Problem
Instructional feedback workflows need consistency and speed, but public demos should not imply automatic grading or publish real student submissions, private teacher notes, course identifiers, or LMS records.
Case Study
Feedback, review, and remediation workflow
A public-safe teacher-controlled workflow that turns rubric evidence from synthetic Precalculus FRQ submissions into structured evaluations, reviewed feedback, and targeted remediation planning.
System Story
From rubric evidence to reviewed student-facing language
Solution
The workflow keeps the teacher in control: a rubric defines the criteria, synthetic observations provide evidence, the system drafts structured feedback, and only reviewed language becomes student-facing output.
Tools and Methods
The source repo uses dependency-light Python, JSON rubrics, synthetic submissions, deterministic workflow logic, Markdown reviewer packets, and explicit public-safety rules.
Result and Value
The current Precalculus FRQ demo produces three synthetic learner evaluations, two review-required cases, a teacher-facing reviewer packet, approved student feedback, and a remediation plan organized by rubric category.
Safety Boundary
The public artifact uses fake learners, synthetic rubric evidence, and generated examples only. Real submissions, grades, comments, Canvas identifiers, and teacher-only review artifacts remain outside the public boundary.
What I Learned
The strongest pattern is not automated scoring; it is a controlled artifact pipeline that separates evidence capture, draft generation, teacher review, release language, and remediation planning.