Migration Guide: Move from Manual to AI Grading (4-Week Plan)

Primary keyword: adopt ai grading

Why a managed 4-week rollout?

AI can return criterion-by-criterion feedback quickly, but program-quality adoption means demonstrating validity, reliability, fairness, and transparency under the AERA–APA–NCME Standards—and keeping human oversight for decisions that affect students. AERA 

In parallel, institutions must handle student data under FERPA and evaluate vendor terms and data flows using PTAC guidance (Model Terms of Service; Requirements & Best Practices) before sending any records to third-party tools. Protecting Student Privacy 

Finally, treat AI grading as a risk-managed workflow: identify risks, put controls in place (e.g., HITL sampling, appeals, version logs), and monitor outcomes—an approach consistent with NIST’s AI Risk Management Framework. NIST Publications 

Who this guide is for

Course coordinators and program leads who want a low-friction rollout that shows its work to faculty, students, and reviewers. You’ll get week-by-week tasks, metrics, and templates to move from pilot to SOP.

Assets (use in Week 0)

• Project plan (CSV): tasks, owners, checkpoints, artifacts — download .
• Comms templates: student email and faculty email — student , faculty .

Week 0 (Prep): Pick a course & define goals

Decide scope and stakes. Start with one section or assignment family (e.g., essays or short-answers). Document intended use (formative vs. summative), and the construct you’re measuring; this is foundational for a Standards-aligned deployment. AERA 

Set target metrics.

• Reliability target: κ ≥ 0.60 (substantial) at the course level before scaling; higher for high-stakes. PMC 
• Operational targets: turnaround time (e.g., < 48h), human sampling rate (10–20%), appeal SLA (≤48h).

Data & privacy. Confirm you’re not training public models on student PII, align contracts with PTAC Model Terms, and update course privacy notices/FAQs as needed. Protecting Student Privacy 

Deliverables (Week 0): scope memo, owners (coordinator, QA lead, data steward), baseline samples (50–100 essays), existing rubric, privacy checklist sign-off.

Week 1: Rubric tuning + tiny pilot

Convert rubric → criteria JSON. Make each criterion observable with level descriptors and anchor exemplars. Add “caps” (e.g., “no thesis ⇒ cap on Organization/Evidence”). Clear rubrics raise reliability for both humans and AI. AERA 

Run a tiny pilot (30–50 essays).

• Two human raters score blindly; AI scores the same set.
• Freeze model, prompts, and parameters; log versions for audit. Don’t drift the config mid-pilot. Align with NIST AI RMF practice of documenting system changes. NIST Publications 

Inspect early: Look for systematic mismatches (e.g., thesis missing yet high AI score). Add auto-flags and confidence thresholds.

Deliverables (Week 1): rubric v2 (with anchors/caps), AI config v1 (model, temperature, prompts), pilot dataset with paired scores.

Week 2: Reliability (κ) + bias check + student comms

Compute agreement. Use Cohen’s κ and % agreement by criterion. Interpret κ with established bands (e.g., 0.61–0.80 = substantial, 0.81–1.00 = almost perfect) and look beyond the top-line: inspect confusions (borderlines). PMC 

Bias/fairness spot-check. If policy allows, compare score distributions and flag rates across known groups. Recent work shows that zero-shot AI scoring can be useful yet exhibits equity risks without careful design and monitoring—so check locally. Nature 

Student communications. Send the “heads-up” email (template included) clarifying:

• same rubric, human oversight remains, appeals path, data handling (no training of public models on their work). This aligns with PTAC guidance to disclose how tools collect and use data. Protecting Student Privacy 

Decision gate: If κ meets target and no material disparities are found (or mitigations documented), advance. Otherwise, iterate rubric/prompts and re-run a small check.

Deliverables (Week 2): reliability report, bias summary, student & faculty emails, published FAQ.

Week 3: Expand to full cohort with HITL

Scale up with controls.

• Turn on HITL sampling (10–20%) across the cohort; oversample low-confidence or policy-flagged cases.
• Keep a daily QA cadence (e.g., 20 spot checks/day) and a 48-hour appeal SLA.

Why HITL even if κ passed? Multi-course deployments have shown that LLM-based scoring often reaches fair to moderate agreement with trained raters, which is operationally helpful but still benefits from read-behind and appeals—especially when prompts change or populations vary. The Hechinger Report 

Governance. Log every config change (model version, temperature, prompt/rubric revisions) and maintain an audit trail—a key element in risk-managed AI deployments per NIST AI RMF. NIST Publications 

Deliverables (Week 3): cohort-wide activation, sampling policy, daily QA log, appeals queue.

Week 4: Review metrics & finalize SOP

Compare to Week 0 baseline:

• Reliability: κ, % agreement; review confusion matrices.
• Speed: average turnaround time before vs. after.
• Quality: appeal rate & outcomes.
• Fairness: subgroup deltas and any mitigations.

Publish the SOP. Document: intended use, rubric version, model/version, prompts, sampling, appeals, audit logging, and the plan to re-check reliability/fairness each term—matching the documentation expectations of the Testing Standards. AERA 

Deliverables (Week 4): SOP v1.0, archived configs, next-term audit calendar.

Measurement playbook (quick reference)

• Reliability: Use κ + % agreement per criterion; inspect disagreements (especially 1-level vs 2-level gaps). Target κ ≥ 0.60 at course-level before scale; set a higher bar for higher-stakes uses. PMC 
• Fairness: Compare distributions and flag rates; read behind outliers; document controls. Recent evaluations of zero-shot scoring stress fairness reviews alongside accuracy checks. Nature 
• Governance: Version prompts, model, and rubric; log changes; schedule periodic validity checks—consistent with the NIST AI RMF approach to risk management. NIST Publications 

Email templates & project plan

• CSV project plan: download  and copy into Notion/Sheets.
• Student email: download  and customize links (FAQ, privacy policy).
• Faculty email: download .

Internal resources (next reads)

• Reliability deep-dive: Cohen’s Kappa for AI Grading
• Bias & fairness: Bias Mitigation for AI Grading
• Prompting: Rubric-Driven AI Essay Grader Prompts

FAQ

Is AI grading compliant with FERPA? Yes—if you handle student records under FERPA and adopt contracts and disclosures aligned with PTAC guidance (e.g., Model Terms of Service) before sending data to third-party tools. Don’t let vendors train public models on identifiable student work; disclose processing in your notices. Protecting Student Privacy 

What κ should we aim for? As a rule of thumb, target κ ≥ 0.60 (substantial) at the course level before scaling; stricter thresholds are appropriate in higher-stakes contexts. Always pair κ with qualitative review of disagreements. PMC 

Do we still need humans in the loop if κ looks good? Yes. Studies in 2024–2025 show AI scoring can reach fair–moderate agreement with trained raters, but reliability can vary by prompt and population, so maintain sampling and appeals to manage risk and uphold student trust. The Hechinger Report 

What about fairness? Run subgroup checks and read behind outliers. Recent research on zero-shot AI scoring emphasizes fairness/equity reviews alongside accuracy and transparency. Document mitigations in your SOP. Nature 

What documentation will reviewers expect? The Testing Standards call for clarity on intended use, evidence for validity and reliability, fairness analyses, and transparent scoring procedures. Keep audit logs (prompts, model versions, parameters). AERA