Nova the Chatbot says: My great-great-grandparents solved problems too—let’s explore how they shaped my mind!
Before today’s chatbots, voice assistants, and self-driving cars, scientists were already exploring how to make machines “think.” This module examines the earliest examples of artificial intelligence and how these systems approached problem solving using logic, rules, and algorithms. Students will discover how these pioneering programs laid the groundwork for modern AI—showing that even limited technology could perform reasoning, decision-making, and learning.

Imagine trying to teach a computer how to solve a riddle or play a game of checkers—with no internet, no graphics, and less memory than a calculator. That’s exactly what early AI researchers did! They created the first “thinking” machines using only logic and creativity.

What will we uncover?

Learning Questions:

1. What were the first examples of AI systems, and what problems were they designed to solve?
2. How did early AI systems simulate human thinking using logic, algorithms, and heuristics?
3. What limitations did early AI programs face when dealing with real-world problems?
4. How did early rule-based systems evolve into today’s data-driven AI models?
5. What lessons from early AI development can we apply to building responsible AI today?

Vocabulary Vault

Intro to AI and its History Module | Vocabulary
Word	Definition
AI Winter	A period when AI research slowed due to lack of funding and limited progress.
Algorithm	A sequence of step-by-step instructions a computer follows to solve a problem.
Artificial Intelligence (AI)	Machines designed to perform tasks that require human-like intelligence, reasoning, learning, and decision-making.
Bias in AI	When an AI system produces unfair or inaccurate results due to unbalanced or biased data.
Big Data	Extremely large sets of data used by AI systems to find patterns and make predictions.
Deep Learning	A type of machine learning that uses multiple layers of neural networks to analyze complex patterns in data.
ELIZA	An early chatbot that mimicked human conversation using pattern matching and keyword recognition.
General AI	A not-yet-existing type of AI that could think, learn, and perform many tasks at a human level.
General Problem Solver (GPS)	An early AI program designed to solve puzzles and problems by breaking them into smaller subgoals using logic.
Generative AI	AI that can create new content such as text, images, or music based on patterns it has learned.
Heuristic	A shortcut or rule of thumb that helps solve problems more quickly, though not always perfectly.
Logic Theorist	One of the first AI programs capable of proving mathematical theorems using symbolic reasoning.
Machine Learning (ML)	A branch of AI where computers learn patterns from data instead of following only explicit programmed instructions.
Narrow AI	AI designed to perform one specific task very well.
Neural Network	A computer system inspired by the human brain that recognizes patterns and learns from data.
Symbolic Reasoning	Solving problems using logic, symbols, and rules rather than large datasets.
Traditional Programming	A method where computers follow exact instructions written by humans.
Turing Test	A test created by Alan Turing to determine whether a computer can imitate human conversation convincingly.

Module Lessons

Lesson 1 | Defining AI and its Current State

Lesson 2 | The Evolution of AI

Lesson 3 | Early AI Examples and Problem-Solving

[CC BY-NC-SA 4.0] UNLESS OTHERWISE NOTED | IMAGES: LICENSED AND USED ACCORDING TO TERMS OF SUBSCRIPTION - INTENDED ONLY FOR USE WITHIN LESSON. Some images generated by Microsoft Copilot. (2026)