> ## Documentation Index
> Fetch the complete documentation index at: https://docs.crazygoldfish.com/llms.txt
> Use this file to discover all available pages before exploring further.
# Workflow
> How evaluation data is transformed into personalized student action plans with targets, practice, and milestones.
The Student Action Plan API powers the **Personalization Layer**. It turns **evaluation outputs** into structured **student targets, practice tasks, and milestones**, feeding downstream into AI Studio and refined by ClassTrack.
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## Step-by-Step Flow
* Takes outputs from **Exam Evaluation Suite** and **Assignment Evaluation Suite**.
* Inputs include scores, rubric alignment, misconceptions, and feedback JSON.
* Optional preferences (language, duration, difficulty).
* Map results to curriculum-aligned competencies (NEP 2020, NCERT).
* Identify knowledge gaps and select relevant practice tasks.
* Generate progress checkpoints and adaptive milestones.
* Structured **student plan** with targets, practice activities, and milestones.
* Flows into **AI Studio** to generate remedial worksheets and assignments.
* Refined over time using **ClassTrack** observation signals.
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## Ecosystem Integration
* **Upstream:** Evaluation Suites (Exam + Assignment).
* **Core:** Student Action Plan API creates learning pathways.
* **Downstream:** AI Studio generates tailored content; ClassTrack refines future plans.
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## Next Step
**Next →** See real-world [Use Cases](/products/student-action-plan/usecases) for Schools, Tutoring, LMS/ERP, and E-learning platforms.
## FAQ
Once student work is scored (targeting 95% accuracy) by the Evaluation layer, the results are passed into the Student Action Plan API. The API generates personalized targets, adaptive practice recommendations, and milestones, with human-in-the-loop controls for educator review and approval. Plans remain CBSE/ICSE/GDPR aligned for curriculum and privacy fit.
It consumes structured evaluation outputs (scores, rubric/model‑answer insights, feedback JSON) produced by CrazyGoldFish’s evaluation APIs. It returns a structured student plan with goals, recommended practice tasks, and milestones that can be rendered in a white‑label UI or pushed into your LMS/ERP via API.
Student Action Plans are generated directly from evaluation data and align with Teacher Action Plans (which group learners by misconceptions and suggest reteach strategies). Parent Action Plans convert those same results and student plan context into simplified activity packs and progress updates. This keeps all stakeholders coordinated on the same competencies and targets.
Yes. A human‑in‑the‑loop workflow lets educators review, edit, or approve plan elements and preserves audit trails. This maintains trust and policy compliance while retaining the system’s speed and accuracy at scale.
Yes—multimodal evaluation is handled upstream (text, handwriting, diagrams, audio, video) using rubric/model‑answer logic. The Student Action Plan API then personalizes goals and practice based on those evaluation results, ensuring plans reflect the full range of student evidence.
Use the REST APIs to pull plan JSON and push it into your existing LMS/ERP dashboards; white‑label modules are also available for embedding. Integration follows the same API‑first patterns used across CrazyGoldFish (evaluations, action plans, content) and supports role‑based access and exportable reports for governance.
Plans are generated with mappings to competencies and board standards, keeping outputs CBSE/ICSE aligned. The same standards tagging is used across Student/Teacher/Parent plans and AI Studio materials, ensuring consistency from assessment through remediation.
The Evaluate → Personalize → Generate → Observe loop continuously refreshes plans: new evaluation results regenerate Student Action Plans, and ClassTrack observations feed insights that refine strategies. This creates a closed loop where assessments, instruction, and observation improve each subsequent cycle.
Workflows are GDPR aligned with data minimization, role‑based access, and audit trails. Human‑in‑the‑loop checkpoints can be enabled for sensitive scenarios to maintain oversight and compliance while operating at scale.
Student plan targets feed AI Studio to generate curriculum‑aligned lesson plans, worksheets, and model answers tailored to the identified gaps. These materials can be edited by teachers and kept aligned with the same standards used in the plans, accelerating remediation.