Students shift from human classification to machine learning as they build or test an AI classifier using tools like Teachable Machine. They experience firsthand how training data impacts accuracy and bias, observing successes and failures in rock identification. Through guided discussion, students analyze why certain misclassifications occur and explore ethical questions about data sourcing and representation. By the end, they synthesize insights about the strengths and limitations of AI in scientific contexts, reinforcing the need for human judgment and accountability in technology design.
Lesson Plan
Assessment Rubric
Sample Training Images
Rock Turing Test Table
Instructional Considerations
