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Technology Stack

Technology Stack (Current Snapshot)​

The RTS prototype combines modern web, database, and AI technologies to scaffold student inquiry, reflection, and narrative development.

Frontend​

  • Framework: React 18 + Next.js (Pages Router), TypeScript
  • Styling: Tailwind CSS
  • UI Components: shadcn/ui + Radix UI
  • Authentication UI: Supabase Auth UI + Cookie Sessions

Backend​

  • Database: Supabase (PostgreSQL) with Row Level Security (RLS)
  • Authentication: Supabase Auth (email/password + cookie-based session)
  • API Routes: Next.js API handlers (Pages Router)
  • Deployment: Vercel (production), GitHub Codespaces (development)

AI Integration​

  • Model: Google Gemini (latest available) via @google/genai SDK
  • Output Modes:
    • Structured JSON via responseMimeType: "application/json" + responseSchema for question generation
    • Pure markdown for synthesis generation
  • “Thinking” Tokens: thinkingConfig with includeThoughts: true for surfacing intermediate computational artifacts
  • Model Flexibility: Dynamic model_override parameter for testing different models without code changes
  • AI Observability: Complete metadata capture at every interaction — token counts (prompt, response, “thinking”, total), model string, “thought” summaries, timestamps
  • Prompt Architecture: System instructions externalized into separate .ts files, decoupled from handler logic

For details on how AI is constrained and positioned pedagogically, see A Note on AI Use. For how observability functions as pedagogy, see AI Observability.

Black Box Micro-Engagement (BBME) System (In Development)​

  • Content Management: Template-based BBME creation with course assignment
  • 4-Part Reflection Structure: Action step completion, personal reflection, relational reflection, source documentation
  • Instructor Tools: Creation, assignment, and management dashboard
  • Student Workflow: Course-scoped BBME access and submission
  • AI Synthesis: Gemini-powered reflection analysis with full metadata capture

Architectural Philosophy​

  • Pedagogy-Oriented Orchestration:

    • System design scaffolds thinking, not just output
    • Student reflections are structured artifacts persisted in the living ledger
    • Reflection trails build context across rounds — each round's AI output is grounded in the student's previous writing

  • Instructor-Student Ecosystem:

    • Walled garden course structure with RLS-enforced boundaries
    • Structured assignment workflows (Movement 1, Deep Dives, BBMEs)
    • Complete audit trail of student work and AI interactions

  • Reflections, not Answers:

    • Every API route is designed to be a compositional aid instead of a solution generator
    • System is designed for recursive, inquiry-driven use across movements
    • AI generates questions and reorganizes text — it does not introduce new content