pdf, 40.48 KB
pdf, 40.48 KB
pdf, 219.38 KB
pdf, 219.38 KB
pdf, 6.23 MB
pdf, 6.23 MB
pdf, 100.19 KB
pdf, 100.19 KB

Students train and test a machine learning model to classify images and understand how AI learns from examples.

This Year 6–8 artificial intelligence lesson introduces image classification and machine learning models. Students learn that AI systems can assign labels to images by learning from many examples rather than following fixed rules.

Students train their own machine learning model, test it with new images, and consider how accurate the model is. The lesson helps students understand training data, testing, classification, prediction, accuracy, and the limitations of machine learning.

This resource supports AI literacy, image classification, machine learning, data, model training, model testing, prediction, accuracy, and computational thinking.

Included file type: PDF.

Keywords: machine learning model, image classification, AI model, train and test AI, artificial intelligence, data, prediction, accuracy, ML4K, Year 6 AI, Year 7 AI, Year 8 AI.

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A bundle is a package of resources grouped together to teach a particular topic, or a series of lessons, in one place.

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How Artificial Intelligence works

This bundle includes a complete set of PDF resources for teaching students how Artificial Intelligence works, with a focus on face data, face detection, machine learning, image classification, and Scratch face sensing. Students explore how AI uses data to detect faces, respond to facial features, and support tools such as phone security, photo tagging, smart attendance systems, face filters, and interactive games. They also consider the limitations of AI by investigating privacy, fairness, accuracy, bias, false positives, false negatives, and the ways AI systems can make mistakes. Included in this bundle: 10 detailed lesson plans with starter and plenary activities 73 teaching slides Unplugged activities including sorting tasks, system design, class presentations, ethical discussions, and group problem-solving Digital activities involving AI tools, Machine Learning for Kids, Scratch face sensing blocks, and AI-inspired game controls Student worksheets and activity resources Opportunities for discussion, design, testing, evaluation, and reflection The unit introduces students to key AI and computing concepts, including: artificial intelligence in everyday life human vs AI decision-making face detection vs face recognition face data and privacy machine learning models training data and testing data image classification pattern recognition AI bias, fairness, and mistakes Scratch programming with face sensing variables, loops, clones, events, and game controls The resources were written to align with Grade 3–5 AI priorities for K–12 curriculum and are suitable for Year 6–8 students who are new to learning how AI works. They can be used as a full AI unit, a digital technologies/computing project, a Scratch programming extension, or a cross-curricular technology and ethics unit. This resource supports AI literacy, machine learning, classification, training data, testing data, data labels, image recognition, computational thinking, digital technologies, programming, and responsible technology use. Included file type: PDF Keywords: artificial intelligence, AI lesson, AI unit, machine learning, face detection, face recognition, face data, Scratch face sensing, Machine Learning for Kids, training data, testing data, data labels, classification, image sorting, pattern recognition, AI ethics, AI bias, digital technologies, computing, Year 6 AI, Year 7 AI, Year 8 AI.

£10.00

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