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This video is licensed under Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0). You may share and adapt this work for non-commercial purposes only, with attribution. More information: https://creativecommons.org/licenses/... ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ What is Machine Learning—and what should educators know about it? In this AI Foundations video from Ed3, we explain machine learning in plain language, using a simple story (chickens + egg patterns) to show what ML really is: learning from data to make predictions. Machine learning powers many of the tools educators see every day—adaptive learning platforms, personalization systems, plagiarism detectors, and the algorithms underneath generative AI. But it also has limits: ML learns patterns, not truth, and it can only be as fair and accurate as the data it’s trained on. This video covers: What machine learning is (and why it’s not “magic”) How machines “learn” from examples instead of being explicitly programmed Why ML is at the core of generative AI (predicting the next word/pixel) Where ML shows up in education—and what can go wrong How training data gaps can disadvantage learners (dialects, identities, cultural contexts) Three practical ways teachers can use ML tools wisely A common misconception is that machine learning is objective. It isn’t. It reflects patterns in the training data, including blind spots, biases, and omissions. That’s why understanding ML helps educators use tools critically—without outsourcing professional judgment. This video is part of the AI Foundations series by Ed3, supporting educators worldwide in making informed, ethical, and human-centered decisions about AI in classrooms. 👉 Learn more about Ed3: https://www.ed3global.org 👉 Explore professional learning, courses, and events designed for educators navigating AI responsibly. 👉 Join our community of practice: https://community.ed3global.org Timestamps 00:11 A simple story: learning patterns (chickens and eggs) 00:39 What “machine learning” means 00:47 How ML powers generative AI 00:54 Learning from examples vs coding rules 01:11 Where ML shows up in education 01:21 The blind spots: bias + missing data 01:50 What this means for students and teachers 02:00 Three ways teachers can use ML wisely 02:44 Machine learning = statistics at scale 02:53 Separating hype from reality #MachineLearning #AIinEducation #AIFoundations #EdTech #DigitalEquity #CriticalThinking #EthicalAI #Ed3