A teacher-friendly reference, with simple explanations you can share directly with students.
Core Concepts
- Artificial Intelligence (AI): Computers or machines that can perform tasks that normally require human intelligence, like recognizing speech, finding patterns, or making decisions.
- Machine Learning (ML): A type of AI where computers learn from data instead of being programmed with step-by-step instructions.
- Neural Network: A computer system inspired by the brain, used to recognize patterns in data (like photos, speech, or text).
- Algorithm: A step-by-step set of instructions that tells a computer how to solve a problem.
- Training Data: The examples (text, images, numbers, etc.) given to an AI system to help it learn.
- Model: The result of training an AI — it’s the system that can now make predictions or generate outputs.
AI in Action
- Chatbot: A computer program that can “chat” with humans, often powered by AI.
- Generative AI: AI that creates new content (like writing, art, or music) based on patterns it has learned.
- Natural Language Processing (NLP): A branch of AI that helps computers understand and respond to human language.
- Computer Vision: AI that helps machines “see” and understand images or video.
- Recommendation System: AI that suggests content (like movies, songs, or products) based on your past choices.
Classroom-Relevant Terms
- Bias: When an AI system treats some people unfairly because of problems in the data it was trained on.
- Hallucination: When an AI confidently makes up information that isn’t true.
- Misinformation: False or misleading information that can be spread by people or AI.
- Transparency: How much we can understand and explain what an AI system is doing.
- Ethics: Questions about what’s right or wrong when using AI, such as privacy, fairness, and responsibility.
- Digital Citizenship: Using technology safely, responsibly, and respectfully, including when interacting with AI.
Advanced (Teacher Reference)
- Large Language Model (LLM): A powerful AI trained on vast amounts of text to generate and understand language (e.g., ChatGPT, Gemini, Claude).
- Prompt: The input or question you give to an AI system.
- Token: A chunk of text (like a word or part of a word) that AI uses to process language.
- Training vs. Inference: Training is when the AI learns from data; inference is when it uses what it has learned to make predictions or generate answers.
- Supervised vs. Unsupervised Learning: In supervised learning, AI is trained with labeled examples (like “this is a cat”); in unsupervised learning, AI looks for patterns without labels.