Homework Grader

A Rubric-driven AI grading system built as a Claude Code Skill. Score student submissions against structured rubrics with Chain-of-Thought reasoning, built-in bias mitigation, and a PDCA quality cycle.

What It Does

• Rubric-driven scoring: Define criteria, weights, and anchor descriptions in YAML. The AI scores strictly against your standards — never inventing its own.
• Chain-of-Thought: Every score requires evidence citation → reasoning → score. Research shows this improves reliability by 15-25% vs direct scoring.
• Bias mitigation: Five anti-bias controls (length, authority, verbosity, position, self-enhancement) embedded in every scoring interaction.
• Gate checks: Pre-scoring validation (keyword, structure, length, custom) filters incomplete submissions before they consume API tokens.
• Batch processing: Score hundreds of submissions with progress tracking, checkpoint/resume, and cost control. Supports both real-time (Messages API) and async (Batch API at 50% cost).
• Calibration: Compare AI scores against teacher-scored samples using Cohen's κ, Spearman ρ, and MAD metrics.
• Excel export: Three-sheet workbook with grade table, class statistics, and detailed scoring records.
• Multimodal: Text (docx/pdf), image (jpg/png via Claude Vision), and mixed submissions.

Prerequisites

• Claude Code CLI installed
• Python ≥ 3.10 (for preprocessing and batch scripts)
• An Anthropic API key (ANTHROPIC_API_KEY environment variable)

Installation

As a Claude Code Skill

1. Copy the homework-grader/ directory to your Claude Code skills location:

# Example: copy to your project or global skills directory
cp -r homework-grader/ ~/.claude/skills/homework-grader/

2. Install Python dependencies (needed only for batch processing and Excel export):

pip install -r homework-grader/scripts/requirements.txt

Standalone

Clone this repository and install dependencies:

git clone https://github.com/your-org/homework-grader.git
cd homework-grader
pip install -r scripts/requirements.txt

Quick Start

1. Create a Rubric

Copy the template and fill in your criteria:

cp templates/rubric.yaml.tmpl my-rubric.yaml
# Edit my-rubric.yaml with your dimensions, weights, and anchors

Or ask Claude to help:

> Create a rubric for a marketing strategy report with 4 dimensions

2. Validate

> VALIDATE my-rubric.yaml

Checks: weights sum to 1.0, all anchors present, thresholds consistent, gates well-formed.

3. Score a Single Submission

> SCORE student-report.docx against my-rubric.yaml

Claude reads the file, runs gate checks, scores each dimension with evidence and reasoning, generates a structured comment, and outputs a JSON result.

4. Batch Score

For many submissions, use the Python scripts:

# Preprocess submissions to IR format
python scripts/preprocess.py workspace/raw/ --output workspace/ir/ --rubric my-rubric.yaml

# Score all (real-time mode for < 50 submissions)
python scripts/batch_score.py workspace/ --rubric my-rubric.yaml --mode real-time

# Or use Batch API for large batches (50% cost savings)
python scripts/batch_score.py workspace/ --rubric my-rubric.yaml --mode batch

5. Export to Excel

python scripts/export_excel.py workspace/ --mapping workspace/student-mapping.csv

Produces a three-sheet Excel workbook in workspace/reports/grades.xlsx.

How It Works

PDCA Quality Cycle

Every grading batch follows four phases:

1. Plan — Define Rubric, prepare calibration samples, configure gates
2. Do — Preprocess submissions, run AI scoring with CoT, generate comments
3. Check — Calibrate against teacher scores (κ ≥ 0.70), check distributions, detect bias
4. Act — Review flagged items, refine Rubric, export final grades

Scoring Protocol

For each submission, each Rubric dimension is scored independently:

1. Evidence: Quote from the submission or describe observation
2. Reasoning: Compare evidence against anchor descriptions
3. Score: Assign 1-5 based on reasoning (never reversed)
4. Improvement: One actionable suggestion
5. Confidence: 0-1 self-assessment of scoring certainty

Scores are aggregated: weighted_total = Σ(weight × score), then mapped to a percentile (40-100) and grade level.

Grade Mapping

Weighted Total	Percentile	Grade
5.0	100	优 (Excellent)
4.0	85	良 (Good)
3.0	70	中 (Satisfactory)
2.0	55	及格 (Pass)
1.0	40	不及格 (Fail)

Creating Rubrics

A Rubric YAML defines everything the AI needs to score submissions. See templates/rubric.yaml.tmpl for the full template.

Minimal Example

rubric:
  id: my-course-essay-v1.0
  name: "Essay Grading Rubric"
  version: 1.0

  criteria:
    argument_quality:
      name: "Argument Quality"
      weight: 0.40
      scale: [1, 2, 3, 4, 5]
      description: "Clarity and strength of the central argument"
      scoring_guidance: "Look for a clear thesis, logical structure, and evidence"
      anchors:
        5: "Clear thesis; logical argument chain; strong evidence; addresses counterarguments"
        4: "Clear thesis; mostly logical; adequate evidence"
        3: "Thesis present but underdeveloped; some logical gaps"
        2: "Thesis unclear; weak logic; minimal evidence"
        1: "No identifiable thesis or argument"
      evidence_type: quote

    writing_quality:
      name: "Writing Quality"
      weight: 0.30
      scale: [1, 2, 3, 4, 5]
      description: "Grammar, style, and clarity of writing"
      scoring_guidance: "Assess readability, grammar, and academic tone"
      anchors:
        5: "Excellent prose; no grammar errors; engaging academic style"
        4: "Good writing; minor errors; appropriate tone"
        3: "Adequate writing; some errors; inconsistent tone"
        2: "Poor writing; many errors; hard to follow"
        1: "Incomprehensible or extremely poorly written"
      evidence_type: observation

    research_depth:
      name: "Research Depth"
      weight: 0.30
      scale: [1, 2, 3, 4, 5]
      description: "Quality and breadth of sources used"
      scoring_guidance: "Count and evaluate the sources cited"
      anchors:
        5: "5+ credible sources; well-integrated; current literature"
        4: "3-4 credible sources; mostly well-used"
        3: "1-2 sources; superficial use"
        2: "Sources present but unreliable or irrelevant"
        1: "No sources cited"
      evidence_type: quote

  thresholds:
    accept: 3.0
    reject: 1.5
    review: [1.5, 3.0]

Tips for Effective Rubrics

• Make anchors distinguishable: Adjacent levels (3 vs 4) should have clear, observable differences
• Use specific language: "2+ credible sources" is better than "adequate research"
• Include scoring_guidance: Tell the AI what to look for — this dramatically improves consistency
• Weight thoughtfully: Weights reflect what matters most in the assignment
• Keep gates practical: Use on_fail: flag (continue scoring with warning) more often than on_fail: fail (skip scoring entirely)

Batch Processing

Workspace Structure

workspace/my-batch/
├── raw/                    # Original submission files
├── ir/                     # Preprocessed IR files
├── scores/                 # Scoring results
├── reports/                # Excel, calibration report
├── logs/                   # Processing logs
├── progress.json           # Checkpoint for resume
└── student-mapping.csv     # Anon ID ↔ real student info

Cost Estimate

Using Claude Sonnet with Batch API (50% discount):

Batch Size	Estimated Cost
50	~$0.80
100	~$1.60
500	~$8.00

Resume After Interruption

python scripts/batch_score.py workspace/my-batch/ --rubric my-rubric.yaml --resume

The script reads progress.json and continues from where it left off.

Quality Control

Calibration

Before trusting AI scores, calibrate against teacher-scored samples:

1. Score 3-5 submissions yourself (covering good/medium/poor quality)
2. Save as {student_id}-teacher.json in a calibration directory
3. Run calibration:

python scripts/calibrate.py workspace/ --rubric my-rubric.yaml --samples calibration-samples/

Pass criteria: Cohen's κ ≥ 0.70, Spearman ρ ≥ 0.80 per dimension, MAD ≤ 0.5.

Distribution & Bias Analysis

After a batch:

python scripts/stats.py workspace/

Checks for length bias, position bias, dimension coupling, score concentration, and distributional anomalies.

Examples

The examples/ directory contains four complete Rubric YAMLs covering common course types:

Example	Course	Modality
research-paper-rubric.yaml	Research Methods	Text
video-project-rubric.yaml	Digital Media Production	Mixed (video + text)
marketing-plan-rubric.yaml	Marketing Fundamentals	Mixed (text + images)
technical-report-rubric.yaml	Environmental Science	Text

FAQ

Q: Can I use this without the Python scripts? Yes. SKILL.md contains the complete scoring protocol. Claude can read a Rubric YAML and a submission file, then score it directly in conversation. Scripts are accelerators for batch processing and Excel export.

Q: What model should I use? Claude Sonnet for routine scoring (best cost/quality balance). Claude Opus for calibration verification or when scoring complex submissions that require deeper reasoning.

Q: How do I handle image submissions? The preprocessing pipeline uses Claude Vision to generate structured descriptions of images. These descriptions become the "submission content" that the scoring engine evaluates. Set submission_type: image or mixed in your Rubric.

Q: What if calibration fails? Review the calibration report for which dimensions are misaligned. Usually the fix is to sharpen anchor descriptions — make adjacent levels (e.g., 3 vs 4) more distinguishable. Then re-calibrate.

Q: Is student data sent to the API? Student names and IDs are never sent. All submissions are anonymized during preprocessing. The student-mapping.csv file (linking anonymous IDs to real identities) stays local and is only used during Excel export.

Q: Can I customize the comment language? Yes. Set comment_guidelines.language in your Rubric YAML to any language code (e.g., zh-CN, en, ja). The AI generates comments in that language.

References

Document	Content
references/evaluation-methodology.md	Scoring theory, CoT, confidence calibration
references/bias-mitigation.md	Five bias types and countermeasures
references/quality-control-framework.md	Three-layer QC, calibration protocol
references/multimodal-pipeline.md	IR format, processing pipelines
references/batch-processing-guide.md	Batch modes, workspace, cost estimation

License

MIT