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Case Type Case Report Status Completed

AAB-CASE-2025-LL-004

5-day mini AI summer camp (Grades 6–9; approx. ages 11–14) at an afterschool center in Diamond Bar, Southern California. The camp combined AI concept slides with simulation-based coding labs (CodeCombat AI HackStack) and hands-on browser-based model training (Teachable Machine), including discussion of bias, perspective, and limitations.

This page documents a real-world educational activity for registry purposes. It is descriptive (not a controlled study) and does not imply endorsement of any specific tool.
AgeMiddle school (11–14) SettingAfterschool center AI FunctionGenerative + Simulation PedagogyProject + exploratory Risk LevelLow Data SensitivityNone

Implementing Organization

1
Organization Type
Afterschool center
Location
Diamond Bar, Southern California, USA (suburban)
Primary Facilitator Role
Undergraduate and graduate CS students; technical educators

Learning Context

2
Setting Type
  • Informal learning
  • Afterschool center
  • In-school (K–12)
  • Private program
Session Format
Mini AI Summer Camp
Duration
5 days
Group Size
6 students
Devices
Individual device
Constraints
  • No individual logins allowed
  • No personal data collection
  • Time-limited setup and teardown
  • Variable Wi-Fi quality

Learner Profile (Non-identifiable)

3
Age Range
Grades 6–9 (approx. ages 11–14)
Prior AI Exposure (Assumed)
Limited or no prior formal AI education
Prior Coding Background (Assumed)
Some prior exposure to basic coding concepts assumed

Educational Intent

4
Primary Learning Goals
  • Develop conceptual understanding of artificial intelligence systems
  • Learn how AI models learn from data and generate outputs
  • Explore real-world applications of generative AI
  • Build computational thinking through simulation-based coding
Secondary Learning Goals
  • Understand bias and perspective in AI systems
  • Compare generative approaches (diffusion vs GAN, simplified)
  • Build confidence in presenting AI-powered applications
What This Was Not
  • Not a rigorous machine learning theory course
  • Not focused on mathematical foundations
  • Not designed for formal assessment or certification

AI Tool & Learning Materials Description

5
Tool & Platform Types
  • AI concept instructional slides
  • CodeCombat AI HackStack simulation labs
  • Teachable Machine (browser-based)
Learning Materials Included
  • AI slides: What is AI; How AI learns & creates; Generative tools and applications
  • HackStack labs: Pandemic simulation; Cupcake order form app; Weather app
  • Teachable Machine: thumbs-up / thumbs-down training for simple ML models
AI Role
  • Conceptual AI model
  • Generative system example
  • Simulation-based decision system
  • Tutor
  • Evaluator
Languages
English
Safeguards
  • No personal data collected
  • No student accounts created

Activity Design

6
Overall Structure
5-day mini AI summer camp combining conceptual instruction with simulation-based coding labs
Activity Flow
  1. Introduction to AI concepts and generative systems
  2. Guided exploration of CodeCombat AI HackStack labs
  3. Teachable Machine model training activities
  4. Discussion of bias, perspective, and limitations
  5. Creative project exploration and customization
  6. Informal project sharing and demos
Human vs AI Responsibilities
  • Human: defining goals, interpreting outputs, ethical reflection
  • AI: generating behaviors, simulating outcomes, responding to trained models
Scaffolding Strategies
  • Live demos and walkthroughs
  • Simplified analogies for GAN vs diffusion
  • Peer discussion and collaborative troubleshooting
  • Educator-guided reflection

Observed Challenges (Educator-Reported)

7
  • Abstract concepts required repeated analogies
  • Debugging logic in simulation labs
  • Balancing creativity with technical constraints

Design Adaptations Made

8
  • Used simplified visual metaphors for generative models
  • Allowed playful customization of projects
  • Reduced emphasis on correctness; increased exploration

Reported Outcomes (Descriptive, Not Measured)

9
Engagement
High engagement throughout; frequent laughter and spontaneous discussion
Learning Signals
  • Students explained how AI models learn from feedback
  • Students articulated bias and perspective concerns
  • Students compared different generative approaches at a conceptual level
  • Students built creative projects (e.g., a Peppa Pig–themed Google browser clone)
Educator Reflection
Students learned very well and were highly engaged. The classroom atmosphere was joyful, with frequent laughter, and students took pride in their creative AI-powered projects.

Ethical & Privacy Considerations

10
  • No personal data collected
  • No student names recorded
  • No online accounts created

Evidence Type

11
  • Practitioner observation
  • Activity documentation
  • Student project demonstrations
  • Learning analytics
  • Longitudinal assessment

Relevance to AI Education Research

12
Potential Research Use
Middle school AI literacy; Simulation-based AI learning; Bias and ethics education
Relevant Research Domains
Learning sciences; Educational technology; AI literacy; Ethics in AI

Case Status

13
  • Completed
  • Planned expansion
  • Scaling across sites

AAB Classification Tags

14
Age
Middle school
Setting
Afterschool center
AI Function
Generative AI, Simulation-based AI
Pedagogy
Project-based learning, Exploratory learning
Risk Level
Low
Data Sensitivity
None