Understanding Artificial Intelligence

AI: From Zero to Takeoff

Start with a game small enough to inspect completely. Then train a language model, test forecasts against evidence, and open classroom activities built for specific disciplines.

These lessons are by Bryant Harrison, Murray State University. Each interactive activity includes its original Guide, presenter notes and printable guide. Measurements, scripted examples and sourced claims retain their original labels and snapshot dates.

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What AI is

Two demonstrations make the machinery visible. The first compares three ways to choose a move on the same tic-tac-toe board. The second builds from a small language model to reasoning and tool use.

1. Types of AI
2. How a Language Model Works

Artificial intelligence

A broad label for machines performing tasks that call for judgment, prediction, planning, perception, or language.

Machine learning

A part of AI in which behavior improves from data or experience. Neural networks are one family of machine-learning models.

Generative AI

Systems that produce new text, images, audio, or other content. Modern examples often use neural networks, but “generative” describes what a system does.

Models and agents

A language model predicts sequences. Reasoning training can improve multi-step work; an agent adds tools, memory, and a loop around a model.

These labels overlap; they are not one universal ladder. Rules-based systems, learned tables, neural networks, generative systems, reasoning methods, and agents describe different mechanisms or behaviors. Ask what was learned, what was written by people, and what the system can actually do.

Where AI is going

One demo tests your intuition against measured progress. The other tests confident predictions against seventy years of history. Read together, they support urgency with skepticism.

1. The Pace of AI Progress
2. AI Winters: Boom and Bust

Use the dates, sources, measurement conditions and qualifications inside each activity when weighing its evidence.

How to use AI

Practice writing a clear request, then choose the right tool for the job. These two activities show how your instructions and your choice of tools shape what AI can help you do.

1. Prompting Strategies
2. AI Tool Guide

Three ideas to carry forward

1. Ask what kind of system you are looking at. Human-written rules, learned values, neural networks, language models, and agents behave differently.
2. Match the tool to the job and the review you can provide. Choose a workflow whose results you can inspect.
3. Bring your discipline’s standards. A useful classroom tool starts from the learning goal, exposes its assumptions, and makes evidence available for challenge.

Explore the classroom example projects. Each includes a guide to its model, assumptions, and teaching use. Start with a learning goal and inspect the evidence before adapting a project for your class.