Artificial intelligence curriculum for primary and secondary schools
AI in Informatics at the 1st level
Robots at the cat show
Learning from examples
Lessons 05
Learning from examples
Ju and Pi found themselves at a cat show and were all giddy! So many breeds, colors and sizes! How are they supposed to know a cat when each one is different? Because for robots to learn to recognize cats well, they have to see a lot of them. We'll see how good they are at that and who they end up taking home from the show.
How the lesson works
The lesson begins with a story in which the robots Ju and Pi go to a cat show and try to identify which animals are really cats. The children consider how humans recognise objects around them and how computers do this. The teacher leads a discussion about the fact that machines learn to recognize things based on examples that humans give them, and that the more examples they have, the more accurate their recognition becomes. In a hands-on activity, the children analyze different pictures and look for common characteristics of cats while learning the importance of having a diverse training set. They then discuss how computers can make mistakes in recognition, for example, not distinguishing between a chihuahua and a loaf of chocolate. The lesson continues with a demonstration of how artificial intelligence learns from examples, such as recognizing handwritten numbers or using applications like Quick, Draw! Finally, students reflect on their learning and look for other examples where AI object recognition could be applied, for example in autonomous vehicles or healthcare.
Information about lessons
Subsidies and years
45-90 minutes, grades 3-5 Elementary school
Aids
Educator: presentation on screening
Students: writing materials or printed worksheets
Building stones
Machine learning with a teacher (learning from examples)
What pupils learn
Computers can learn to recognise things based on examples set by humans.
Why is it taught
Understanding the principle of machine learning with a teacher is an important piece in the machine learning mosaic.
How do we know if they've learned
He will explain in his own words how computers learn by examples and what examples they need to do so.
Outputs of the RVP
Computer Science:
Information Systems: I-5-3-01 in the systems that surround it, recognizing the elements and relationships among them
Digital competences
Contribution and development - understands the importance of digital technologies for human society, learns about new technologies, critically evaluates their benefits and reflects on the risks of their use.
Bloom's taxonomy
Memorization: students recall and identify key characteristics of objects.
Comprehension: students compare and classify objects according to set criteria.
Analysis: students distinguish situations where recognition errors may occur and look for the causes of these errors.
Five Big Ideas
1-B-I perception vs. processing
1-C-I domain knowledge and their types
3-A-I people vs. machines
