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Participant Groups

Our research design includes six distinct participant categories, each representing a different stage of programming knowledge and AI tool familiarity. This stratification allows us to map the complete spectrum of programming education needs.

🌱 The Blank Slate Coders​

Profile: Complete Programming Beginners​

  • ✅ Zero programming experience
  • ✅ Never written a line of code
  • ✅ Basic computer literacy only
  • ✅ Fresh perspective on AI-assisted development

Research Target​

What absolute minimum knowledge enables AI collaboration?

This group helps us understand the theoretical floor of programming knowledge. Can someone with zero coding experience use AI tools effectively? What basic concepts must they grasp first?

Expected Insights​

  • Minimum conceptual understanding required for AI partnership
  • Most effective learning pathways from zero to functional
  • Critical knowledge gaps that prevent AI collaboration
  • Natural intuition vs. learned programming concepts

🔍 The Code Curious​

Profile: Programming Enthusiasts​

  • ✅ Watched tutorials and online courses
  • ✅ Tried coding 1-3 times but never finished projects
  • ✅ Understands basic concepts but lacks hands-on experience
  • ✅ Motivated to learn but intimidated by traditional coding

Research Target​

Does theoretical knowledge translate to AI-assisted success?

This group tests whether passive learning (videos, articles) provides sufficient foundation for AI-assisted development, or if hands-on experience remains essential.

Expected Insights​

  • Gap between theoretical understanding and practical application
  • How AI tools bridge knowledge-to-implementation barriers
  • Most common misconceptions from tutorial-based learning
  • Effectiveness of AI as a "practice partner"

🛠️ The Traditional Builders​

Profile: Experienced Developers​

  • ✅ 1-3 years of hands-on programming
  • ✅ Built 3+ complete applications from scratch
  • ✅ Strong traditional coding workflows
  • ✅ Minimal AI tool experience

Research Target​

Which traditional skills remain essential vs. become obsolete?

This group provides our control baseline - developers who learned programming the "traditional" way. We can identify which of their hard-earned skills remain valuable in the AI era.

Expected Insights​

  • Traditional skills that enhance AI collaboration
  • Workflows that become obsolete or counterproductive
  • Adaptation patterns from traditional to AI-assisted development
  • Skills that transfer vs. skills that must be unlearned

🤖 The AI Natives​

Profile: AI-First Developers​

  • ✅ 6 months - 1 year using AI coding tools
  • ✅ Built projects primarily with AI assistance
  • ✅ Comfortable with prompt engineering
  • ✅ Limited traditional "from scratch" experience

Research Target​

What happens when AI tools are the primary learning method?

This group represents the future of programming education - developers who learned primarily through AI assistance. What are their strengths and blind spots?

Expected Insights​

  • Unique capabilities of AI-native developers
  • Knowledge gaps from AI-dependent learning
  • Strengths in AI collaboration and prompt crafting
  • Weaknesses when AI tools are unavailable or insufficient

🎓 The Hybrid Learners​

Profile: AI-Traditional Bridge​

  • ✅ Traditional programming background (2+ years)
  • ✅ Actively using AI tools for 6+ months
  • ✅ Experience in both coding paradigms
  • ✅ Can compare effectiveness of both approaches

Research Target​

What's the optimal balance between traditional and AI skills?

This group provides our "ideal" comparison - developers who understand both paradigms and can articulate the benefits and limitations of each approach.

Expected Insights​

  • Optimal integration patterns for AI tools
  • Which traditional skills enhance AI collaboration
  • Most effective transition strategies
  • Best practices for hybrid development workflows

🏢 The Industry Veterans​

Profile: Senior Developers & Team Leads​

  • ✅ 5+ years professional development experience
  • ✅ Currently working at tech companies
  • ✅ Team leadership and mentoring experience
  • ✅ Perspective on industry hiring and skill requirements

Research Target​

What skills do companies actually need in the AI era?

This group provides real-world industry context - what skills are actually valued in professional development environments?

Expected Insights​

  • Industry skill requirements vs. educational priorities
  • Professional development workflow adaptations
  • Team leadership in AI-integrated environments
  • Long-term career implications of AI-assisted development

📊 Participant Distribution Strategy​

Balanced Representation​

  • Equal team sizes within each group for AI vs. traditional approaches
  • Diverse backgrounds within groups (different industries, educational paths)
  • Mixed experience levels to capture within-group variations

Target Numbers​

  • 24-30 participants per group (12-15 AI-assisted, 12-15 traditional)
  • Total: 144-180 participants
  • 6 company challenges with mixed teams

Selection Criteria​

  • Self-assessment accuracy verified through practical evaluation
  • Commitment to full 4-day participation
  • Diverse demographic representation
  • Geographic distribution (when possible)

This participant structure ensures comprehensive data collection across the complete spectrum of programming knowledge and AI readiness, providing robust insights for educational institutions worldwide.