Class 9 AI Unit 1: AI Reflection, Project Cycle and Ethics
30 theory hours · 25 practical hours · 10 theory marks
What this unit is about
The biggest Class 9 unit. It introduces AI and its three realms (data, computer vision, NLP), then the six-stage AI project cycle: problem scoping with the 4Ws canvas, data acquisition, data exploration, modelling (rule-based vs learning-based), evaluation (true/false positives and negatives) and deployment. It ends with AI ethics, bias and access.
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What the unit covers
- What AI is, where it shows up in daily life, and its three realms.
- The AI project cycle, including the 4Ws problem canvas and system maps.
- Data visualisation; rule-based vs learning-based models; evaluation terms; deployment (case study: preventable blindness).
- AI ethics, AI bias and AI access.
Key terms
- 4Ws problem canvas
- Who has the problem, what it is, where it happens, and why solving it matters.
- System map
- A diagram of the data features in a problem and how they affect each other.
- Rule-based model
- The developer writes the rules; the machine just follows them.
- Learning-based model
- The machine finds the rules itself from examples.
- AI bias
- Unfair results caused by data or design that leaves some groups out.
Exam-style questions
1. What are the 4Ws of problem scoping?
Show answer →
Who (stakeholders), What (the problem), Where (the context or situation), Why (the benefit of solving it).
2. Give one example each of a rule-based and a learning-based system.
Show answer →
Rule-based: a calculator or a fixed-reply chatbot. Learning-based: a spam filter trained on many labelled emails.
3. Give one way bias can enter an AI system.
Show answer →
If the training data mostly represents one group, the model works worse for groups it rarely saw.
Sources
Unit names, hours and marks checked against the official CBSE 2026-27 curriculum on 1 November 2026. Explanations and questions are written by CogniGuru.