AI is a part of our everyday lives, encompassing chatbots, smart recommendations, and image generators, but do our students truly understand how it works?

 This is Apirak Sang-ngenchai, and I am teaching 2nd year students in the "Fundamentals of AI" class. In our class, we focus on an active learning approach that helps students understand the fundamental concepts behind AI through engaging with the topic. Instead of just listening to lectures, students explore the world of AI through a variety of fun, interactive activities.

 We started with the basic questions: What is AI? What is machine learning? And how can a computer see and recognize an object? Rather than explaining everything through slides, we used activities that sparked curiosity and brought these topics to life.

Here are some of the tools and activities we used:

• Chat with Eliza: A simple AI chatbot that mimics a therapist. This helped students understand rule-based AI and how early AI systems communicated with users, unlike ChatGPT or Co-pilot, which provide a smarter and more diverse range of information nowadays. It also led to fun and thoughtful conversations about how AI can seem “smart” even when it doesn’t really understand us.
  Link: https://web.njit.edu/~ronkowit/eliza.html

• Google Quick, Draw: A game where students draw objects and the AI tries to guess what they’re drawing. This activity introduced the concept of unsupervised learning techniques and pattern recognition, showing how machines learn from massive amounts of human data.
  Link: https://quickdraw.withgoogle.com/
  
  
• Teachable Machine: An easy-to-use website where students can train their image or human body key points recognition models. Students quickly grasped the idea of training data, labels, and model accuracy, as well as the concept of supervised learning techniques, while having fun with creative experiments.
  Link: https://teachablemachine.withgoogle.com/
  
  
• Google Colab Coding: We also introduced students to basic Python coding on Google Colab. They ran simple machine learning scripts, changed parameters, and observed how the results changed. This provided them with a hands-on introduction to machine learning algorithms without requiring a complex setup.
  Link: https://colab.google/

 Through these activities, students didn’t just learn about AI; they also gained practical experience. They experienced how it works. They learned that machines require training, that data is important, and that results aren’t always perfect. Active learning also helped create a lively classroom atmosphere where students helped each other, shared discoveries, and reflected together. In the end, they weren’t just learning about AI. They were learning how to learn actively, a skill that will be valuable across all disciplines no matter what the future brings.

Apirak Sang-ngenchai