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A short class with Sarah and her students. The other ways are under More.
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Today, we're going to explore how to create an AI model. Who can explain what an AI model is?
An AI model is like a computer program that learns from data to make predictions.
Exactly! We can use tools like Teachable Machine. What types of data can we train models with?
Images, sounds, and text!
Great job! Let’s remember 'IST' — Images, Sounds, Text. Now, how do we start creating a model?
We need to collect sample data first, right?
Yes! Collecting sample data is the first step. Can anyone suggest an example project we could do?
We could create an image classifier to recognize happy and sad faces!
That's a wonderful idea! Remember to train and test your model after collecting data. Let’s recap: we’ve learnt what an AI model is, types of data to use, the acronym 'IST', and an example project.
Now, let's discuss how we can identify problems related to the Sustainable Development Goals. What are some issues you think we could address?
Maybe pollution in our area could be one?
Absolutely! Pollution is a major global concern. How could we detail this problem using the 4Ws canvas?
For 'Who', we could say local residents are affected. 'What' is air pollution. 'Where' is the industrial area. And 'Why' is it a concern because it affects health.
Perfect! Now, if we want to explore features relating to air pollution, what data points could we use?
We could look at vehicle counts and air quality index levels!
Great suggestions! Lastly, how do we visualize the data we collect?
We can use graphs like bar or line charts to display our findings.
Exactly! Remember our project structure: identify a problem, use 4Ws for deep analysis, identify features, and visualize data. That’s a lot of knowledge packed into one discussion!
Now that we’ve identified problems, how do we create AI-enabled solutions? Any suggestions?
What about a mobile app that alerts us about pollution levels?
Excellent idea! How might this app work?
It could use sensors to measure air quality and notify users when pollution levels are too high.
Exactly! And what about another solution related to water? Any thoughts?
We could develop a smart watering system that only waters plants based on the soil moisture level!
Fantastic! We connect technology with sustainability this way. Remember, our focus is creativity along with practicality. Let's summarize: we discussed possible AI-enabled solutions such as a pollution alert app and smart watering systems!
Engaging students through practical projects in AI.
Building models using user-friendly tools to understand AI processes.
Aligning projects with global goals to address real-world problems.
Collecting and visualizing data effectively.
To learn with AI's might, we train it right, data and tools in sight, solutions take flight!
Once upon a time, in a town clouded with pollution, kids created an AI model that saved the day by alerting everyone about dirty air. Unity and technology brought hope to their community!
Remember 'IPDS' — Identify the problem, Plan the solution, Develop the model, Share the result.
IST: Images, Sounds, Text — the data we can use to build our AI models!
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AI Model
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Sustainable Development Goals
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4Ws Canvas
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Data Visualization
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A program that learns from data to make predictions or decisions.
Global goals aimed at addressing various social, environmental, and economic challenges.
The graphical representation of information and data.
A tool used to analyze problems by answering Who, What, Where, and Why.
This section focuses on engaging students in hands-on AI projects that promote creativity and problem-solving skills.
Students learn to apply AI concepts through two major projects; creating an AI model and solving real-world problems aligned with Sustainable Development Goals. By utilizing user-friendly tools, they develop critical thinking, teamwork, and data handling skills in the context of AI and sustainability.
In this section, we introduce a series of suggested projects designed for ninth-grade students to apply their knowledge of Artificial Intelligence (AI) through hands-on experiences. Emphasizing the importance of learning by doing, students are encouraged to select projects that stimulate their creativity and address real-world challenges. The projects are organized into two main focal points: creating an AI model using accessible tools such as Teachable Machine and Machine Learning for Kids, and identifying and developing AI solutions for problems related to the Sustainable Development Goals (SDGs). Through these activities, students not only learn the basics of AI, including datasets and the training and testing processes but also engage in data visualization and system mapping to better understand and address global issues like pollution and resource consumption. The chapter concludes by highlighting the benefits of such projects, including practical understanding of AI, enhanced data literacy, teamwork development, and an increased awareness of global challenges.