The Personalization Layer transforms evaluation results into actionable strategies. The Action Plan APIs generate student targets, teacher reteach guides, and parent activity packs, ensuring that assessment data drives measurable improvement across the ecosystem.
Why Personalization Matters
- Problem: Evaluation without remediation leaves learning gaps unresolved.
- Solution: Action Plan APIs close the loop by converting scores + feedback into personalized action plans for every stakeholder.
- Ecosystem Fit: Plans flow downstream into AI Studio (auto-generate lesson plans, worksheets, and remedial content) and are refined upstream by ClassTrack observations.
Three APIs, One Purpose
Student Action Plan
Learning targets, personalized practice, and progress milestones.
Teacher Action Plan
Group insights, reteach strategies, and extension content.
Parent Action Plan
Weekly activity packs, simplified guides, and structured home support.
Action Plan
Ecosystem Integration
- Upstream: Inputs from Exam & Assignment Evaluation Suites (scores, feedback, annotated copies).
- Core: Action Plan APIs → generate tailored strategies for students, teachers, and parents.
- Downstream: Output feeds into AI Studio for content generation and ClassTrack for continuous improvement.
Next Steps
- Start with Student Action Plan.
- Or explore Teacher and Parent plans directly.
FAQ
How does the Personalization Layer turn evaluation outputs into Student, Teacher, and Parent Action Plans?
How does the Personalization Layer turn evaluation outputs into Student, Teacher, and Parent Action Plans?
CrazyGoldFish connects Evaluate → Personalize → Generate → Observe → Engage so criteria-level scores, strengths, and improvement areas flow directly into actionable plans. Student targets, teacher reteach strategies, and parent-friendly updates are generated from the same evidence, keeping outcomes CBSE/ICSE/GDPR aligned and auditable. A human-in-the-loop workflow ensures educators can review or adjust before publishing.
What inputs does the Personalization Layer consume and how are they delivered to our system?
What inputs does the Personalization Layer consume and how are they delivered to our system?
It consumes multimodal evaluation artifacts—text, handwriting, diagrams, audio, and video—scored via rubric and/or model-answer logic with strengths and improvement suggestions. Delivery is API-first (REST and webhooks) with structured JSON plus exportable PDFs/CSVs for LMS/ERP updates; most teams complete a 24h integration with no AI team required.
How are grouped misconceptions and reteach strategies computed and surfaced to teachers?
How are grouped misconceptions and reteach strategies computed and surfaced to teachers?
Evaluation outputs power Teacher Action Plans that cluster common errors and misconceptions for targeted reteach groups. These insights publish alongside criteria-level feedback and can be exported to your LMS/ERP, giving educators a practical, standards-aligned starting point for instruction.
How does the human-in-the-loop review influence personalization quality and governance?
How does the human-in-the-loop review influence personalization quality and governance?
Teachers remain in control—review, calibrate, or override evaluations before plans are published, with audit logs for every change. This preserves trust, keeps workflows CBSE/ICSE/GDPR aligned, and ensures personalized recommendations reflect institution-approved outcomes.
Can we embed the Personalization Layer in our LMS or app while keeping our brand and data flow intact?
Can we embed the Personalization Layer in our LMS or app while keeping our brand and data flow intact?
Yes. You can launch a white-label embeddable UI for ultra-fast rollout or stitch the APIs into existing flows to push results and plans to your LMS/ERP. Exports (PDF/CSV), role-based access, and branding controls let you keep your UX while adopting the Evaluate → Personalize pipeline in 24h—no AI team required.
How does the Personalization Layer handle multimodal evidence when generating plans and interventions?
How does the Personalization Layer handle multimodal evidence when generating plans and interventions?
Because the upstream Evaluation Layer is multimodal, the Personalization Layer uses consistent, rubric/model‑answer–aligned evidence from text, handwriting, diagrams, audio, and video. This keeps plans coherent across formats and aligned to board expectations with transparent artifacts for audits.
What compliance and audit features support Personalization deployments across regions?
What compliance and audit features support Personalization deployments across regions?
Workflows are CBSE/ICSE/GDPR aligned with privacy-by-design, audit logs, and export controls. Plans and reports remain traceable and inspection-ready, and data handling can be mapped to institutional policies (e.g., role-based access and retention) without bespoke ML operations.
How does AI Studio interact with the Personalization Layer to generate remedial content?
How does AI Studio interact with the Personalization Layer to generate remedial content?
Once plans are created, AI Studio generates standards-aligned lesson plans, worksheets, exams, and model answers tied to the identified gaps. Educators stay in the loop to accept or edit materials, closing the Evaluate → Personalize → Generate loop for fast, accountable remediation.
How do rechecks or appeals propagate into personalized recommendations and reports?
How do rechecks or appeals propagate into personalized recommendations and reports?
Students or staff can trigger rechecks that append audit logs and update outcomes after human review. The Personalization Layer then refreshes action plans and exports so downstream LMS/ERP reports and artifacts stay consistent and transparent.
What’s the fastest path to integrate and validate the Personalization Layer on our data?
What’s the fastest path to integrate and validate the Personalization Layer on our data?
Start with the embeddable, white-label UI and sandbox keys to stand up a pilot in 24h, then wire REST APIs and webhooks for deeper orchestration. As you promote to production, connect exports to your LMS/ERP and enable human-in-the-loop checkpoints to maintain up to 95% accuracy while scaling—no AI team required.