Aidena

— AI STACK RECOMMENDATION

AI Essay Grading & Feedback System

Automated essay evaluation with LLM-powered feedback, evaluation metrics, and scalable document processing for educational institutions.

Stays alive for 365 days after the last visit.

Education

AI Essay Grading & Feedback System

Automated essay evaluation with LLM-powered feedback, evaluation metrics, and scalable document processing for educational institutions.

high confidence

Core Stack ℹ︎

Claude Opus 4

Primary

Most capable model for nuanced essay analysis, detailed feedback generation, and complex reasoning about writing quality, grammar, and argumentation.

$0.015/1K input tokens

DeepEval

Primary

Built-in evaluation metrics for essay quality assessment including relevancy, coherence, and custom rubric scoring with Pytest integration for CI/CD testing.

$0/month (open-source)

AWS Textract

Primary

Extracts text from scanned essays and PDFs at scale, handling handwritten submissions and complex document layouts for bulk processing.

$1.5/1K pages

Complete the Stack ℹ︎

Dagster

Alternative

Orchestrates essay ingestion, processing, grading, and feedback generation pipelines with asset lineage and data quality monitoring for institutional workflows.

$0/month (open-source, self-hosted)

Arize Phoenix

Alternative

Monitors LLM grading consistency, detects bias in feedback, and provides tracing for debugging grading decisions across student submissions.

$0/month (open-source)

Chroma

Alternative

Stores essay embeddings and rubric examples for semantic similarity matching, enabling consistency checks and retrieval of similar essays for comparative grading.

$0/month (open-source)

Getting started

  1. 1Set up AWS Textract to batch-process submitted essays (PDF/scanned documents) into clean text.
  2. 2Use Claude Opus via API to generate detailed feedback against institutional rubrics, scoring dimensions (clarity, argumentation, grammar, originality).
  3. 3Integrate DeepEval metrics to validate grading consistency and detect outliers.
  4. 4Build Dagster DAG to orchestrate: document ingestion → text extraction → LLM grading → evaluation → feedback storage.
  5. 5Use Chroma to embed essays and rubric examples for similarity-based consistency checks across batches.
  6. 6Deploy Arize Phoenix to monitor grading patterns, detect potential bias, and log all LLM calls for audit trails.
  7. 7Create REST API endpoint to submit essays and retrieve grades + feedback in real-time.
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