Computer Science Lesson Plan
Class: 5 - Lesson: 6.3 Training an AI Model in Data Collection

Purpose: To develop an AI model to classify farming objects, and to understand machine learning, the importance of accurate data, bias in AI systems, and their social impact.

No. of Classes

One class.

Material Required

Code.org, Amazon PartyRock.

Curriculum Content
  • 10.1) Data
    •
  • 10.1) Using AI Tools(4th)
    •
    Prior Knowledge

    Students should know:

  • The basics of a spreadsheet.
  • How to open a browser and use a web activity.

    • Exercise

    Exercise 1


    1. Activity for the code.org farmer activity page.

    • Click on the link to open the activity.
    • Help the AI bot identify whether an object in the farm is a crop or a weed.
    • Help the AI bot identify different weeds, including crops.
    • Observe how biased concepts can lead to biased or prejudiced data.
    • Learn what happens when incorrect data is given to an AI system, and how this affects future predictions.

    Exercise 2


    1. Activity for finding data for a spreadsheet.

    Solutions



    Teacher Instructions
    1. Begin the activity by introducing machine learning through simple, relatable examples such as identifying crops or weeds. Help students understand that AI systems do not "think" like humans but learn by recognising patterns from the data they are trained on.
    2. Clearly explain that an AI model's understanding is entirely dependent on the data it receives. Emphasise that AI does not truly understand objects or concepts - it only makes predictions based on patterns it has learned.
    3. Highlight the importance of accurate data labelling, explaining that incorrect or inconsistent labels can directly lead to incorrect predictions. Reinforce that the quality of input data strongly affects the quality of AI outputs.
    4. Introduce the concept of bias in data, and explain how biased or unbalanced datasets can result in biased AI behaviour. Connect this idea to real-world situations where AI systems may produce unfair or inaccurate outcomes.
    5. Encourage students to think critically about the social impact of AI, especially when systems are used to make decisions based on incomplete, incorrect, or biased data.
    6. POINTS TO PONDER: Can AI learn only if we explicitly train it with labelled data, or can it learn on its own? Click here
    7. POINTS TO PONDER: What is more important for AI learning - the number of examples, or the variety of examples? Click here
    8. How AI Works
      Click here Tamil / English to view the video for What is AI.