As the school year draws to a close, it is a great time to reflect on the happenings of the past year, celebrate our accomplishments, and plan for the future. The year wouldn’t be complete without a blog post dealing with the subject of Artificial Intelligence (AI). After all, AI has featured prominently in the media and discussions of the past couple years largely due to the proliferation of generative AI tools such as Chat GPT. 

A screenshot of the data in Quick Draw for a tree

Quick, Draw uses real drawings made by real people as data so that it can guess what you have drawn.

This year, many of us at LEARN have explored various AI tools. We have both presented on the topic and, on numerous occasions, have been participants in various workshops. Our focus has largely been on Machine Learning (ML).

Why Machine Learning?

Machine learning is more than just a buzzword; it’s a transformative technology that is revolutionizing industries across the globe. Machine learning is a subset of artificial intelligence (AI) that involves training computers to learn from data and make decisions or predictions without being explicitly programmed to perform those tasks. Simply put, it is teaching a computer to recognize patterns and make decisions based on those patterns. For instance, when you use a search engine, it learns from your search history to provide more relevant results. From healthcare to finance, from entertainment to transportation, ML is driving innovation and efficiency. 

How Does Machine Learning Work?

A screenshot showing the model created in ML Machine

With ML Machine, the accelerometer in the micro:bit is used to capture movement data. Then the machine is trained and the model is created. Finally, it is time to test the model.

Machine learning involves several key steps:

  1. Data Collection: Gathering large amounts of data relevant to the task at hand.
  2. Data Preparation: Cleaning and organizing the data to ensure it’s suitable for analysis.
  3. Model Training: Using algorithms to train the machine learning model on the prepared data.
  4. Model Testing: Evaluating the model’s accuracy and making adjustments as necessary.
  5. Deployment: Implementing the model so it can make predictions or decisions in real-world scenarios.

How to Explore Machine Learning?

Over the past year, we have experimented with a variety of tools that support the development of understanding of what machine learning is and how it works. Each of these platforms offers unique features that make machine learning accessible and engaging for students.

  • Teachable Machine allows students to create their own ML models through a simple, user-friendly interface.
  • Scratch provides an excellent introduction to programming concepts and ML extensions.
  • Quick Draw, an AI experiment by Google, helps students understand how neural networks recognize and learn from drawings, offering a fun and interactive way to explore ML.
  • ML Machine, on the other hand, incorporates the BBC micro:bit to challenge students to delve deeper into data analysis and model training.
  • Make: AI Robots combines the power of the BBC micro:bit with Teachable Machine to offer an engaging platform where students can learn to build and program their own AI-powered robots.

  Check out our Padlet of curated resources for more information about these tools.

Why Teach Machine Learning?

In today’s technology-driven world, digital competency is essential for students to succeed in both their academic and future professional lives. One of the most impactful ways to boost this competency is by introducing students to ML. Teaching ML not only equips students with cutting-edge skills but also encourages critical thinking, problem-solving, and a deeper understanding of how technology shapes our world. 

  • Hands-On Learning: ML projects offer practical, hands-on learning experiences. Whether it’s creating a simple image recognition system or developing a predictive model, these projects provide tangible results that can be incredibly rewarding and motivating for students.
  • Future-Ready Skills: ML is a rapidly growing field with applications across various industries. Familiarity with ML concepts prepares students for future careers in technology, data science, and beyond.
  • Critical Thinking: Learning how ML models work helps students develop critical thinking and analytical skills as they learn to interpret data and understand algorithms.
  • Problem-Solving: Machine learning projects require students to tackle complex problems, fostering resilience and innovative thinking. A screenshot of the final result of a Quick Draw activity
  • Digital Literacy: Understanding ML increases students’ overall digital literacy, making them more informed consumers and creators of technology.
  • Ethical Awareness: As we delve into machine learning, it’s essential to address the ethical considerations. Discussing issues such as data privacy, algorithmic bias, and the societal impact of AI helps develop a well-rounded perspective. This awareness is crucial for fostering responsible and ethical use of technology.