The Evaluation Layer is the foundation of CrazyGoldFish’s AI reasoning stack. It automates the evaluation of handwritten, digital, audio, video, and diagram-based responses — ensuring fair, fast, and scalable assessment.

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Why Evaluation Matters

  • Problem: Manual grading is slow, inconsistent, and resource-intensive.
  • Solution: The Evaluation Layer uses AI to evaluate multi modal submissions, deliver transparent, auditable results, and feed downstream remediation.
  • Ecosystem Fit: It is the entry point of the loop — providing structured outputs that power the Personalization Layer (Action Plans) and Content Generation Layer (AI Studio).

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Core Capabilities

  • Multi modal Support → Handwritten answer sheets, typed responses, diagrams, audio/video (assignments).
  • Dual Evaluation Modes → Rubric-based + Model-answer grading (used separately or combined).
  • Auditable Workflows → Query handling, re-checks, annotated answer copies, compliance-ready exports.
  • Scalability → Handles large batches and long responses with consistent accuracy.
  • Dashboards → Track turnaround time (TAT), accuracy, and overall evaluation quality.

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Components

📝 Exam Evaluation Suite

Automates grading of subjective exams with rubric + model-answer evaluation.

📘 Assignment Evaluation Suite

Handles daily classroom work, multi modal assignments (text, audio, video), and instant feedback.

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Stakeholder Value

  • Students → Receive annotated, transparent feedback in days → hours.
  • Teachers → Save 7–10 hours/week on grading, focus more on teaching.
  • Leaders → Get compliance-ready dashboards with turnaround + accuracy insights.
  • Parents → Gain visibility into evaluation quality via annotated reports.

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Ecosystem Integration

  • Upstream: Exam/assignment submissions (handwritten, digital, audio, video).
  • Core: Automated, rubric/model-answer evaluation with feedback JSON + annotations.
  • Downstream: Outputs feed into Action Plan APIs (Student, Teacher, Parent), which then power AI Studio content generation.

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Next Steps