Computer |
| 2.3 | The Second Boom | The Era of Robotic Insects |
| 3.1 | Steady Recovery | The Rise of Machine Learning |
| 3.2 | Steady Recovery | Deep Blue: A Crowning Achievement |
| 3.3 | Steady Recovery | Watson: An Encyclopedia That Thinks |
| 4.1 | The Present and Future | The Rise of Deep Learning |
| 4.2 | The Present and Future | The Era of Large Models |
| 4.3 | The Present and Future | Toward the Future |
A.2.4Grade 4, Second Semester
| No. | Unit | Section |
|---|---|---|
| 1.1 | AI and Medicine | Surgical Assistance Robots: A Physician’s Super Assistant |
| 1.2 | AI and Medicine | AI Reads Chest X-Rays: A Physician’s Intelligent Eye |
| 1.3 | AI and Medicine | AI Helps Develop Cancer Vaccines |
| 2.1 | AI and Scientific Research | AlphaFold: AI Predicts Protein Structures |
| 2.2 | AI and Scientific Research | AI Helps Mathematicians Form Conjectures |
| 2.3 | AI and Scientific Research | AI Supports Space Exploration |
| 2.4 | AI and Scientific Research | AI Enhances Microscopic Vision |
| 3.1 | AI and Everyday Life | AI Weather Forecasting |
| 3.2 | AI and Everyday Life | The AI Architect |
| 3.3 | AI and Everyday Life | AI Protects Wildlife |
| 4.1 | AI and Art | The AI Painter |
| 4.2 | AI and Art | The AI Musician |
| 4.3 | AI and Art | The AI Poet |
A.2.5Grade 5, First Semester
| No. | Unit | Section |
|---|---|---|
| 1.1 | AI Risks (1) | Information Fabrication |
| 1.2 | AI Risks (1) | Information Leakage |
| 1.3 | AI Risks (1) | Filter Bubbles |
| 2.1 | AI Risks (2) | AI Hallucinations |
| 2.2 | AI Risks (2) | Sycophantic Artificial Intelligence |
| 2.3 | AI Risks (2) | Will Robots Rebel Against Humans? |
| 3.1 | AI Ethics (1) | Artificial Intelligence and Social Fairness |
| 3.2 | AI Ethics (1) | Should AI Help Write School Essays? |
| 3.3 | AI Ethics (1) | Copyright Ownership of AI-Generated Works |
| 4.1 | AI Ethics (2) | Can AI Serve as a Judge? |
| 4.2 | AI Ethics (2) | Who Is Responsible When Autonomous Driving Fails? |
| 4.3 | AI Ethics (2) | Does an AI That Comforts You Truly Understand You? |
A.2.6Grade 5, Second Semester
| No. | Unit | Section |
|---|---|---|
| 1.1 | Computer Foundations | Understanding Computers |
| 1.2 | Computer Foundations | Computer Programs |
| 1.3 | Computer Foundations | Computer Operating Systems |
| 1.4 | Computer Foundations | Algorithms |
| 2.1 | Knowledge-Based Artificial Intelligence | Knowledge and Intelligence |
| 2.2 | Knowledge-Based Artificial Intelligence | Game-Playing Algorithms |
| 2.3 | Knowledge-Based Artificial Intelligence | Theorem Proving |
| 2.4 | Knowledge-Based Artificial Intelligence | Expert Systems |
| 2.5 | Knowledge-Based Artificial Intelligence | Knowledge Graphs |
| 3.1 | Foundational Concepts of Machine Learning | How Machines Learn from Examples |
| 3.2 | Foundational Concepts of Machine Learning | Learning with Answers: Supervised Learning |
| 3.3 | Foundational Concepts of Machine Learning | Learning Without Answers: Unsupervised Learning |
| 3.4 | Foundational Concepts of Machine Learning | Learning through Reward: Reinforcement Learning |
A.2.7Grade 6, First Semester
| No. | Unit | Section |
|---|---|---|
| 1.1 | Data and Models | Data and Artificial Intelligence |
| 1.2 | Data and Models | Preparing Data |
| 1.3 | Data and Models | Designing Models |
| 1.4 | Data and Models | Training Models |
| 2.1 | Common Machine-Learning Tasks | Regression Tasks |
| 2.2 | Common Machine-Learning Tasks | Classification Tasks |
| 2.3 | Common Machine-Learning Tasks | Clustering Tasks |
| 3.1 | Artificial Neural Networks (1) | The Human Nervous System |
| 3.2 | Artificial Neural Networks (1) | Pitts and His Neuron Model |
| 3.3 | Artificial Neural Networks (1) | The Perceptron: A Neural Network That Learns |
| 4.1 | Artificial Neural Networks (2) | Multilayer Perceptrons |
| 4.2 | Artificial Neural Networks (2) | Convolutional Neural Networks |
| 4.3 | Artificial Neural Networks (2) | Recurrent Neural Networks |
A.2.8Grade 6, Second Semester
| No. | Unit | Section |
|---|---|---|
| 1.1 | Deep Learning | The Story of Geoffrey Hinton |
| 1.2 | Deep Learning | The Secrets of Deep Learning |
| 1.3 | Deep Learning | Fei-Fei Li and the ImageNet Dataset |
| 1.4 | Deep Learning | GPUs: From Games to Artificial Intelligence |
| 1.5 | Deep Learning | Understanding AlphaGo |
| 2.1 | Large Models and Agents | Language Models |
| 2.2 | Large Models and Agents | Large Language Models |
| 2.3 | Large Models and Agents | Large Vision Models |
| 2.4 | Large Models and Agents | Large Multimodal Models |
| 2.5 | Large Models and Agents | Large Reasoning Models |
| 2.6 | Large Models and Agents | Agents |
| 3.1 | Challenges of Deep Learning | Intelligence That Is Hard to Understand |
| 3.2 | Challenges of Deep Learning | Adversarial Examples |
| 3.3 | Challenges of Deep Learning | Super-Agents |
Lower-Secondary School
B.1Integrated Stage Edition
B.1.1Textbook Contents
This appendix is based on the lower-secondary textbook in the Tsinghua Artificial Intelligence General Education Series for Primary, Secondary, and University Education.
| No. | Chapter | Lesson |
|---|---|---|
| 1 | Chapter 1 What Is Artificial Intelligence? | The Dream of Intelligent Machines |
| 2 | Chapter 1 What Is Artificial Intelligence? | What Is Artificial Intelligence? |
| 3 | Chapter 1 What Is Artificial Intelligence? | The Eyes of a Machine |
| 4 | Chapter 1 What Is Artificial Intelligence? | The Ears of a Machine |
| 5 | Chapter 1 What Is Artificial Intelligence? | The Voice of a Machine |
| 6 | Chapter 1 What Is Artificial Intelligence? | The Hands and Feet of a Machine |
| 7 | Chapter 2 The Birth of Artificial Intelligence | Origins of Human Intelligence |
| 8 | Chapter 2 The Birth of Artificial Intelligence | Formalizing the Laws of Human Thought |
| 9 | Chapter 2 The Birth of Artificial Intelligence | The Birth of the Computer |
| 10 | Chapter 2 The Birth of Artificial Intelligence | The Great Alan Turing |
| 11 | Chapter 2 The Birth of Artificial Intelligence | The Dartmouth Conference |
| 12 | Chapter 3 History of Artificial Intelligence | Dreams and Disillusionment |
| 13 | Chapter 3 History of Artificial Intelligence | The Era of Deep Learning |
| 14 | Chapter 3 History of Artificial Intelligence | The Era of Large Models |
| 15 | Chapter 3 History of Artificial Intelligence | Interdisciplinary Convergence |
| 16 | Chapter 3 History of Artificial Intelligence | Toward the Future |
| 17 | Chapter 4 Frontiers of Artificial Intelligence | AI and Board Games |
| 18 | Chapter 4 Frontiers of Artificial Intelligence | AI and Language |
| 19 | Chapter 4 Frontiers of Artificial Intelligence | AI and Art |
| 20 | Chapter 4 Frontiers of Artificial Intelligence | AI and Astronomy |
| 21 | Chapter 4 Frontiers of Artificial Intelligence | AI and Biology |
| 22 | Chapter 4 Frontiers of Artificial Intelligence | AI and Medicine |
| 23 | Chapter 5 AI Ethics | The Three Laws of Robotics |
| 24 | Chapter 5 AI Ethics | Information Fabrication |
| 25 | Chapter 5 AI Ethics | Information Leakage |
| 26 | Chapter 5 AI Ethics | Filter Bubbles |
| 27 | Chapter 5 AI Ethics | Artificial Intelligence and Social Fairness |
| 28 | Chapter 5 AI Ethics | Legal Responsibility |
| 29 | Chapter 6 Foundational AI Methods | Knowledge-Based Intelligence |
| 30 | Chapter 6 Foundational AI Methods | Learning-Based Intelligence |
| 31 | Chapter 6 Foundational AI Methods | Supervised and Unsupervised Learning |
| 32 | Chapter 6 Foundational AI Methods | Reinforcement Learning |
| 33 | Chapter 6 Foundational AI Methods | Traditions in Machine Learning |
| 34 | Chapter 7 Deep Learning Methods | The Human Nervous System |
| 35 | Chapter 7 Deep Learning Methods | The Beginning of Artificial Neural Networks |
| 36 | Chapter 7 Deep Learning Methods | History of Artificial Neural Networks |
| 37 | Chapter 7 Deep Learning Methods | The Beginning of Deep Learning |
| 38 | Chapter 7 Deep Learning Methods | Basic Principles of Deep Learning |
| 39 | Chapter 7 Deep Learning Methods | Deep-Learning Challenge: Adversarial Examples |
| 40 | Chapter 7 Deep Learning Methods | Deep-Learning Challenge: Explainability |
B.1.2Practice Manual Contents
Tsinghua High School Edition
This appendix is based on the lower-secondary practice manual in the series, Tsinghua High School Edition.
| No. | Activity Type | Activity |
|---|---|---|
| 1 | Analysis | Origins of Artificial Intelligence (1): The Mathematization of Human Thought |
| 2 | Analysis | Origins of Artificial Intelligence (2): The Birth of the Computer |
| 3 | Tool | Origins of Artificial Intelligence (3): The Great Alan Turing |
| 4 | Analysis | Origins of Artificial Intelligence (4): The Dartmouth Conference |
| 5 | Analysis | The Development of Artificial Intelligence (5): Dreams and Disillusionment |
| 6 | Tool | The Development of Artificial Intelligence (7): The New Era of Large Models |
| 7 | Survey, Debate | The Development of Artificial Intelligence (8): Interdisciplinary Convergence |
| 8 | Tool | Creating with AI and Art |
| 9 | Survey, Analysis | AI and Biology |
| 10 | Survey, Analysis | AI and Medicine |
| 11 | Analysis, Debate | The Three Laws of Robotics |
| 12 | Coding | Machine-Learning Examples |
| 13 | Analysis | The Human Nervous System |
| 14 | Analysis | The Beginning of Artificial Neural Networks |
| 15 | Analysis | History of Artificial Neural Networks |
| 16 | Analysis | Introduction to Convolutional Neural Networks |
| 17 | Analysis, Coding | Understanding Deep Learning: The Secrets of AlphaGo |
| 18 | Tool | Understanding Deep Learning: The Sora Video Generator |
| 19 | Analysis, Tool | Filter Bubbles and Algorithmic Recommendation |
| 20 | Debate, Tool | Legal and Ethical Responsibility for Artificial Intelligence |
Suzhou Xingpu Experimental Middle School Edition
This appendix is based on the lower-secondary practice manual in the series, Suzhou Xingpu Experimental Middle School Edition.
| No. | Part/Chapter | Activity Type | Activity |
|---|---|---|---|
| 1.1 | Part I | Survey | Presenting Artificial Intelligence at a Convention |
| 1.2 | Part I | Presentation | What If the Eyes of a Machine Become Biased? |
| 1.3 | Part I | Tool | Experiencing Speech Synthesis |
| 2.1 | Part II | Analysis | Experiencing the Development of Human Thought |
| 2.2 | Part II | Test | Can Machines Think Like Humans? |
| 2.3 | Part II | Drama | The Dartmouth Conference |
| 3.1 | Part III | Essay | The Course of AI Development |
| 3.2 | Part III | Tool | An In-Depth Experience of Large Models |
| 3.3 | Part III | Essay | Writing a Science-Fiction Story |
| 4.1 | Part IV | Coding | Gomoku Based on Alpha–Beta Pruning |
| 4.2 | Part IV | Tool | AI Art Generation |
| 4.3 | Part IV | Survey | Protein-Structure Analysis |
| 5.1 | Part V | Debate | The Autonomous-Vehicle Dilemma |
| 5.2 | Part V | Role Play | Simulating Personalized Recommendation |
| 5.3 | Part V | Debate | Artificial Intelligence and Social Fairness |
| 6.1 | Part VI | Tool | An Intelligent Fruit-Recognition System |
| 6.2 | Part VI | Analysis | Three Core Paradigms of Machine Learning |
| 7.1 | Part VII | Survey | Exploring Mechanisms of Self-Recovery |
| 7.2 | Part VII | Analysis | Visualizing Neural Networks |
| 7.3 | Part VII | Coding | Experiencing Deep Learning |
B.2Grade-Level Volumes
This appendix is based on the Grade 7 and Grade 8 textbooks in the series and lists units and sections by grade and semester.
B.2.1Grade 7, First Semester
| No. | Unit | Section |
|---|---|---|
| 1.1 | Concepts of Artificial Intelligence | The Dream of Intelligent Machines |
| 1.2 | Concepts of Artificial Intelligence | What Is Artificial Intelligence? |
| 1.3 | Concepts of Artificial Intelligence | The Eyes of a Machine |
| 1.4 | Concepts of Artificial Intelligence | The Ears of a Machine |
| 1.5 | Concepts of Artificial Intelligence | The Voice of a Machine |
| 1.6 | Concepts of Artificial Intelligence | The Hands and Feet of a Machine |
| 2.1 | The Birth of Artificial Intelligence | Origins of Human Intelligence |
| 2.2 | The Birth of Artificial Intelligence | Formalizing the Laws of Human Thought |
| 2.3 | The Birth of Artificial Intelligence | The Birth of the Computer |
| 2.4 | The Birth of Artificial Intelligence | The Great Alan Turing |
| 2.5 | The Birth of Artificial Intelligence | The Dartmouth Conference |
| 3.1 | The Development of Artificial Intelligence | Dreams and Disillusionment |
| 3.2 | The Development of Artificial Intelligence | The Era of Deep Learning |
| 3.3 | The Development of Artificial Intelligence | The Era of Large Models |
| 3.4 | The Development of Artificial Intelligence | Interdisciplinary Integration |
B.2.2Grade 7, Second Semester
| No. | Unit | Section |
|---|---|---|
| 1.1 | AI Ethics | The Three Laws of Robotics |
| 1.2 | AI Ethics | Information Fabrication |
| 1.3 | AI Ethics | Information Leakage |
| 1.4 | AI Ethics | Filter Bubbles |
| 1.5 | AI Ethics | Artificial Intelligence and Social Fairness |
| 1.6 | AI Ethics | Legal Responsibility |
| 2.1 | Frontiers of Artificial Intelligence | Artificial Intelligence and Games |
| 2.2 | Frontiers of Artificial Intelligence | AI and Language |
| 2.3 | Frontiers of Artificial Intelligence | AI and Art |
| 2.4 | Frontiers of Artificial Intelligence | AI and Astronomy |
| 2.5 | Frontiers of Artificial Intelligence | AI and Biology |
| 2.6 | Frontiers of Artificial Intelligence | AI and Medicine |
| 2.7 | Frontiers of Artificial Intelligence | AI and New Materials |
| 2.8 | Frontiers of Artificial Intelligence | AI and Mathematics |
| 2.9 | Frontiers of Artificial Intelligence | AI and Urban Transportation |
B.2.3Grade 8, First Semester
| No. | Unit | Section |
|---|---|---|
| 1.1 | Foundational AI Methods (1) | History of Computers |
| 1.2 | Foundational AI Methods (1) | Foundations of Computer Algorithms |
| 1.3 | Foundational AI Methods (1) | Knowledge-Based Intelligence |
| 1.4 | Foundational AI Methods (1) | Learning-Based Intelligence |
| 2.1 | Foundational AI Methods (2) | Machine-Learning Examples |
| 2.2 | Foundational AI Methods (2) | Supervised and Unsupervised Learning |
| 2.3 | Foundational AI Methods (2) | Reinforcement Learning |
| 2.4 | Foundational AI Methods (2) | Traditions in Machine Learning |
| 3.1 | Neural-Network Methods | The Human Nervous System |
| 3.2 | Neural-Network Methods | The Beginning of Artificial Neural Networks |
| 3.3 | Neural-Network Methods | History of Artificial Neural Networks |
| 3.4 | Neural-Network Methods | Introduction to Convolutional Neural Networks |
| 3.5 | Neural-Network Methods | Introduction to Recurrent Neural Networks |
B.2.4Grade 8, Second Semester
| No. | Unit | Section |
|---|---|---|
| 1.1 | Deep Learning Methods | The Beginning of Deep Learning |
| 1.2 | Deep Learning Methods | Basic Principles of Deep Learning |
| 1.3 | Deep Learning Methods | Word Vectors and Object Embeddings |
| 1.4 | Deep Learning Methods | Sequence-to-Sequence Models |
| 1.5 | Deep Learning Methods | Attention Mechanisms |
| 1.6 | Deep Learning Methods | Deep-Learning Challenge: Adversarial Examples |
| 1.7 | Deep Learning Methods | Deep-Learning Challenge: Explainability |
| 2.1 | Large Models | Neural Language Models |
| 2.2 | Large Models | Introduction to Transformers |
| 2.3 | Large Models | Pretraining Methods |
| 2.4 | Large Models | Human Alignment |
| 2.5 | Large Models | Large Multimodal Models |
| 2.6 | Large Models | Toward the Future |
Upper-Secondary School
C.1Integrated Stage Edition
C.1.1Textbook Contents
This appendix is based on the upper-secondary textbook in the Tsinghua Artificial Intelligence General Education Series for Primary, Secondary, and University Education.
| No. | Chapter | Lesson |
|---|---|---|
| 1 | Chapter 1 Introduction to Artificial Intelligence | What Is Artificial Intelligence? |
| 2 | Chapter 1 Introduction to Artificial Intelligence | Origins of Human Intelligence |
| 3 | Chapter 1 Introduction to Artificial Intelligence | Origins of Artificial Intelligence: Mathematical Logic |
| 4 | Chapter 1 Introduction to Artificial Intelligence | Origins of Artificial Intelligence: The Birth of the Computer |
| 5 | Chapter 1 Introduction to Artificial Intelligence | Turing: Father of Artificial Intelligence |
| 6 | Chapter 1 Introduction to Artificial Intelligence | The Beginning of Artificial Intelligence |
| 7 | Chapter 1 Introduction to Artificial Intelligence | History of Artificial Intelligence (1) |
| 8 | Chapter 1 Introduction to Artificial Intelligence | History of Artificial Intelligence (2) |
| 9 | Chapter 1 Introduction to Artificial Intelligence | AI Ethics (1): Immediate Risks from Artificial Intelligence |
| 10 | Chapter 1 Introduction to Artificial Intelligence | AI Ethics (2): Long-Term Risks from Artificial Intelligence |
| 11 | Chapter 2 Foundations of Artificial Intelligence | Knowledge-Based Artificial Intelligence |
| 12 | Chapter 2 Foundations of Artificial Intelligence | Learning-Based Intelligence |
| 13 | Chapter 2 Foundations of Artificial Intelligence | The Basic Machine-Learning Workflow |
| 14 | Chapter 2 Foundations of Artificial Intelligence | Machine-Learning Methods |
| 15 | Chapter 2 Foundations of Artificial Intelligence | Four Traditions in Machine Learning |
| 16 | Chapter 2 Foundations of Artificial Intelligence | Artificial Neural Networks |
| 17 | Chapter 2 Foundations of Artificial Intelligence | Representative Network Architectures |
| 18 | Chapter 2 Foundations of Artificial Intelligence | Deep Learning |
| 19 | Chapter 2 Foundations of Artificial Intelligence | Basic Principles of Large Models (1) |
| 20 | Chapter 2 Foundations of Artificial Intelligence | Basic Principles of Large Models (2) |
| 21 | Chapter 3 AI Applications | Machine Vision: Facial Recognition |
| 22 | Chapter 3 AI Applications | Machine Vision: The Master Painter |
| 23 | Chapter 3 AI Applications | Machine Vision: Fabrication and Detection |
| 24 | Chapter 3 AI Applications | Machine Audition: Speech Recognition |
| 25 | Chapter 3 AI Applications | Machine Audition: Speech Synthesis |
| 26 | Chapter 3 AI Applications | Language Understanding: Machine Translation |
| 27 | Chapter 3 AI Applications | Human–AI Game Playing: The Secrets Behind AlphaGo |
| 28 | Chapter 3 AI Applications | Human–AI Game Playing: The AI Gamer |
| 29 | Chapter 3 AI Applications | Information Retrieval: The Secrets of Search Engines |
| 30 | Chapter 3 AI Applications | Information Retrieval: Recommendation Algorithms |
| 31 | Chapter 4 Interdisciplinary Artificial Intelligence | Working with Mathematicians |
| 32 | Chapter 4 Interdisciplinary Artificial Intelligence | Imitating a Bat’s Ears |
| 33 | Chapter 4 Interdisciplinary Artificial Intelligence | Solving Protein Structures |
| 34 | Chapter 4 Interdisciplinary Artificial Intelligence | Reconstructing Material Microstructures |
| 35 | Chapter 4 Interdisciplinary Artificial Intelligence | Predicting Chemical Reactions |
| 36 | Chapter 4 Interdisciplinary Artificial Intelligence | The Astronomer’s Assistant |
| 37 | Chapter 4 Interdisciplinary Artificial Intelligence | The AI Composer |
| 38 | Chapter 4 Interdisciplinary Artificial Intelligence | Detecting Anthrax Spores |
| 39 | Chapter 4 Interdisciplinary Artificial Intelligence | Developing Cancer Vaccines |
| 40 | Chapter 4 Interdisciplinary Artificial Intelligence | Toward the Future |
C.1.2Practice Manual Contents
This appendix is based on the upper-secondary practice manual in the series, Tsinghua High School Daxing Edition.
| No. | Part/Chapter | Activity Type | Activity |
|---|---|---|---|
| 1.1 | Part I Introduction to Artificial Intelligence | Tool | Mapping the History of Artificial Intelligence |
| 1.2 | Part I Introduction to Artificial Intelligence | Survey | A Survey of AI Risks |
| 2.1 | Part II Foundations of Artificial Intelligence | Analysis | How AI Can Help Address Marine Pollution |
| 2.2 | Part II Foundations of Artificial Intelligence | Survey | Machine Learning |
| 2.3 | Part II Foundations of Artificial Intelligence | Analysis | Exploring Artificial Neural Networks |
| 2.4 | Part II Foundations of Artificial Intelligence | Analysis | How Transformers Work |
| 3.1 | Part III AI Applications | Coding | Qingxing Smart Cafeteria |
| 3.2 | Part III AI Applications | Coding | Qingxing Smart Parking |
| 3.3 | Part III AI Applications | Tool | Designing a Qingxing Magazine Cover |
| 3.4 | Part III AI Applications | Coding | Qingxing Intelligent Audio Guide |
| 3.5 | Part III AI Applications | Coding | Qingxing Technology Corner: Game-Playing Robot (1) |
| 3.6 | Part III AI Applications | Coding | Qingxing Technology Corner: Game-Playing Robot (2) |
| 4.1 | Part IV Frontiers of Artificial Intelligence | Tool | Tessellating with AI |
| 4.2 | Part IV Frontiers of Artificial Intelligence | Survey | Solving the Mystery of Protein Structure |
| 4.3 | Part IV Frontiers of Artificial Intelligence | Tool | Creating the Qingxing School Song |
C.2Grade-Level Volumes
This appendix is based on the Grade 10 and Grade 11 textbooks in the series and lists units and sections by grade and semester.
C.2.1Grade 10, First Semester
| No. | Unit | Section |
|---|---|---|
| 1.1 | Origins of Artificial Intelligence | What Is Artificial Intelligence? |
| 1.2 | Origins of Artificial Intelligence | Origins of Human Intelligence |
| 1.3 | Origins of Artificial Intelligence | Origins of Artificial Intelligence: Mathematical Logic |
| 1.4 | Origins of Artificial Intelligence | Origins of Artificial Intelligence: The Birth of the Computer |
| 1.5 | Origins of Artificial Intelligence | Turing: Father of Artificial Intelligence |
| 2.1 | The Development of Artificial Intelligence | The Beginning of Artificial Intelligence |
| 2.2 | The Development of Artificial Intelligence | History of Artificial Intelligence (1) |
| 2.3 | The Development of Artificial Intelligence | History of Artificial Intelligence (2) |
| 2.4 | The Development of Artificial Intelligence | Astonishing Artificial Intelligence |
| 3.1 | AI Ethics | AI Ethics: Immediate Risks |
| 3.2 | AI Ethics | AI Ethics: Long-Term Risks |
| 3.3 | AI Ethics | Compliant Use of AI Tools |
C.2.2Grade 10, Second Semester
| No. | Unit | Section |
|---|---|---|
| 1.1 | AI Methods | Knowledge-Based Artificial Intelligence |
| 1.2 | AI Methods | Learning-Based Artificial Intelligence |
| 1.3 | AI Methods | The Basic Machine-Learning Workflow |
| 1.4 | AI Methods | Machine-Learning Methods |
| 2.1 | Deep Learning | An Introduction to Artificial Neural Networks |
| 2.2 | Deep Learning | Representative Neural Networks |
| 2.3 | Deep Learning | Foundations of Deep Learning |
| 2.4 | Deep Learning | Frontiers of Deep Learning (1) |
| 2.5 | Deep Learning | Frontiers of Deep Learning (2) |
| 3.1 | Large Models | Basic Principles of Large Models (1) |
| 3.2 | Large Models | Basic Principles of Large Models (2) |
C.2.3Grade 11, First Semester
| No. | Unit | Section |
|---|---|---|
| 1.1 | Machine Vision | Facial Recognition |
| 1.2 | Machine Vision | Image Generation |
| 1.3 | Machine Vision | Fabrication and Detection |
| 2.1 | Machine Audition | Speech Recognition |
| 2.2 | Machine Audition | Speaker Recognition |
| 2.3 | Machine Audition | Speech Synthesis |
| 3.1 | Language Processing | Machine Translation |
| 3.2 | Language Processing | Machine Writers |
| 3.3 | Language Processing | Machine Poets |
| 4.1 | Human–AI Game Playing | AlphaGo |
| 4.2 | Human–AI Game Playing | Artificial Intelligence and Games |
| 5.1 | Search and Recommendation | Search Engines |
| 5.2 | Search and Recommendation | Recommendation Algorithms |
C.2.4Grade 11, Second Semester
| No. | Unit | Section |
|---|---|---|
| 1.1 | AI in Mathematics and Engineering | Working with Mathematicians |
| 1.2 | AI in Mathematics and Engineering | Imitating a Bat’s Ears |
| 1.3 | AI in Mathematics and Engineering | The Astronomer’s Assistant |
| 2.1 | AI in Materials Science and Chemistry | Reconstructing Three-Dimensional Material Microstructures |
| 2.2 | AI in Materials Science and Chemistry | Predicting Classes of Chemical Reactions |
| 2.3 | AI in Materials Science and Chemistry | Solving Protein Structures |
| 3.1 | AI in Biology and Medicine | Detecting Anthrax Bacilli |
| 3.2 | AI in Biology and Medicine | Developing Cancer Vaccines |
| 3.3 | AI in Biology and Medicine | Predicting Health Status |
| 4.1 | AI in the Humanities and Social Sciences | Deciphering Ancient Scripts |
| 4.2 | AI in the Humanities and Social Sciences | AI Composition |
| 4.3 | AI in the Humanities and Social Sciences | Toward the Future |
Higher Education
D.1Textbook Contents
This appendix is based on the Tsinghua textbook Artificial Intelligence General Education: Principles, Methods, Applications, and Interdisciplinary Integration.
| No. | Part | Chapter |
|---|---|---|
| 1 | Part I Introduction to Artificial Intelligence | What Is Artificial Intelligence? |
| 2 | Part I Introduction to Artificial Intelligence | Origins of Human Intelligence |
| 3 | Part I: Introduction to Artificial Intelligence | Origins of Artificial Intelligence: Mathematical Logic |
| 4 | Part I: Introduction to Artificial Intelligence | Origins of Artificial Intelligence: The Birth of the Computer |
| 5 | Part I Introduction to Artificial Intelligence | Turing: Father of Artificial Intelligence |
| 6 | Part I Introduction to Artificial Intelligence | The Beginning of Artificial Intelligence |
| 7 | Part I Introduction to Artificial Intelligence | History of Artificial Intelligence |
| 8 | Part I: Introduction to Artificial Intelligence | Astonishing Artificial Intelligence |
| 9 | Part I Introduction to Artificial Intelligence | AI Risk and Ethics |
| 10 | Part II Foundations of Artificial Intelligence | Knowledge-Based Artificial Intelligence |
| 11 | Part II Foundations of Artificial Intelligence | Learning-Based Artificial Intelligence |
| 12 | Part II Foundations of Artificial Intelligence | The Basic Machine-Learning Workflow |
| 13 | Part II Foundations of Artificial Intelligence | Machine-Learning Methods |
| 14 | Part II Foundations of Artificial Intelligence | Machine-Learning Strategies |
| 15 | Part II Foundations of Artificial Intelligence | An Introduction to Artificial Neural Networks |
| 16 | Part II Foundations of Artificial Intelligence | Representative Neural-Network Architectures |
| 17 | Part II Foundations of Artificial Intelligence | Foundations of Deep Learning |
| 18 | Part II: Foundations of Artificial Intelligence | Frontiers of Deep Learning (1) |
| 19 | Part II: Foundations of Artificial Intelligence | Frontiers of Deep Learning (2) |
| 20 | Part II: Foundations of Artificial Intelligence | Basic Principles of Large Models (1) |
| 21 | Part II: Foundations of Artificial Intelligence | Basic Principles of Large Models (2) |
| 22 | Part II Foundations of Artificial Intelligence | Challenges Facing Deep Learning |
| 23 | Part III: AI Applications | Machine Vision: Facial Recognition |
| 24 | Part III: AI Applications | Machine Vision: License-Plate Recognition |
| 25 | Part III: AI Applications | Machine Vision: AI Beautification |
| 26 | Part III: AI Applications | Machine Vision: The Master Painter |
| 27 | Part III: AI Applications | Machine Vision: Detecting AI-Generated Fakes |
| 28 | Part III: AI Applications | Machine Audition: Speech Recognition |
| 29 | Part III: AI Applications | Machine Audition: Speaker Recognition |
| 30 | Part III: AI Applications | Machine Audition: Speech Synthesis |
| 31 | Part III: AI Applications | Language Understanding: Writing and Dialogue |
| 32 | Part III: AI Applications | Language Processing: The AI Poet |
| 33 | Part III: AI Applications | Language Processing: Machine Translation |
| 34 | Part III: AI Applications | Human–AI Game Playing: Mastering Go |
| 35 | Part III: AI Applications | Human–AI Game Playing: AI in Video Games |
| 36 | Part III AI Applications | Robot Vacuum Cleaners |
| 37 | Part III AI Applications | Search Engines |
| 38 | Part III AI Applications | Recommendation Algorithms |
| 39 | Part IV: Interdisciplinary Artificial Intelligence | Solving the Mystery of Protein Structure |
| 40 | Part IV: Interdisciplinary Artificial Intelligence | Reconstructing Three-Dimensional Material Microstructures |
| 41 | Part IV: Interdisciplinary Artificial Intelligence | Predicting Classes of Chemical Reactions |
| 42 | Part IV: Interdisciplinary Artificial Intelligence | Evidence for Biomimicry |
| 43 | Part IV: Interdisciplinary Artificial Intelligence | Locating Sound Sources |
| 44 | Part IV: Interdisciplinary Artificial Intelligence | Detecting Anthrax |
| 45 | Part IV: Interdisciplinary Artificial Intelligence | Space Exploration |
| 46 | Part IV: Interdisciplinary Artificial Intelligence | AI Composition |
| 47 | Part IV: Interdisciplinary Artificial Intelligence | Working with Mathematicians |
| 48 | Part IV: Interdisciplinary Artificial Intelligence | Machines That Dream |
| 49 | Part IV: Interdisciplinary Artificial Intelligence | The Astronomer’s Assistant |
| 50 | Part IV: Interdisciplinary Artificial Intelligence | Predicting COVID-19 Infectivity |
| 51 | Part IV: Interdisciplinary Artificial Intelligence | Developing Cancer Vaccines |
| 52 | Part IV: Interdisciplinary Artificial Intelligence | AI-Enhanced Microscopy |
| 53 | Part IV Interdisciplinary Artificial Intelligence | Toward the Future |
D.2Practice Manual Contents
This appendix is based on the practice manual accompanying the Tsinghua edition of Artificial Intelligence General Education: Principles, Methods, Applications, and Interdisciplinary Integration.
| No. | Part/Chapter | Activity Type | Activity |
|---|---|---|---|
| 1.1 | Part I | Survey | AI Literacy |
| 1.2 | Part I | Debate | Multiple Intelligences vs. General Intelligence |
| 1.3 | Part I | Essay | The Turing–Searle Debate |
| 1.4 | Part I | Tool | A Professional Science-Communication Agent |
| 1.5 | Part I | Essay | Humans and Robots |
| 2.1 | Part II | Debate | Is a Silicon-Based Civilization Possible? A Human–AI Debate |
| 2.2 | Part II | Coding | Linear Classification Models |
| 2.3 | Part II | Analysis | Visualizing Neural Networks |
| 2.4 | Part II | Analysis | Visualizing Convolutional Neural Networks |
| 2.5 | Part II | Analysis | Understanding Convolutional Neural Networks in Depth |
| 2.6 | Part II | Analysis | Understanding Transformers in Depth |
| 2.7 | Part II | Tool | Generating a Nature Cover |
| 2.8 | Part II | Survey | Chain of Thought in Large-Model Technology |
| 2.9 | Part II | Survey | Adversarial Examples |
| 3.1 | Part III | Coding | A Facial-Recognition System |
| 3.2 | Part III | Coding | A License-Plate-Recognition System |
| 3.3 | Part III | Tool | Star of Art |
| 3.4 | Part III | Coding | Speech Synthesis and Voice Cloning |
| 3.5 | Part III | Essay | Large Language Models and Academic Integrity |
| 3.6 | Part III | Tool | The AI Poet |
| 3.7 | Part III | Coding | Master of Gomoku |
| 3.8 | Part III | Essay | The Benefits and Harms of Commercial Ranking in Search Engines |
| 4.1 | Part IV | Survey | A Deployment Plan for the GNoME System |
| 4.2 | Part IV | Survey | Feasibility Analysis for an AI+Chemistry Project |
| 4.3 | Part IV | Tool | The AI Composer |
| 4.4 | Part IV | Survey | Forecasting the Development of AI in Medicine |
| 4.5 | Part IV | Survey | An Urban AI Development Plan |
Lifelong Learning
E.1Textbook Contents
This appendix is based on the Tsinghua edition of Artificial Intelligence General Education: Lifelong Learning Edition.
| No. | Chapter (Unit) | Section |
|---|---|---|
| 1.1 | Artificial Intelligence: The Future Is Here | The Dream of Intelligent Machines |
| 1.2 | Artificial Intelligence: The Future Is Here | What Is Artificial Intelligence? |
| 1.3 | Artificial Intelligence: The Future Is Here | A Brief History of Artificial Intelligence |
| 1.4 | Artificial Intelligence: The Future Is Here | The Power of Artificial Intelligence |
| 2.1 | Artificial Intelligence Around Us | The Eyes of a Machine |
| 2.2 | Artificial Intelligence Around Us | The Ears of a Machine |
| 2.3 | Artificial Intelligence Around Us | The Voice of a Machine |
| 2.4 | Artificial Intelligence Around Us | The Hands and Feet of a Machine |
| 2.5 | Artificial Intelligence Around Us | Search and Recommendation |
| 2.6 | Artificial Intelligence Around Us | Language Understanding |
| 3.1 | AI Foundations: Deep Learning | Machine Intelligence and Machine Learning |
| 3.2 | AI Foundations: Deep Learning | The Story of Geoffrey Hinton |
| 3.3 | AI Foundations: Deep Learning | The Secrets of Deep Learning |
| 3.4 | AI Foundations: Deep Learning | Data and Computation |
| 3.5 | AI Foundations: Deep Learning | Understanding AlphaGo |
| 4.1 | AI Frontiers: Large Models and Agents | Language Models |
| 4.2 | AI Frontiers: Large Models and Agents | Large Language Models |
| 4.3 | AI Frontiers: Large Models and Agents | Large Vision Models |
| 4.4 | AI Frontiers: Large Models and Agents | Large Multimodal Models |
| 4.5 | AI Frontiers: Large Models and Agents | Large Reasoning Models |
| 4.6 | AI Frontiers: Large Models and Agents | Agents |
| 5.1 | AI Ethics | The Three Laws of Robotics |
| 5.2 | AI Ethics | Information Fabrication |
| 5.3 | AI Ethics | Information Leakage |
| 5.4 | AI Ethics | Filter Bubbles |
| 5.5 | AI Ethics | Artificial Intelligence and Social Fairness |
| 5.6 | AI Ethics | Legal Responsibility |
| 6.1 | AI as a Companion in Everyday Life | AI Question Answering: Using AI to Obtain Information |
| 6.2 | AI as a Companion in Everyday Life | AI-Assisted Writing |
| 6.3 | AI as a Companion in Everyday Life | AI Poetry: Let AI Help You Express Your Talent |
| 6.4 | AI as a Companion in Everyday Life | AI Artist: Greeting Cards, Posters, and Image Editing |
| 6.5 | AI as a Companion in Everyday Life | AI Video: Memories, Greetings, and Records |
| 6.6 | AI as a Companion in Everyday Life | AI Music: A Song of Your Own |
| 7.1 | Looking to the Future | Trends in AI Development |
| 7.2 | Looking to the Future | Directions in AI Research |
| 7.3 | Looking to the Future | Frontier Developments in AI for Healthy Living |
E.2Practice Manual Contents
This appendix is based on the Practice Guide to Artificial Intelligence General Education: Lifelong Learning Edition.
| No. | Topic | Section |
|---|---|---|
| 1.1 | An Easy First Step into AI | Using Smartphone Apps |
| 1.2 | An Easy First Step into AI | Basic Intelligence in Smartphone Software |
| 2.1 | AI Tools | What Are AI Tools? |
| 2.2 | AI Tools | Finding AI Tools |
| 2.3 | AI Tools | Getting Started with AI Tools |
| 3 | Understanding Artificial Intelligence | |
| 4.1 | AI Question Answering: Using AI to Obtain Information | Health and Medicine |
| 4.2 | AI Question Answering: Using AI to Obtain Information | Shopping and Consumption |
| 4.3 | AI Question Answering: Using AI to Obtain Information | Travel and Transportation |
| 4.4 | AI Question Answering: Using AI to Obtain Information | Administrative and Public Services |
| 4.5 | AI Question Answering: Using AI to Obtain Information | Household Tasks and Smartphone Use |
| 5 | AI-Assisted Writing | |
| 6 | AI Poetry: Let AI Help You Express Your Talent | |
| 7 | AI Artist: Greeting Cards, Posters, and Image Editing | |
| 8 | AI Video: Memories, Greetings, and Records | |
| 9 | AI Music: A Song of Your Own | |
| 10 | Safety Guide | |
| 11 | Frontier Applications and Creation | |
| 12 | Creative Portfolio |
References
- Adams, C., P. Pente, G. Lemermeyer, and G Rockwell. 2023. “Ethical Principles for Artificial Intelligence in k-12 Education.” Computers and Education: Artificial Intelligence 4: 100131. https://doi.org/10.1016/j.caeai.2023.100131.
- AI4K12 Initiative. 2019. “Five Big Ideas in Artificial Intelligence and k–12 AI Guidelines.” Association for the Advancement of Artificial Intelligence; Computer Science Teachers Association. 2019. https://ai4k12.org/.
- Alayrac, Jean-Baptiste, Jeff Donahue, Pauline Luc, and others. 2022. “Flamingo: A Visual Language Model for Few-Shot Learning.” In Advances in Neural Information Processing Systems, 35:23716–36.
- Arum, Richard, and Josipa Roksa. 2011. Academically Adrift: Limited Learning on College Campuses. Chicago: University of Chicago Press.
- Bahdanau, D., K. Cho, and Y Bengio. 2015. “Neural Machine Translation by Jointly Learning to Align and Translate.” In International Conference on Learning Representations.
- Bakshy, E., S. Messing, and L. A Adamic. 2015. “Exposure to Ideologically Diverse News and Opinion on Facebook.” Science 348 (6239): 1130–32.
- Balkin, Jack M. 2018. “Free Speech in the Algorithmic Society: Big Data, Private Governance, and New School Speech Regulation.” UC Davis Law Review 51 (3): 1149–1210. https://lawreview.law.ucdavis.edu/archives/51/3/free-speech-algorithmic-society-big-data-private-governance-and-new-school-speech.
- Barnett, Susan M., and Stephen J. Ceci. 2002. “When and Where Do We Apply What We Learn? A Taxonomy for Far Transfer.” Psychological Bulletin 128 (4): 612–37. https://doi.org/10.1037/0033-2909.128.4.612.
- Beijing Municipal Education Commission. 2025. “Beijing Action Plan for Advancing AI Education in Primary and Secondary Schools (2025–2027).” https://jw.beijing.gov.cn/xxgk/2024zcwj/2024qtwj/202503/t20250307_4028227.html.
- Bengio, Yoshua, Geoffrey Hinton, Andrew Yao, Dawn Song, Pieter Abbeel, Trevor Darrell, Yuval Noah Harari, et al. 2024. “Managing Extreme AI Risks Amid Rapid Progress.” Science 384 (6698): 842–45. https://doi.org/10.1126/science.adn0117.
- Biesta, Gert. 2010. Good Education in an Age of Measurement: Ethics, Politics, Democracy. Boulder, CO: Paradigm Publishers.
- Biggs, John, and Catherine Tang. 2011. Teaching for Quality Learning at University. 4th ed. Maidenhead: Open University Press.
- Bishop, C. M. 2006. Pattern Recognition and Machine Learning. Springer.
- Bommasani, Rishi, Drew A. Hudson, Ehsan Adeli, and others. 2021. “On the Opportunities and Risks of Foundation Models.” 2021. https://arxiv.org/abs/2108.07258.
- Boole, George. 1854. An Investigation of the Laws of Thought, on Which Are Founded the Mathematical Theories of Logic and Probabilities. London: Walton; Maberly.
- Borrego, Maura, and Lynita K. Newswander. 2010. “Definitions of Interdisciplinary Research: Toward Graduate-Level Interdisciplinary Learning Outcomes.” The Review of Higher Education 34 (1): 61–84. https://doi.org/10.1353/rhe.2010.0006.
- Bransford, John D., Ann L. Brown, and Rodney R. Cocking, eds. 2000. How People Learn: Brain, Mind, Experience, and School: Expanded Edition. Washington, DC: National Academies Press. https://doi.org/10.17226/9853.
- Brennan, Karen, and Mitchel Resnick. 2012. “New Frameworks for Studying and Assessing the Development of Computational Thinking.” In Proceedings of the 2012 Annual Meeting of the American Educational Research Association. Vancouver, Canada. https://web.media.mit.edu/~kbrennan/files/Brennan_Resnick_AERA2012_CT.pdf.
- Bresnahan, Timothy F., and Manuel Trajtenberg. 1995. “General Purpose Technologies: Engines of Growth?” Journal of Econometrics 65 (1): 83–108. https://doi.org/10.1016/0304-4076(94)01598-t.
- Brin, Sergey, and Lawrence Page. 1998. “The Anatomy of a Large-Scale Hypertextual Web Search Engine.” Computer Networks and ISDN Systems 30 (1–7): 107–17. https://doi.org/10.1016/s0169-7552(98)00110-x.
- Brown, Peter F., Stephen A. Della Pietra, Vincent J. Della Pietra, and Robert L. Mercer. 1993. “The Mathematics of Statistical Machine Translation: Parameter Estimation.” Computational Linguistics 19 (2): 263–311.
- Brown, Tom B., Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, et al. 2020. “Language Models Are Few-Shot Learners.” In Advances in Neural Information Processing Systems, 33:1877–1901.
- Bruner, Jerome S. 1960. The Process of Education. Cambridge, MA: Harvard University Press.
- Buolamwini, Joy, and Timnit Gebru. 2018. “Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification.” In Proceedings of the 1st Conference on Fairness, Accountability and Transparency, 81:77–91. Proceedings of Machine Learning Research.
- Cadena, Cesar, Luca Carlone, Henry Carrillo, and others. 2016. “Past, Present, and Future of Simultaneous Localization and Mapping: Toward the Robust-Perception Age.” IEEE Transactions on Robotics 32 (6): 1309–32. https://doi.org/10.1109/tro.2016.2624754.
- Chaney, Allison J. B., Brandon M. Stewart, and Barbara E. Engelhardt. 2018. “How Algorithmic Confounding in Recommendation Systems Increases Homogeneity and Decreases Utility.” In Proceedings of the 12th ACM Conference on Recommender Systems, 224–32. Association for Computing Machinery. https://doi.org/10.1145/3240323.3240370.
- Chi, Michelene T. H., Paul J. Feltovich, and Robert Glaser. 1981. “Categorization and Representation of Physics Problems by Experts and Novices.” Cognitive Science 5 (2): 121–52. https://doi.org/10.1207/s15516709cog0502_2.
- China et al., Cyberspace Administration of. 2023. “Interim Measures for the Management of Generative Artificial Intelligence Services.” 2023. https://www.cac.gov.cn/2023-07/13/c_1690898327029107.htm.
- Chinn, Clark A., Luke A. Buckland, and Ala Samarapungavan. 2011. “Expanding the Dimensions of Epistemic Cognition: Arguments from Philosophy and Psychology.” Educational Psychologist 46 (3): 141–67. https://doi.org/10.1080/00461520.2011.587722.
- Columbia College. 2026. “Contemporary Civilization.” 2026. https://www.college.columbia.edu/core-curriculum/classes/contemporary-civilization.
- Covington, Paul, Jay Adams, and Emre Sargin. 2016. “Deep Neural Networks for YouTube Recommendations.” In Proceedings of the 10th ACM Conference on Recommender Systems, 191–98. https://doi.org/10.1145/2959100.2959190.
- Cyberspace Administration of China, Ministry of Industry and Information Technology, and Ministry of Public Security. 2022. “Provisions on the Administration of Deep Synthesis Internet Information Services.” 2022. https://www.cac.gov.cn/2022-12/11/c_1672221949354811.htm.
- Cyberspace Administration of China, Ministry of Industry and Information Technology, Ministry of Public Security, and National Radio and Television Administration. 2025. “Measures for Labeling AI-Generated and Synthetic Content.” 2025. https://www.cac.gov.cn/2025-03/14/c_1743654684782215.htm.
- Davies, Alex, Petar Veličković, Lars Buesing, and others. 2021. “Advancing Mathematics by Guiding Human Intuition with AI.” Nature 600: 70–74. https://doi.org/10.1038/s41586-021-04086-x.
- Devlin, Jacob, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. “BERT: Pre-Training of Deep Bidirectional Transformers for Language Understanding.” In Proceedings of NAACL-HLT, 4171–86. https://doi.org/10.18653/v1/n19-1423.
- Dewey, John. 1916. Democracy and Education: An Introduction to the Philosophy of Education. New York: Macmillan. https://www.gutenberg.org/ebooks/852.
- Dijck, José van, Thomas Poell, and Martijn de Waal. 2018. The Platform Society: Public Values in a Connective World. New York: Oxford University Press. https://doi.org/10.1093/oso/9780190889760.001.0001.
- diSessa, A. A. 1993. “Toward an Epistemology of Physics.” Cognition and Instruction 10 (2–3): 105–225.
- Du, Shan, Mahmoud Ibrahim, Mohamed Shehata, and Wael Badawy. 2013. “Automatic License Plate Recognition (ALPR): A State-of-the-Art Review.” IEEE Transactions on Circuits and Systems for Video Technology 23 (2): 311–25. https://doi.org/10.1109/tcsvt.2012.2203741.
- Durrant-Whyte, Hugh, and Tim Bailey. 2006. “Simultaneous Localization and Mapping: Part i.” IEEE Robotics and Automation Magazine 13 (2): 99–110. https://doi.org/10.1109/mra.2006.1638022.
- Eliasmith, Chris, Terrence C. Stewart, Xuan Choo, Trevor Bekolay, Travis DeWolf, Yichuan Tang, and Daniel Rasmussen. 2012. “A Large-Scale Model of the Functioning Brain.” Science 338 (6111): 1202–5. https://doi.org/10.1126/science.1225266.
- Eppler, Martin J., and Jeanne Mengis. 2004. “The Concept of Information Overload: A Review of Literature from Organization Science, Accounting, Marketing, MIS, and Related Disciplines.” The Information Society 20 (5): 325–44. https://doi.org/10.1080/01972240490507974.
- Espeland, W. N., and M Sauder. 2007. “Rankings and Reactivity: How Public Measures Recreate Social Worlds.” American Journal of Sociology 113 (1): 1–40.
- European Union. 2024. “Regulation (EU) 2024/1689 of the European Parliament and of the Council Laying down Harmonised Rules on Artificial Intelligence.” 2024. https://eur-lex.europa.eu/eli/reg/2024/1689/oj.
- Facione, P. A. 1990. “Critical Thinking: A Statement of Expert Consensus for Purposes of Educational Assessment and Instruction.” The Delphi Report. Millbrae, CA: California Academic Press.
- Feigenbaum, Edward A. 1977. “The Art of Artificial Intelligence: Themes and Case Studies of Knowledge Engineering.” In Proceedings of the International Joint Conference on Artificial Intelligence, 5:1014–29.
- Flavell, John H. 1979. “Metacognition and Cognitive Monitoring: A New Area of Cognitive-Developmental Inquiry.” American Psychologist 34 (10): 906–11. https://doi.org/10.1037/0003-066x.34.10.906.
- Fleming, Stephen M., and Raymond J. Dolan. 2012. “The Neural Basis of Metacognitive Ability.” Philosophical Transactions of the Royal Society B: Biological Sciences 367 (1594): 1338–49. https://doi.org/10.1098/rstb.2011.0417.
- Flexner, Abraham. 1930. Universities: American, English, German. New York: Oxford University Press.
- Floridi, L., J. Cowls, M. Beltrametti, R. Chatila, P. Chazerand, V. Dignum, C. Luetge, et al. 2018. “AI4People—an Ethical Framework for a Good AI Society: Opportunities, Risks, Principles, and Recommendations.” Minds and Machines 28: 689–707.
- Fong, Geoffrey T., David H. Krantz, and Richard E. Nisbett. 1986. “The Effects of Statistical Training on Thinking about Everyday Problems.” Cognitive Psychology 18 (3): 253–92. https://doi.org/10.1016/0010-0285(86)90001-0.
- Gatys, Leon A., Alexander S. Ecker, and Matthias Bethge. 2016. “Image Style Transfer Using Convolutional Neural Networks.” In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2414–23. https://doi.org/10.1109/cvpr.2016.265.
- Geirhos, R., J.-H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F. A Wichmann. 2020. “Shortcut Learning in Deep Neural Networks.” Nature Machine Intelligence 2: 665–73.
- Gigerenzer, Gerd, and Ulrich Hoffrage. 1995. “How to Improve Bayesian Reasoning Without Instruction: Frequency Formats.” Psychological Review 102 (4): 684–704. https://doi.org/10.1037/0033-295x.102.4.684.
- Gillespie, T. 2014. “The Relevance of Algorithms.” In Media Technologies: Essays on Communication, Materiality, and Society, edited by P. J. Boczkowski T. Gillespie and K. A. Foot, 167–94. MIT Press.
- Goodfellow, Ian, Jean Pouget-Abadie, Mehdi Mirza, and others. 2014. “Generative Adversarial Nets.” In Advances in Neural Information Processing Systems, 27:2672–80.
- Graves, Alex, Santiago Fernández, Faustino Gomez, and Jürgen Schmidhuber. 2006. “Connectionist Temporal Classification: Labelling Unsegmented Sequence Data with Recurrent Neural Networks.” In Proceedings of the 23rd International Conference on Machine Learning, 369–76. https://doi.org/10.1145/1143844.1143891.
- Griffiths, T. L., and J. B Tenenbaum. 2006. “Optimal Predictions in Everyday Cognition.” Psychological Science 17 (9): 767–73.
- Guo, Chuan, Geoff Pleiss, Yu Sun, and Kilian Q. Weinberger. 2017. “On Calibration of Modern Neural Networks.” In Proceedings of the 34th International Conference on Machine Learning, 70:1321–30. Proceedings of Machine Learning Research.
- Halliday, M. A. K. 1978. Language as Social Semiotic: The Social Interpretation of Language and Meaning. Edward Arnold.
- Hardwig, J. 1985. “Epistemic Dependence.” The Journal of Philosophy 82 (7): 335–49.
- Harvard University Committee on the Objectives of a General Education in a Free Society. 1945. General Education in a Free Society. Cambridge, MA: Harvard University Press.
- Hendrycks, D., and T Dietterich. 2019. “Benchmarking Neural Network Robustness to Common Corruptions and Perturbations.” In International Conference on Learning Representations.
- Hestenes, D. 1992. “Modeling Games in the Newtonian World.” American Journal of Physics 60 (8): 732–48.
- Hidi, Suzanne, and K. Ann Renninger. 2006. “The Four-Phase Model of Interest Development.” Educational Psychologist 41 (2): 111–27. https://doi.org/10.1207/s15326985ep4102_4.
- Hinton, Geoffrey, Li Deng, Dong Yu, George E. Dahl, Abdel-rahman Mohamed, Navdeep Jaitly, Andrew Senior, et al. 2012. “Deep Neural Networks for Acoustic Modeling in Speech Recognition.” IEEE Signal Processing Magazine 29 (6): 82–97. https://doi.org/10.1109/msp.2012.2205597.
- Ho, Jonathan, Ajay Jain, and Pieter Abbeel. 2020. “Denoising Diffusion Probabilistic Models.” In Advances in Neural Information Processing Systems, 33:6840–51.
- Hofer, Barbara K., and Paul R. Pintrich. 1997. “The Development of Epistemological Theories: Beliefs about Knowledge and Knowing and Their Relation to Learning.” Review of Educational Research 67 (1): 88–140. https://doi.org/10.3102/00346543067001088.
- Hoffmann, Jordan, Sebastian Borgeaud, Arthur Mensch, and others. 2022. “Training Compute-Optimal Large Language Models.” In Advances in Neural Information Processing Systems, 35:30016–30.
- Holland, John H. 1975. Adaptation in Natural and Artificial Systems. Ann Arbor, MI: University of Michigan Press.
- Huang, C.-Z. A., A. Vaswani, J. Uszkoreit, and others. 2018. “Music Transformer: Generating Music with Long-Term Structure.” 2018. https://arxiv.org/abs/1809.04281.
- Huang, Lei, Weijiang Yu, Weitao Ma, Weihong Zhong, Zhangyin Feng, Haotian Wang, Qianglong Chen, et al. 2025. “A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions.” ACM Transactions on Information Systems 43 (2): 1–55. https://doi.org/10.1145/3703155.
- Humboldt, Wilhelm von. 1810. “On the Internal and External Organization of the Higher Scientific Institutions in Berlin.” 1810. https://germanhistorydocs.org/en/the-holy-roman-empire-1648-1815/wilhelm-von-humboldt-s-treatise-quot-on-the-internal-and-external-organization-of-the-higher-scientific-institutions-in-berlin-quot-1810.pdf.
- Hutchins, Edwin. 1995. Cognition in the Wild. Cambridge, MA: MIT Press.
- Inhelder, Bärbel, and Jean Piaget. 1958. The Growth of Logical Thinking from Childhood to Adolescence. New York: Basic Books.
- Ji, Ziwei, Nayeon Lee, Rita Frieske, Tiezheng Yu, Dan Su, Yan Xu, Etsuko Ishii, Yejin Bang, Andrea Madotto, and Pascale Fung. 2023. “Survey of Hallucination in Natural Language Generation.” ACM Computing Surveys 55 (12): 1–38. https://doi.org/10.1145/3571730.
- Jo, YoungJu, SangYun Park, JaeHwang Jung, and others. 2017. “Holographic Deep Learning for Rapid Optical Screening of Anthrax Spores.” Science Advances 3 (8): e1700606. https://doi.org/10.1126/sciadv.1700606.
- Jumper, J., R. Evans, A. Pritzel, and others. 2021. “Highly Accurate Protein Structure Prediction with AlphaFold.” Nature 596: 583–89. https://doi.org/10.1038/s41586-021-03819-2.
- Kaplan, Jared, Sam McCandlish, Tom Henighan, and others. 2020. “Scaling Laws for Neural Language Models.” arXiv Preprint arXiv:2001.08361. https://doi.org/10.48550/arxiv.2001.08361.
- Kench, Steven, and Samuel J. Cooper. 2021. “Generating Three-Dimensional Structures from a Two-Dimensional Slice with Generative Adversarial Network-Based Dimensionality Expansion.” Nature Machine Intelligence 3: 299–305. https://doi.org/10.1038/s42256-021-00322-1.
- Kerr, Clark. 2001. The Uses of the University. 5th ed. Cambridge, MA: Harvard University Press.
- Kerrigan, Joshua, Paul La Plante, Saul Kohn, and others. 2019. “Optimizing Sparse RFI Prediction Using Deep Learning.” Monthly Notices of the Royal Astronomical Society 488 (2): 2605–15. https://doi.org/10.1093/mnras/stz1865.
- Kitchin, Rob. 2014. “Big Data, New Epistemologies and Paradigm Shifts.” Big Data & Society 1 (1). https://doi.org/10.1177/2053951714528481.
- Kliebard, Herbert M. 2004. The Struggle for the American Curriculum, 1893–1958. 3rd ed. New York: RoutledgeFalmer.
- Knowles, Malcolm S., Elwood F. Holton, and Richard A. Swanson. 2015. The Adult Learner: The Definitive Classic in Adult Education and Human Resource Development. 8th ed. London: Routledge.
- Koh, P. W., S. Sagawa, H. Marklund, S. M. Xie, M. Zhang, A. Balsubramani, W. Hu, et al. 2021. “WILDS: A Benchmark of in-the-Wild Distribution Shifts.” In Proceedings of the 38th International Conference on Machine Learning.
- Koren, Yehuda, Robert Bell, and Chris Volinsky. 2009. “Matrix Factorization Techniques for Recommender Systems.” Computer 42 (8): 30–37. https://doi.org/10.1109/mc.2009.263.
- Krajcik, Joseph S., and Phyllis C. Blumenfeld. 2006. “Project-Based Learning.” In The Cambridge Handbook of the Learning Sciences, edited by R. Keith Sawyer, 317–34. Cambridge: Cambridge University Press. https://doi.org/10.1017/CBO9780511816833.020.
- Krizhevsky, Alex, Ilya Sutskever, and Geoffrey E. Hinton. 2012. “ImageNet Classification with Deep Convolutional Neural Networks.” In Advances in Neural Information Processing Systems, 25:1097–1105.
- Kuhn, Deanna. 1999. “A Developmental Model of Critical Thinking.” Educational Researcher 28 (2): 16–46. https://doi.org/10.3102/0013189x028002016.
- Kurakin, Alexey, Ian J. Goodfellow, and Samy Bengio. 2017. “Adversarial Examples in the Physical World.” In ICLR Workshop. https://research.google/pubs/adversarial-examples-in-the-physical-world/.
- Lazer, D. M. J., M. A. Baum, Y. Benkler, A. J. Berinsky, K. M. Greenhill, F. Menczer, M. J. Metzger, et al. 2018. “The Science of Fake News.” Science 359 (6380): 1094–96.
- Lazer, David, Alex Pentland, Lada Adamic, and others. 2009. “Computational Social Science.” Science 323 (5915): 721–23. https://doi.org/10.1126/science.1167742.
- LeCun, Yann, Yoshua Bengio, and Geoffrey Hinton. 2015. “Deep Learning.” Nature 521: 436–44. https://doi.org/10.1038/nature14539.
- Lee, J. D., and K. A See. 2004. “Trust in Automation: Designing for Appropriate Reliance.” Human Factors 46 (1): 50–80.
- Lewis, Patrick, Ethan Perez, Aleksandra Piktus, and others. 2020. “Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks.” In Advances in Neural Information Processing Systems, 33:9459–74. https://proceedings.neurips.cc/paper/2020/hash/6b493230-Abstract.html.
- Lin, Stephanie, Jacob Hilton, and Owain Evans. 2022. “TruthfulQA: Measuring How Models Mimic Human Falsehoods.” In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 3214–52. Dublin, Ireland: Association for Computational Linguistics. https://doi.org/10.18653/v1/2022.acl-long.229.
- Long, Duri, and Brian Magerko. 2020. “What Is AI Literacy? Competencies and Design Considerations.” In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, 1–16. Association for Computing Machinery. https://doi.org/10.1145/3313831.3376727.
- Manovich, Lev. 2020. Cultural Analytics. Cambridge, MA: MIT Press.
- Matthias, Andreas. 2004. “The Responsibility Gap: Ascribing Responsibility for the Actions of Learning Automata.” Ethics and Information Technology 6 (3): 175–83. https://doi.org/10.1007/s10676-004-3422-1.
- McCarthy, John. 2007. “What Is Artificial Intelligence?” 2007. http://jmc.stanford.edu/artificial-intelligence/what-is-ai/index.html.
- McCarthy, John, Marvin L. Minsky, Nathaniel Rochester, and Claude E. Shannon. 1955. “A Proposal for the Dartmouth Summer Research Project on Artificial Intelligence.” Dartmouth College. 1955. http://jmc.stanford.edu/articles/dartmouth/dartmouth.pdf.
- McCulloch, Warren S., and Walter Pitts. 1943. “A Logical Calculus of the Ideas Immanent in Nervous Activity.” The Bulletin of Mathematical Biophysics 5: 115–33. https://doi.org/10.1007/bf02478259.
- Mellers, B., E. Stone, P. Atanasov, N. Rohrbaugh, S. E. Metz, L. Ungar, M. M. Bishop, M. Horowitz, E. Merkle, and P Tetlock. 2015. “The Psychology of Intelligence Analysis: Drivers of Prediction Accuracy in World Politics.” Journal of Experimental Psychology: Applied 21 (1): 1–14.
- Merriam, Sharan B., and Laura L. Bierema. 2013. Adult Learning: Linking Theory and Practice. San Francisco: Jossey-Bass.
- Miao, Fengchun, and Mutlu Cukurova. 2024. “AI Competency Framework for Teachers.” Paris: UNESCO. https://unesdoc.unesco.org/ark:/48223/pf0000391104.
- Miao, Fengchun, and Wayne Holmes. 2023. “Guidance for Generative AI in Education and Research.” Paris: UNESCO. https://unesdoc.unesco.org/ark:/48223/pf0000386693.
- Miao, Fengchun, Kelly Shiohira, and Natalie Lao. 2024. “AI Competency Framework for Students.” Paris: UNESCO. https://doi.org/10.54675/JKJB9835.
- Ministry of Education of the People’s Republic of China. 2022. Information Technology Curriculum Standards for Compulsory Education (2022 Edition). Beijing: Beijing Normal University Press.
- ———. 2024. “Ministry of Education Announces Measures to Strengthen AI Education in Primary and Secondary Schools.” December 2, 2024. https://www.moe.gov.cn/jyb_xwfb/gzdt_gzdt/s5987/202412/t20241202_1165500.html.
- Ministry of Education of the People’s Republic of China et al. 2026. “AI+education Action Plan.” April 10, 2026. https://www.moe.gov.cn/srcsite/A16/s3342/202604/t20260410_1433240.html.
- Minsky, Marvin, and Seymour Papert. 1969. Perceptrons: An Introduction to Computational Geometry. Cambridge, MA: MIT Press.
- Mishra, Punya, and Matthew J. Koehler. 2006. “Technological Pedagogical Content Knowledge: A Framework for Teacher Knowledge.” Teachers College Record 108 (6): 1017–54. https://doi.org/10.1111/j.1467-9620.2006.00684.x.
- Mitchell, Tom M. 1997. Machine Learning. New York: McGraw-Hill.
- Mnih, Volodymyr, Koray Kavukcuoglu, David Silver, and others. 2015. “Human-Level Control Through Deep Reinforcement Learning.” Nature 518: 529–33. https://doi.org/10.1038/nature14236.
- Morales-Navarro, Luis, Yasmin B. Kafai, Eric Yang, and Asep Suryana. 2025. “What Can Youth Learn about Artificial Intelligence and Machine Learning in One Hour? Examining How Hour of Code Activities Address the Five Big Ideas of AI.” Proceedings of the AAAI Conference on Artificial Intelligence 39 (28): 29195–202. https://doi.org/10.1609/aaai.v39i28.35193.
- National Institute of Standards and Technology. 2023. “Artificial Intelligence Risk Management Framework (AI RMF 1.0).” NIST AI 100-1. National Institute of Standards; Technology. https://doi.org/10.6028/NIST.AI.100-1.
- National People’s Congress, Standing Committee of the. 2021. “Personal Information Protection Law of the People’s Republic of China.” 2021. https://www.cac.gov.cn/2021-08/20/c_1631050028355286.htm.
- Nelson, T. O., and L Narens. 1990. “Metamemory: A Theoretical Framework and New Findings.” In The Psychology of Learning and Motivation, edited by G. H. Bower, 26:125–73. San Diego: Academic Press.
- Neumann, John von. 1945. “First Draft of a Report on the EDVAC.” Moore School of Electrical Engineering, University of Pennsylvania.
- New Generation Artificial Intelligence, National Governance Committee for. 2021. “Ethical Norms for New Generation Artificial Intelligence.” 2021. https://www.most.gov.cn/kjbgz/202109/t20210926_177063.html.
- Newell, Allen, and Herbert A. Simon. 1976. “Computer Science as Empirical Inquiry: Symbols and Search.” Communications of the ACM 19 (3): 113–26. https://doi.org/10.1145/360018.360022.
- Newman, John Henry. 1996. The Idea of a University. Edited by Frank M. Turner. New Haven: Yale University Press.
- Ng, Davy Tsz Kit, Jac Ka Lok Leung, Samuel Kai Wah Chu, and Maggie Shen Qiao. 2021. “Conceptualizing AI Literacy: An Exploratory Review.” Computers and Education: Artificial Intelligence 2: 100041. https://doi.org/10.1016/j.caeai.2021.100041.
- Nisbett, Richard E., David H. Krantz, Christopher Jepson, and Ziva Kunda. 1983. “The Use of Statistical Heuristics in Everyday Inductive Reasoning.” Psychological Review 90 (4): 339–63. https://doi.org/10.1037/0033-295x.90.4.339.
- Nussbaum, Martha C. 2010. Not for Profit: Why Democracy Needs the Humanities. Princeton: Princeton University Press.
- OECD. 2019. “Recommendation of the Council on Artificial Intelligence.” Paris: OECD. https://legalinstruments.oecd.org/en/instruments/OECD-LEGAL-0449.
- OECD, and European Commission. 2026. “Empowering Learners for the Age of AI: An AI Literacy Framework for Primary and Secondary Education.” Paris: OECD Publishing. https://doi.org/10.1787/65cd27d4-en.
- Oord, Aäron van den, Sander Dieleman, Heiga Zen, Karen Simonyan, Oriol Vinyals, Alex Graves, Nal Kalchbrenner, Andrew Senior, and Koray Kavukcuoglu. 2016. “WaveNet: A Generative Model for Raw Audio.” 2016. https://doi.org/10.48550/arxiv.1609.03499.
- Ott, P. A., Z. Hu, D. B. Keskin, and others. 2017. “An Immunogenic Personal Neoantigen Vaccine for Patients with Melanoma.” Nature 547: 217–21. https://doi.org/10.1038/nature22991.
- Page, Lawrence, Sergey Brin, Rajeev Motwani, and Terry Winograd. 1999. “The PageRank Citation Ranking: Bringing Order to the Web.” 1999-66. Stanford InfoLab.
- Papert, S. 1980. Mindstorms: Children, Computers, and Powerful Ideas. Basic Books.
- Parasuraman, Raja, and Victor Riley. 1997. “Humans and Automation: Use, Misuse, Disuse, Abuse.” Human Factors 39 (2): 230–53. https://doi.org/10.1518/001872097778543886.
- Pascarella, Ernest T., and Patrick T. Terenzini. 2005. How College Affects Students: A Third Decade of Research. Vol. 2. San Francisco: Jossey-Bass.
- Passi, Samir, and Solon Barocas. 2019. “Problem Formulation and Fairness.” In Proceedings of the Conference on Fairness, Accountability, and Transparency, 39–48. Association for Computing Machinery. https://doi.org/10.1145/3287560.3287567.
- Payne, B. H. 2019. “An Ethics of Artificial Intelligence Curriculum for Middle School Students.” MIT Media Lab. 2019. https://ec.europa.eu/futurium/en/system/files/ged/mit_ai_ethics_education_curriculum.pdf.
- Pearl, Judea. 1988. Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. San Mateo, CA: Morgan Kaufmann.
- Pennycook, G., and D. G Rand. 2019. “Lazy, Not Biased: Susceptibility to Partisan Fake News Is Better Explained by Lack of Reasoning Than by Motivated Reasoning.” Cognition 188: 39–50.
- Perdomo, Juan, Tijana Zrnic, Celestine Mendler-Dünner, and Moritz Hardt. 2020. “Performative Prediction.” In Proceedings of the 37th International Conference on Machine Learning, edited by Hal Daumé III and Aarti Singh, 119:7599–609. Proceedings of Machine Learning Research. PMLR. https://proceedings.mlr.press/v119/perdomo20a.html.
- Perkins, David N., and Gavriel Salomon. 1988. “Teaching for Transfer.” Educational Leadership 46 (1): 22–32.
- Pintrich, P. R. 2000. “The Role of Goal Orientation in Self-Regulated Learning.” In Handbook of Self-Regulation, edited by P. R. Pintrich M. Boekaerts and M. Zeidner, 451–502. San Diego: Academic Press.
- Pólya, George. 1957. How to Solve It: A New Aspect of Mathematical Method. 2nd ed. Princeton University Press.
- Pylyshyn, Zenon W. 1980. “Computation and Cognition: Issues in the Foundations of Cognitive Science.” Behavioral and Brain Sciences 3 (1): 111–32. https://doi.org/10.1017/S0140525X00002053.
- Rabiner, Lawrence R. 1989. “A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition.” Proceedings of the IEEE 77 (2): 257–86. https://doi.org/10.1109/5.18626.
- Radford, Alec, Jong Wook Kim, Chris Hallacy, and others. 2021. “Learning Transferable Visual Models from Natural Language Supervision.” In Proceedings of the 38th International Conference on Machine Learning, 139:8748–63.
- Reber, Rolf, Norbert Schwarz, and Piotr Winkielman. 2004. “Processing Fluency and Aesthetic Pleasure: Is Beauty in the Perceiver’s Processing Experience?” Personality and Social Psychology Review 8 (4): 364–82. https://doi.org/10.1207/s15327957pspr0804_3.
- Recht, Benjamin, Rebecca Roelofs, Ludwig Schmidt, and Vaishaal Shankar. 2019. “Do ImageNet Classifiers Generalize to ImageNet?” In Proceedings of the 36th International Conference on Machine Learning, 97:5389–5400. https://proceedings.mlr.press/v97/recht19a.html.
- Reichstein, Markus, Gustau Camps-Valls, Bjorn Stevens, Martin Jung, Joachim Denzler, Nuno Carvalhais, and Prabhat. 2019. “Deep Learning and Process Understanding for Data-Driven Earth System Science.” Nature 566: 195–204. https://doi.org/10.1038/s41586-019-0912-1.
- Risko, E. F., and S. J Gilbert. 2016. “Cognitive Offloading.” Trends in Cognitive Sciences 20 (9): 676–88. https://doi.org/10.1016/j.tics.2016.07.002.
- Rombach, Robin, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. 2022. “High-Resolution Image Synthesis with Latent Diffusion Models.” In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 10684–95. https://doi.org/10.1109/cvpr52688.2022.01042.
- Rosenblatt, Frank. 1958. “The Perceptron: A Probabilistic Model for Information Storage and Organization in the Brain.” Psychological Review 65 (6): 386–408. https://doi.org/10.1037/h0042519.
- Rosenblatt, L. M. 1978. The Reader, the Text, the Poem: The Transactional Theory of the Literary Work. Southern Illinois University Press.
- Rudin, Cynthia. 2019. “Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and Use Interpretable Models Instead.” Nature Machine Intelligence 1 (5): 206–15. https://doi.org/10.1038/s42256-019-0048-x.
- Rumelhart, David E., Geoffrey E. Hinton, and Ronald J. Williams. 1986. “Learning Representations by Back-Propagating Errors.” Nature 323: 533–36. https://doi.org/10.1038/323533a0.
- Russell, Stuart J., and Peter Norvig. 2021. Artificial Intelligence: A Modern Approach. 4th ed. Pearson.
- Sadler, D. R. 1989. “Formative Assessment and the Design of Instructional Systems.” Instructional Science 18 (2): 119–44. https://doi.org/10.1007/bf00117714.
- Saffran, Jenny R., Richard N. Aslin, and Elissa L. Newport. 1996. “Statistical Learning by 8-Month-Old Infants.” Science 274 (5294): 1926–28. https://doi.org/10.1126/science.274.5294.1926.
- Sahin, Ugur, Evelyna Derhovanessian, Mark Miller, and others. 2017. “Personalized RNA Mutanome Vaccines Mobilize Poly-Specific Therapeutic Immunity Against Cancer.” Nature 547: 222–26. https://doi.org/10.1038/nature23003.
- Samborska, Veronika, James L. Butler, Mark E. Walton, Timothy E. J. Behrens, and Thomas Akam. 2022. “Complementary Task Representations in Hippocampus and Prefrontal Cortex for Generalizing the Structure of Problems.” Nature Neuroscience 25: 1314–26. https://doi.org/10.1038/s41593-022-01149-8.
- Samuel, Arthur L. 1959. “Some Studies in Machine Learning Using the Game of Checkers.” IBM Journal of Research and Development 3 (3): 210–29. https://doi.org/10.1147/rd.33.0210.
- Sandoval, William A., Jeffrey A. Greene, and Ivar Bråten. 2016. “Understanding and Promoting Thinking about Knowledge: Origins, Issues, and Future Directions of Research on Epistemic Cognition.” Review of Research in Education 40 (1): 457–96. https://doi.org/10.3102/0091732x16669319.
- Schick, Timo, Jane Dwivedi-Yu, Roberto Dessì, and others. 2023. “Toolformer: Language Models Can Teach Themselves to Use Tools.” In Advances in Neural Information Processing Systems, 36:68539–51.
- Schoenfeld, A. H. 1985. Mathematical Problem Solving. Academic Press.
- ———. 1992. “Learning to Think Mathematically: Problem Solving, Metacognition, and Sense Making in Mathematics.” In Handbook of Research on Mathematics Teaching and Learning, edited by D. Grouws, 334–70. Macmillan.
- Schroff, Florian, Dmitry Kalenichenko, and James Philbin. 2015. “FaceNet: A Unified Embedding for Face Recognition and Clustering.” In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 815–23. https://doi.org/10.1109/cvpr.2015.7298682.
- Schwaller, P., T. Laino, T. Gaudin, P. Bolgar, C. A. Hunter, C. Bekas, and A. A Lee. 2019. “Molecular Transformer: A Model for Uncertainty-Calibrated Chemical Reaction Prediction.” ACS Central Science 5 (9): 1572–83. https://doi.org/10.1021/acscentsci.9b00576.
- Schwaller, P., D. Probst, A. C. Vaucher, and others. 2021. “Mapping the Space of Chemical Reactions Using Attention-Based Neural Networks.” Nature Machine Intelligence 3: 144–52. https://doi.org/10.1038/s42256-020-00284-w.
- Seixas, P., and T Morton. 2013. The Big Six Historical Thinking Concepts. Nelson Education.
- Selbst, Andrew D., Danah Boyd, Sorelle A. Friedler, Suresh Venkatasubramanian, and Janet Vertesi. 2019. “Fairness and Abstraction in Sociotechnical Systems.” In Proceedings of the Conference on Fairness, Accountability, and Transparency, 59–68. Association for Computing Machinery. https://doi.org/10.1145/3287560.3287598.
- Shannon, Claude E. 1938. “A Symbolic Analysis of Relay and Switching Circuits.” Transactions of the American Institute of Electrical Engineers 57 (12): 713–23. https://doi.org/10.1109/t-aiee.1938.5057767.
- Sharma, Mrinank, Meg Tong, Tomasz Korbak, David Duvenaud, Amanda Askell, Samuel R. Bowman, Newton Cheng, et al. 2023. “Towards Understanding Sycophancy in Language Models.” https://doi.org/10.48550/arXiv.2310.13548.
- Shen, Jonathan, Ruoming Pang, Ron J. Weiss, and others. 2018. “Natural TTS Synthesis by Conditioning WaveNet on Mel Spectrogram Predictions.” In Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing, 4779–83. https://doi.org/10.1109/icassp.2018.8461368.
- Shepard, Roger N. 1987. “Toward a Universal Law of Generalization for Psychological Science.” Science 237 (4820): 1317–23. https://doi.org/10.1126/science.3629243.
- Shokri, Reza, Marco Stronati, Congzheng Song, and Vitaly Shmatikov. 2017. “Membership Inference Attacks Against Machine Learning Models.” In 2017 IEEE Symposium on Security and Privacy (SP), 3–18. IEEE. https://doi.org/10.1109/SP.2017.41.
- Shulman, Lee S. 1986. “Those Who Understand: Knowledge Growth in Teaching.” Educational Researcher 15 (2): 4–14. https://doi.org/10.3102/0013189x015002004.
- Shute, V. J. 2008. “Focus on Formative Feedback.” Review of Educational Research 78 (1): 153–89. https://doi.org/10.3102/0034654307313795.
- Silver, David, Aja Huang, Chris J. Maddison, and others. 2016. “Mastering the Game of Go with Deep Neural Networks and Tree Search.” Nature 529: 484–89. https://doi.org/10.1038/nature16961.
- Silver, David, Thomas Hubert, Julian Schrittwieser, and others. 2018. “A General Reinforcement Learning Algorithm That Masters Chess, Shogi, and Go Through Self-Play.” Science 362 (6419): 1140–44. https://doi.org/10.1126/science.aar6404.
- Simon, H. A. 1971. “Designing Organizations for an Information-Rich World.” In Computers, Communications, and the Public Interest, edited by M. Greenberger, 37–72. Johns Hopkins Press.
- Skitka, Linda J., Kathleen L. Mosier, and Mark Burdick. 1999. “Does Automation Bias Decision-Making?” International Journal of Human-Computer Studies 51 (5): 991–1006. https://doi.org/10.1006/ijhc.1999.0252.
- Snyder, David, Daniel Garcia-Romero, Gregory Sell, Daniel Povey, and Sanjeev Khudanpur. 2018. “X-Vectors: Robust DNN Embeddings for Speaker Recognition.” In Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing, 5329–33. https://doi.org/10.1109/icassp.2018.8461375.
- Sparrow, B., J. Liu, and D. M Wegner. 2011. “Google Effects on Memory: Cognitive Consequences of Having Information at Our Fingertips.” Science 333 (6043): 776–78. https://doi.org/10.1126/science.1207745.
- Sperber, Dan, Fabrice Clément, Christophe Heintz, Olivier Mascaro, Hugo Mercier, Gloria Origgi, and Deirdre Wilson. 2010. “Epistemic Vigilance.” Mind & Language 25 (4): 359–93. https://doi.org/10.1111/j.1468-0017.2010.01394.x.
- Strathern, M. 1997. “Improving Ratings: Audit in the British University System.” European Review 5 (3): 305–21.
- Strubell, Emma, Ananya Ganesh, and Andrew McCallum. 2019. “Energy and Policy Considerations for Deep Learning in NLP.” In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, 3645–50. Florence, Italy: Association for Computational Linguistics. https://doi.org/10.18653/v1/P19-1355.
- Sutton, Richard S., and Andrew G. Barto. 2018. Reinforcement Learning: An Introduction. 2nd ed. Cambridge, MA: MIT Press.
- Taigman, Yaniv, Ming Yang, Marc’Aurelio Ranzato, and Lior Wolf. 2014. “DeepFace: Closing the Gap to Human-Level Performance in Face Verification.” In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 1701–8. https://doi.org/10.1109/cvpr.2014.220.
- Teaching Steering Committee for Basic Education, Ministry of Education. 2025a. “Guidelines for Artificial Intelligence General Education in Primary and Secondary Schools (2025 Edition).” Teaching Steering Committee for Basic Education, Ministry of Education. https://app.www.gov.cn/govdata/gov/202505/15/528972/article.html.
- ———. 2025b. “Guidelines for the Use of Generative Artificial Intelligence by Primary and Secondary School Students (2025 Edition).” Teaching Steering Committee for Basic Education, Ministry of Education. https://app.www.gov.cn/govdata/gov/202505/15/528972/article.html.
- Tenenbaum, J. B., C. Kemp, T. L. Griffiths, and N. D Goodman. 2011. “How to Grow a Mind: Statistics, Structure, and Abstraction.” Science 331 (6022): 1279–85.
- The College, University of Chicago. 2026. “History of the Core.” 2026. https://college.uchicago.edu/academics/core/history.
- Tolosana, Ruben, Ruben Vera-Rodriguez, Julian Fierrez, Aythami Morales, and Javier Ortega-Garcia. 2020. “Deepfakes and Beyond: A Survey of Face Manipulation and Fake Detection.” Information Fusion 64: 131–48. https://doi.org/10.1016/j.inffus.2020.06.014.
- Touretzky, David S., Christina Gardner-McCune, Fred Martin, and Deborah Seehorn. 2019. “Envisioning AI for k–12: What Should Every Child Know about AI?” Proceedings of the AAAI Conference on Artificial Intelligence 33 (1): 9795–99. https://doi.org/10.1609/aaai.v33i01.33019795.
- Trinh, T. H., Y. Wu, Q. V. Le, H. He, and T Luong. 2024. “Solving Olympiad Geometry Without Human Demonstrations.” Nature 625: 476–82. https://doi.org/10.1038/s41586-023-06747-5.
- Turing, Alan M. 1936. “On Computable Numbers, with an Application to the Entscheidungsproblem.” Proceedings of the London Mathematical Society, 2nd series, 42 (1): 230–65. https://doi.org/10.1112/plms/s2-42.1.230.
- ———. 1950. “Computing Machinery and Intelligence.” Mind 59 (236): 433–60. https://doi.org/10.1093/mind/lix.236.433.
- Turk, Matthew, and Alex Pentland. 1991. “Face Recognition Using Eigenfaces.” In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 586–91. https://doi.org/10.1109/cvpr.1991.139758.
- Tversky, Amos, and Daniel Kahneman. 1974. “Judgment Under Uncertainty: Heuristics and Biases.” Science 185 (4157): 1124–31. https://doi.org/10.1126/science.185.4157.1124.
- UNESCO. 2021. “Recommendation on the Ethics of Artificial Intelligence.” Paris: UNESCO. 2021. https://unesdoc.unesco.org/ark:/48223/pf0000380455.
- UNESCO Institute for Lifelong Learning. 2020. “Embracing a Culture of Lifelong Learning: Contribution to the Futures of Education Initiative.” Hamburg: UNESCO Institute for Lifelong Learning. https://unesdoc.unesco.org/ark:/48223/pf0000374112.
- UNICEF Innocenti. 2025. “Guidance on AI and Children.” Version 3.0. UNICEF. https://www.unicef.org/innocenti/reports/policy-guidance-ai-children.
- Vaswani, Ashish, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin. 2017. “Attention Is All You Need.” In Advances in Neural Information Processing Systems, 30:5998–6008.
- Veblen, Thorstein. 1918. The Higher Learning in America: A Memorandum on the Conduct of Universities by Business Men. New York: B. W. Huebsch.
- Vinyals, Oriol, Igor Babuschkin, Wojciech M. Czarnecki, and others. 2019. “Grandmaster Level in StarCraft II Using Multi-Agent Reinforcement Learning.” Nature 575: 350–54. https://doi.org/10.1038/s41586-019-1724-z.
- Vosoughi, S., D. Roy, and S Aral. 2018. “The Spread of True and False News Online.” Science 359 (6380): 1146–51. https://doi.org/10.1126/science.aap9559.
- Vygotsky, L. S. 1978. Mind in Society: The Development of Higher Psychological Processes. Harvard University Press.
- Wang, Pengjie, Kaile Zhang, Xinyu Wang, Shengwei Han, Yongge Liu, Jinpeng Wan, Haisu Guan, et al. 2024. “An Open Dataset for Oracle Bone Character Recognition and Decipherment.” Scientific Data 11: 976. https://doi.org/10.1038/s41597-024-03807-x.
- Wei, Jason, Xuezhi Wang, Dale Schuurmans, and others. 2022. “Chain-of-Thought Prompting Elicits Reasoning in Large Language Models.” In Advances in Neural Information Processing Systems, 35:24824–37. https://proceedings.neurips.cc/paper_files/paper/2022/hash/9d5609613524ecf4f15af0f7b31abca4-Abstract.html.
- Weizenbaum, Joseph. 1966. “ELIZA—a Computer Program for the Study of Natural Language Communication Between Man and Machine.” Communications of the ACM 9 (1): 36–45. https://doi.org/10.1145/365153.365168.
- Whitehead, Alfred North. 1929. The Aims of Education and Other Essays. New York: Macmillan.
- Wilson, Louis R. 1946. “General Education in a Free Society; Report of the Harvard Committee.” College & Research Libraries 7 (2): 186–87. https://doi.org/10.5860/crl_07_02_186.
- Wineburg, S. S. 1991. “Historical Problem Solving: A Study of the Cognitive Processes Used in the Evaluation of Documentary and Pictorial Evidence.” Journal of Educational Psychology 83 (1): 73–87.
- Wineburg, Sam, and Sarah McGrew. 2019. “Lateral Reading and the Nature of Expertise: Reading Less and Learning More When Evaluating Digital Information.” Teachers College Record 121 (11): 1–40. https://doi.org/10.1177/016146811912101102.
- Wing, J. M. 2006. “Computational Thinking.” Communications of the ACM 49 (3): 33–35. https://doi.org/10.1145/1118178.1118215.
- Wolpert, David H., and William G. Macready. 1997. “No Free Lunch Theorems for Optimization.” IEEE Transactions on Evolutionary Computation 1 (1): 67–82. https://doi.org/10.1109/4235.585893.
- Yao, S., J. Zhao, D. Yu, N. Du, I. Shafran, K. Narasimhan, and Y Cao. 2023. “ReAct: Synergizing Reasoning and Acting in Language Models.” In International Conference on Learning Representations. https://arxiv.org/abs/2210.03629.
- Zeng, Daniel Dajun. 2013. “From Computational Thinking to AI Thinking.” IEEE Intelligent Systems 28 (6): 2–4. https://doi.org/10.1109/mis.2013.141.
- Zhou, Xinyu, Jessica Van Brummelen, and Phoebe Lin. 2020. “Designing AI Learning Experiences for k–12: Emerging Works, Future Opportunities and a Design Framework.” 2020. https://arxiv.org/abs/2009.10228.
- Zimmerman, Barry J. 2002. “Becoming a Self-Regulated Learner: An Overview.” Theory Into Practice 41 (2): 64–70. https://doi.org/10.1207/s15430421tip4102_2.