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AI Terminology
AI Terminology
AI (Artificial Intelligence): The broad field of computer science aimed at creating systems capable of performing tasks that typically require human intelligence.
Machine Learning (ML): A subset of AI where the computer "learns" from patterns in data rather than being told exactly what to do. Think of it like a student learning to recognize patterns in a math set through practice.
Neural Networks (NN): The "wiring" of modern AI. It’s a computer system inspired by the human brain, made of layers of "switches" (nodes) that process information and decide which parts are important.
Deep Learning (DL): A specific, advanced type of Machine Learning that uses many layers of Neural Networks to solve really complex problems, like recognizing faces or translating languages.
Foundation Model: The "Base" or "Engine." It is a massive AI model trained on a huge variety of data that can be adapted to many different tasks. Think of it like the chassis of a car—you can build a truck, a van, or a sedan on top of the same foundation.
Generative AI: A type of AI that can create new content (text, images, audio) rather than just analyzing existing data.
LLM (Large Language Model): A type of Foundation Model specifically focused on text. It’s trained on massive amounts of language to understand and generate human-like writing.
API vs. Non-API:
API (Enterprise): A "tunnel" that connects software to the AI engine. Usually more secure and does not use your data for training.
Non-API (Consumer): Using the website (like https://www.google.com/search?q=chatgpt.com). Your data might be used to train the model.
Major Models: The "Big 3" you’ll hear about: ChatGPT/GPT-4o (OpenAI), Claude (Anthropic), and Gemini (Google).
Prompt: The instructions or questions you type into an AI to get a response.
Chat / Chatbot: The interface where you "talk" to the AI (like the ChatGPT or Gemini website).
Gem / Agent / GPT: Custom versions of an AI created for a specific task (e.g., a "Lesson Plan Agent" that is pre-programmed with your rubric).
Slop: the Merriam-Webster 2025 word of the year, defined as “digital content of low quality that is produced usually in quantity by means of AI.”
Learn about AI
Learn about AI
Read
Read
Google has reported that energy efficiency per prompt has improved by 33x in the last year alone. The marginal energy used by a standard prompt from a modern LLM in 2025 is relatively established at this point, from both independent tests and official announcements. It is roughly 0.0003 kWh, the same energy use as 8-10 seconds of streaming Netflix or the equivalent of a Google search in 2008 (interestingly, image creation seems to use a similar amount of energy as a text prompt)1. This is the energy required to answer a standard prompt. It does not take into account the energy needed to train AI models, which is a one-time process that is very energy intensive. We do not know how much energy is used to create a modern model, but it was estimated that training GPT-4 took a little above 500,000 kWh, about 18 hours of a Boeing 737 in flight. How much water these models use per prompt is less clear but ranges from a few drops to a fifth of a shot glass (.25mL to 5mL+), depending on the definitions of water use (here is the low water argument and the high water argument).
Age Recommendations
No AI chatbots for kids under 5. Close supervision for kids age 6–12.
Co-Pilot
Microsoft Copilot age limits and parental controls.
“We have also expanded Copilot access to users between the age of 13 and 18, subject to minimum age requirements that vary by country or region. If your country or region is not listed, then the minimum age requirement to use Copilot is 13 years old.”
Gemini
Guide your child's Gemini Apps experience
Ages 13-18 require adult permission with a supervised account.
Notebook LM is enabled in ISD 196 for grades 8 and up
CSM AI Risk Assessment Gemini with Teen Protections - Google Docs
Listen / View
Listen / View
FREE - Register for Ditch Summit with Matt Miller - Open through January 11
Recommendations:
What Teachers Need to Know about AI in 2026 (and Beyond) with Matt Miller, Ken Shelton and Holly Clark (Released 12/22/25)
Collection
Reflect on AI
Reflect on AI
Analogy
Prompt: "Explain [Complex Topic] to a [Grade Level] student using an analogy about [Interest]."
EL Spark
"Translate this analogy into INSERT LANGUAGE."
Peril
Does the analogy oversimplify?
Human Fix
Verify accuracy. Ensure the core concept isn't lost in the metaphor
Write, Speak, Reflect
Write, Speak, Reflect
Debate
The 'Ethics' Debate: "Find a partner. One person argues for the 'Efficiency' of AI (saving time), and the other argues for the 'Integrity' risk (losing human voice). Discuss for 2 minutes: Where is the middle ground in your specific grade level?"
Play with Prompts
The 'Hallucination' Hunt: "Ask Gemini to write a biography of a famous person from your subject area, but ask it to 'include three subtle factual errors.' Try to find them without using Google. Then, reflect in writing: How might this 'Math over Fact' reality change how you assign research?"
Systems Thinking Reflection
The 'North Star' Audit: "Look at the District 196 Learning & Tech Framework. Write down one current lesson you teach that could be enhanced by AI and one lesson that should stay strictly human. Why is the distinction important for your students' future?"
Explore Ways to Use AI in Education
Explore Ways to Use AI in Education
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