What It Is
hwb2 Learning is an AI-augmented math tutoring platform I am actively building. It automates intake, first-pass feedback, and progress tracking so that human tutoring time is reserved for diagnosis, strategy, and explanation.
This is not “AI tutoring instead of a teacher.” The goal is to scale responsible, human-led instruction by removing repetitive overhead.
Problem → Insight → Solution
Problem
- One-to-one tutoring does not scale
- Administrative overhead consumes instructional time
- Feedback can be delayed and inconsistent
Insight
A large portion of tutoring effort is repetitive cognitive labor that does not require human judgment: intake triage, rubric-like feedback patterns, and progress summaries.
AI is best used before and after sessions—not during—so the live session stays human and high-trust.
Solution
- Standardized Google-based intake
- AI-generated first-pass feedback (structured, reviewable)
- Human-led tutoring focused on the highest leverage moments
Live Demo (v0)
Status: Limited scope, intentionally constrained.
Demo link: Coming soon (When ready, I will link a public v0 workflow here—likely an interest/intake form or a constrained “submit response → receive structured feedback” demo.)
What is real today
- Repeatable ingestion workflow (intake → structured processing)
- Feedback generation using a consistent template
- Foundations for progress tracking and follow-up
What is still manual / in progress
- Human review loop and refinement
- Classroom-scale workflows and dashboards
- Image/PDF ingestion
Architecture Snapshot
Current Stack
- Google Forms → Google Sheets for intake
- Python backend for processing and orchestration
- OpenAI Responses API for structured feedback generation
- Human review loop (manual at v0)
Planned Extensions
- Image and PDF ingestion (including handwritten work)
- Workflow automation (parent/student onboarding + follow-ups)
- Student progress dashboards and reporting
- Classroom-scale routines and data exports
Development Status
- v0 ingestion pipeline: in active development
- Building and iterating: weekly
- Initial private alpha: planned
- Public beta: TBD
Technical Details
Repository: Link coming soon (I will publish a clean repo once the v0 demo workflow is stable and appropriately scoped for public viewing.)
What the implementation emphasizes:
- Secure API usage (keys, environments, minimal exposure)
- Structured prompt design (repeatable, testable outputs)
- Small-scope v0 iteration before scaling features
Build Log
- Dec 2025 — Product framing + workflow planning
- Jan 2026 — v0 ingestion + structured feedback iteration
- Feb 2026 — Demo hardening + automation hooks (planned)
- Spring 2026 — Image/PDF ingestion exploration (planned)