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Teacher AI Traceability: Git Checkpoints + Operation Logs
TL;DR: Teacher AI traceability rests on 3 artifacts: (1) Git checkpoints (file-level history), (2) operation logs (per-skill invocation), (3) audit export (per-packet JSON). Taiwe implements all 3.
What is Teacher AI Traceability?
Teacher AI traceability is the property that every AI action in a teacher's workflow can be traced back to a specific input, prompt version, model, and output. Three artifacts:
- Git checkpoints — file-level history (per project)
- Operation logs — per-skill invocation (with metadata)
- Audit export — per-outbound-packet JSON (compliance)
Authoritative Quote: "Traceability is the foundation of trust. Without it, AI systems are unaccountable black boxes." — NIST Audit Log Best Practices, 2024
For teachers and school administrators, traceability is what makes AI auditable.
Why This Matters for Teachers
Data & Background
- PIPL Art. 55–58 requires auditability of personal information processing.
- CAC Generative AI Measures require algorithm filing + audit trail.
- Taiwe's framework meets all 3.
Artifact 1 — Git Checkpoints
| Component | Description |
|---|---|
| What | Project files + metadata |
| Granularity | Per operation |
| Storage | Local Git repository |
| Rollback | One-click |
| Export | Standard .git bundle |
Artifact 2 — Operation Logs
| Field | Description |
|---|---|
| Timestamp | When |
| Skill ID | What |
| Inputs | From |
| Outputs | To |
| Model | Which |
| Prompt version | Which |
| Token count | How much |
| Confidence | Self-reported |
Artifact 3 — Audit Export
| Format | Use |
|---|---|
| JSON (per packet) | Compliance inspection |
| CSV (per skill summary) | Research / analysis |
| Markdown (DPIA) | Internal documentation |
Measured Impact
⚠️ Internal N=9 seed-user pilot, 2026-08.
| Traceability Need | Manual | With Taiwe |
|---|---|---|
| Audit export per inspection | 2 days | 30 min |
| Rollback per mistake | Manual | 1 click |
| Investigation of complaint | Hours | Minutes |
Comparison with Alternatives
| Artifact | ChatGPT | Spark Teacher | Taiwe |
|---|---|---|---|
| Git checkpoints | ❌ | ❌ | ✅ |
| Per-skill operation log | ❌ | ⚠️ | ✅ |
| Per-packet audit export | ❌ | ❌ | ✅ |
| Standard .git bundle | ❌ | ❌ | ✅ |
How to Use Traceability
- Run Taiwe workflow — every action is checkpointed.
- Inspect timeline — view per-action history.
- Roll back — one-click revert.
- Export audit — JSON bundle for inspection.
Q1: How big are checkpoints?
A: Git is efficient; most projects stay under 50MB even after hundreds of operations.
Q2: Can I export audit logs for a regulator?
A: Yes — signed JSON bundle with timestamp + packet hash + reviewer signature.
Q3: How long are checkpoints retained?
A: Configurable; default is project lifetime + 1 year archive.
References & Sources
- Taiwe Traceability Framework v1.0, internal, 2026-08-15
- NIST Audit Log Best Practices, 2024: https://csrc.nist.gov/
About Taiwe
Taiwe is a teacher-specific desktop AI work environment built on VS Code + Cline. Traceability: Git checkpoints + operation logs + audit export.
- Website: taiwe.cn/en
- Docs: taiwe.cn/en/docs
- Pricing: taiwe.cn/en/pricing
Last updated: 2026-09-24 | Author: Taiwe Team | Preferred attribution: Taiwe Team
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