According to the Merriam-Webster, machine learning is “a computational method that is a subfield of artificial intelligence and that enables a computer to learn to perform tasks by analyzing a large dataset without being explicitly programmed.” Essentially, machine learning is the science that underlies AI applications like ChatGPT and Grammarly. In fact, Grammarly offers its own definition of machine learning, writing, “Machine learning (ML) is a subset of AI that enables computers to learn from data rather than through explicit programming. By recognizing patterns in data, ML allows computers to make predictions and improve over time.”
While teachers don’t need to become machine learning experts to use AI, it can be helpful to have a basic understanding of how it works. This understanding can help with making sense of the responses that we receive from AI applications. It demystifies the experience and can help us remember that, while these machines are getting better at providing answers, they are not really thinking on their own. Rather, they are following sophisticated algorithms in order to predict patterns based on source content.
Being aware of this general process is not only helpful for you as a teacher, but it can be extremely valuable to your students as well. After all, our students are growing up in a world saturated by machine learning and AI, and it’s important that we empower them with an understanding of this powerful technology. Some may pursue a career in technology, and those who don’t will certainly be living their daily lives interacting with it.
Below are three entry points into machine learning that you can use to both learn about it yourself and also introduce it to your students.
Teachable Machine (Google)
You and your students can use this browser-based tool to train a computer to recognize images, sounds, or poses. It’s free, easy to use, and doesn’t require any coding.
Users enter data to train Teachable Machine to recognize a type of image, sound, or pose. For images, you could train it to distinguish between a picture of a person or a dog. For audio, you might teach it to know the difference between stomping or clapping. For poses, it would access your webcam to recognize body positions and movements, like pointing your finger or tilting your head.
To use Teachable Machine, you click on one of the three available input choices, and then the website walks you through the training process. For example, if you click on “Audio Project,” you will be taken to a screen where you can either record or upload audio clips. It will begin by prompting you to record 20 seconds of background noise. Then, you will need to label and record at least eight samples of one class, or type, of sound. After that, you must label a second type of sound and record eight or more samples of that sound.
Once your samples are recorded, click on “Train Model.” After 10–15 seconds, it will finish, and you can then test it out on that same screen. An output meter on the bottom shows what type of sound it thinks is being captured, and it displays a level of confidence in that response by showing a percentage. Analyzing this data and determining how well the program was trained can lead to some great discussions with your students about machine learning and creating effective training inputs.
If you like what you’ve created, you can download or embed the project.
CreateAI (micro:bit)
CreateAI is another free browser-based tool. This one is intended to be used in conjunction with the micro:bit device, which you would need to purchase. The micro:bit device is a pocket-sized computer with built-in sensors, such as an accelerometer, compass, and microphone.
Students would typically wear the micro:bit on their wrist or ankle to track movements. Similar to Google’s Teachable Machine, students use the website to train the micro:bit to recognize various movements, such as waving and clapping.
Students record multiple samples of each movement to train the computer. Each of the trained movements is reflected by a different LED shape that displays on the micro:bit itself. This visual output offers immediate feedback to the students.
Students can also see the difference in movement inputs by studying an accelerometer graph on the website. Students will likely see how the recorded wave patterns begin to represent their specific movements. If the results don’t seem accurate, students can retrain their micro:bit.
Finished models can be edited in Microsoft MakeCode and exported to a micro:bit, so it can run without the computer. Even when untethered from the computer, the LED outputs continue to indicate which type of movement the device thinks you are making.
Students could use block coding in MakeCode to go deeper into their code or extend their learning by creating other machine learning projects.
Your Complete Guide to Machine Learning (Grammarly)
This third option is a resource rather than a tool.
While there are lots of materials online that you can use to learn about machine learning, this webpage from Grammarly is a great place to start if you simply want to learn more about machine learning and better understand what is involved. It’s also a great way to understand how editing tools like Grammarly work and how they are impacting your writing.
This page is broken down into categories of information: types of machine learning, common machine learning tasks, machine learning techniques, and machine learning concepts. The level of writing is more appropriate for a secondary-level student. If you are an elementary teacher, it may be best to gain your own understanding and then share those concepts with your students in age-appropriate language. If you are a secondary teacher, you might assign students to review this material before or after experiencing machine learning with either Teachable Machine or CreateAI.
Google’s Teachable Machine, micro:bit’s CreateAI, and Grammarly’s guide each offer unique, interactive, hands-on, and conceptual entry points into the world of machine learning. By introducing students to these tools, we can teach them how AI works, while also giving them the skills to think critically about the technology shaping their world.
AVID Connections
This resource connects with the following components of the AVID College and Career Readiness Framework:
- Instruction
- Rigorous Academic Preparedness
- Opportunity Knowledge
- Student Agency
- Break Down Barriers
- Advocate for Students
Extend Your Learning
- Teachable Machine (Google)
- CreateAI (micro:bit)
- Your Complete Guide to Machine Learning (Grammarly)
- MakeCode (Microsoft)