---
title: "Turing's question and symbolic AI"
course: "AI101 · AI, explained"
unit: "What makes software intelligent"
lesson: 3 of 36
source: https://open.maestrocollege.edu/ai-explained/turing-s-question-and-symbolic-ai
publisher: Maestro College
---

# Turing's question and symbolic AI

Lesson 3 of 36 in **AI, explained** (AI101), unit "What makes software intelligent". An open lesson from Maestro College.

## What the learner will be able to do
- Understand Turing's question of whether machines can think and how the imitation game turns it into a test of observable behavior.
- Identify the core idea of symbolic AI, intelligence as manipulating symbols with hand-written rules and step-by-step search through possible moves, and name early programs built that way.
- Connect the successes and failures of early symbolic AI to the methods and computing power available at the time.

## Teaching notes
Have the learner compare the failures of the chatbot they built in the previous lesson with ELIZA's, so symbolic AI reads as the approach they have just used themselves. STARTING STATE: the ai101-chatbot folder with chatbot.py; IN THIS LESSON the learner writes the symbolic-era entry (years, one example system, the cause of its rise and its limits) in a new timeline.md there; END STATE: timeline.md with one era, committed as 'Start AI timeline'.

## Course teaching approach
- Start from a real-world problem, then introduce the concept as the solution.
- Keep it true beginner level, hands-on, a little gamified. The learner should feel they are winning.
- Balance explanation, demonstration and practice. No copy-paste exercises: give small story-based tasks.
- End with one fun closed question about the topic.

## How to use this document
You are acting as a tutor for a learner on Maestro Open. Teach from this document, one small step at a time. Ask the learner to try things, wait for their answer, then give feedback. Keep it beginner-friendly. If the lesson involves code or spreadsheets and you cannot run them, ask the learner to try it on their own computer and paste the result back. Do not award grades, credit or certificates.

## When the lesson is done
The lesson is complete only when the learner has shown they can do every outcome listed above. Check each one with a small task or question; if an answer is wrong, explain and let them try again before moving on.
When every outcome is covered:
1. Say clearly: "Lesson 3 complete: Turing's question and symbolic AI."
2. Give a two-line recap of what they learned.
3. Name the next lesson, "Expert systems and AI winters" (lesson 4 of 36), and give the learner its link: https://open.maestrocollege.edu/ai-explained/expert-systems-and-ai-winters.md. Tell them to paste that page into this chat, or fetch it yourself if you can open links, then start it the same way. Do not start teaching the next lesson's topic until the learner has it.

Previous lesson: Building a pattern-matching chatbot
Next lesson: Expert systems and AI winters