Summer Camp · Coming Summer 2026
AI Explorer · Eight Sessions of Curiosity and Creation
We are finalizing the first-year AI Explorer summer experience for middle school innovators. Each session blends storytelling, guided experiments, and reflection so learners build confidence with intelligent tools before diving into advanced pathways.
Session-by-session preview
The AI Explorer camp unfolds across eight 3-hour labs. Facilitators can adapt timing for day or residential formats, but each session keeps the same mix of mini-lessons, studio time, and reflection.
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01 What Is Intelligence?
Biological neurons vs. neural networks
Key concepts
- Differentiate natural and artificial intelligence
- Structure of biological neurons and perceptrons
- How signals and weights influence decision making
Hands-on build
- Build a paper neuron model that routes signals with weights
- Role-play a mini neural network to classify simple inputs
Reflection & share-out
- Write a quick comparison of how brains and machines learn
- Capture one question about intelligence for future exploration
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02 AI Around Us
Weak vs. strong AI and the Turing Test
Key concepts
- Spot everyday narrow AI systems and their goals
- Debate strong AI aspirations and limitations
- Understand the Turing Test and alternative evaluations
Hands-on build
- Run a “guess the bot” chat activity in pairs
- Map community touchpoints where AI already appears
Reflection & share-out
- Discuss where human judgment should stay in the loop
- Exit ticket: one benefit and one risk of pervasive AI
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03 Teachable Machine: Vision
Image classification project
Key concepts
- Training vs. testing examples for image models
- Recognizing overfitting through live demos
- Iterating on labels to improve clarity
Hands-on build
- Collect a quick image dataset with props and drawings
- Train and export a Teachable Machine image model
Reflection & share-out
- Analyze misclassifications and suggest improvements
- Document a short narrative of the project in a portfolio
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04 Teachable Machine: Sound & Pose
Audio cues and movement recognition
Key concepts
- How sensors capture audio and skeletal data
- Comparing model performance across modalities
- Designing signals that are inclusive and dependable
Hands-on build
- Train separate Teachable Machine audio and pose models
- Merge both models into a mini interactive experience
Reflection & share-out
- Evaluate which input type fit the challenge best and why
- Plan a refinement list based on tester feedback
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05 Data Quality Matters
Garbage in, garbage out
Key concepts
- Qualities of reliable datasets: balance, variance, relevance
- Detecting bias and gaps before training
- Ethical data sourcing and consent basics
Hands-on build
- Audit sample datasets for bias using a structured checklist
- Run quick experiments showing how corrupted data hurts models
Reflection & share-out
- Create a team data quality charter for future projects
- Journal prompt: how would poor data impact your community?
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06 Intro to Large Language Models
Prompts, limits, and hallucinations
Key concepts
- Token patterns and why LLMs sometimes hallucinate
- Prompt engineering basics: context, instructions, examples
- Responsible use guidelines for generative text tools
Hands-on build
- Compare outputs from varied prompts in a shared doc
- Design a fact-check protocol to catch hallucinations
Reflection & share-out
- Summarize a best-practice prompt formula in own words
- Capture a case where human review is essential
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07 Scratch Lab: Linear Regression
Visualizing predictions with blocks
Key concepts
- Representing datasets in Scratch lists
- Calculating trendlines to make predictions
- Interpreting slope and intercept in context
Hands-on build
- Code a Scratch project that fits a line to student-created data
- Showcase predictions and error visually on stage
Reflection & share-out
- Explain how changing data shifts the regression line
- Peer feedback on clarity of the Scratch project demo
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08 Scratch Studio & Ethics Forum
Extend builds and debate AI responsibility
Key concepts
- Integrating sensors, APIs, or datasets into Scratch experiments
- Ethical frameworks: fairness, accountability, transparency
- Project storytelling and audience engagement
Hands-on build
- Iterate on Scratch AI projects with new features or datasets
- Host an ethics circle using real-world case studies
Reflection & share-out
- Prepare showcase artifacts (demo video, poster, or write-up)
- Personal pledge on how to build responsible AI in the future