Description
Students navigate an 8-scenario logic maze to classify real-world problems as Traditional Coding or Machine Learning. A fun, game-like capstone for any AI unit!
How do you know when to write explicit rules in code — and when to let a machine learn from data? That's the key question at the heart of this logic maze capstone activity.
Students work through 8 carefully crafted real-world technology scenarios, classifying each as best solved by Traditional Coding (TC) or Machine Learning (ML). The maze format adds a game layer: wrong-answer dead ends redirect students back with instant feedback, while correct paths unlock the next node. No dead end is arbitrary — each includes a targeted hint that reinforces the conceptual reasoning.
This is the capstone of the Science Stop Press AI Literacy series, designed to be used after Products 1–3, but it stands alone as a strong standalone lesson on computational thinking and AI problem-solving strategy.
What's Included
- 14-slide fully designed PowerPoint presentation
- 8 maze scenario nodes covering coding, ML, robotics, games, medical AI, creative AI, and more
- Dead-end redirect slides with targeted feedback hints for each wrong answer
- Answer key slide with reasoning for every classification decision
- Print-and-play maze worksheet version (PDF)
- Vocabulary review slide: training data, explicit rules, pattern recognition, edge cases
- Real-world extension: "Where do YOU see this?" reflection slide
- Standards alignment reference slide
- Speaker notes on all instructional slides
8 Maze Scenarios
- S1 — Thermostat scheduling app (TC)
- S2 — Spam email filter (ML)
- S3 — Traffic light controller (TC)
- S4 — Dog breed photo identifier (ML)
- S5 — Vending machine dispenser (TC)
- S6 — Movie recommendation engine (ML)
- S7 — Elevator floor selector (TC)
- S8 — Medical X-ray analyzer (ML)
Standards Alignment
- CSTA 2017 — 3A-AP-17: Decompose problems and create algorithms to solve computational problems
- CSTA 2017 — 3B-AP-08: Describe how artificial intelligence drives many software and physical systems
- CSTA 2017 — 3A-IC-24: Evaluate the ways computing impacts personal, ethical, social, and cultural practices
- ISTE 2016 — 1.5 Computational Thinker: Students formulate problem definitions suited for technology-assisted methods
- NGSS Science Practices 2 & 6: Developing models and constructing explanations
Grade Level:
Grades 5–8
Suggested Time:
45–60 minutes
Format:
PowerPoint (.pptx) + PDF
traditional coding vs machine learning, AI logic maze, machine learning activity grades 5-8, AI vs coding, computational thinking, AI literacy capstone, STEM logic activity, CSTA standards, technology awareness middle school, rules-based vs ML
Traditional Coding vs. Machine Learning | Logic Maze Activity | Grades 5-8 | AI
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Description
Students navigate an 8-scenario logic maze to classify real-world problems as Traditional Coding or Machine Learning. A fun, game-like capstone for any AI unit!
How do you know when to write explicit rules in code — and when to let a machine learn from data? That's the key question at the heart of this logic maze capstone activity.
Students work through 8 carefully crafted real-world technology scenarios, classifying each as best solved by Traditional Coding (TC) or Machine Learning (ML). The maze format adds a game layer: wrong-answer dead ends redirect students back with instant feedback, while correct paths unlock the next node. No dead end is arbitrary — each includes a targeted hint that reinforces the conceptual reasoning.
This is the capstone of the Science Stop Press AI Literacy series, designed to be used after Products 1–3, but it stands alone as a strong standalone lesson on computational thinking and AI problem-solving strategy.
What's Included
- 14-slide fully designed PowerPoint presentation
- 8 maze scenario nodes covering coding, ML, robotics, games, medical AI, creative AI, and more
- Dead-end redirect slides with targeted feedback hints for each wrong answer
- Answer key slide with reasoning for every classification decision
- Print-and-play maze worksheet version (PDF)
- Vocabulary review slide: training data, explicit rules, pattern recognition, edge cases
- Real-world extension: "Where do YOU see this?" reflection slide
- Standards alignment reference slide
- Speaker notes on all instructional slides
8 Maze Scenarios
- S1 — Thermostat scheduling app (TC)
- S2 — Spam email filter (ML)
- S3 — Traffic light controller (TC)
- S4 — Dog breed photo identifier (ML)
- S5 — Vending machine dispenser (TC)
- S6 — Movie recommendation engine (ML)
- S7 — Elevator floor selector (TC)
- S8 — Medical X-ray analyzer (ML)
Standards Alignment
- CSTA 2017 — 3A-AP-17: Decompose problems and create algorithms to solve computational problems
- CSTA 2017 — 3B-AP-08: Describe how artificial intelligence drives many software and physical systems
- CSTA 2017 — 3A-IC-24: Evaluate the ways computing impacts personal, ethical, social, and cultural practices
- ISTE 2016 — 1.5 Computational Thinker: Students formulate problem definitions suited for technology-assisted methods
- NGSS Science Practices 2 & 6: Developing models and constructing explanations
Grade Level:
Grades 5–8
Suggested Time:
45–60 minutes
Format:
PowerPoint (.pptx) + PDF
traditional coding vs machine learning, AI logic maze, machine learning activity grades 5-8, AI vs coding, computational thinking, AI literacy capstone, STEM logic activity, CSTA standards, technology awareness middle school, rules-based vs ML
