Case Study

AI Learning Assistant Suite speeds up planning and deepens classroom engagement

IYKYK Education

Industry

Education Technology (K-12 & Professional Learning)

Use Case

AI-Powered Lesson Planning, Feedback, and Ethics Curriculum Support

Tech Stack

Node.js

LangGraph

LlamaIndex

Vector Store

Mongo (RLS)

Models & Hosting

GPT-4o

Claude-3

Azure VNet managed by IYKYK

DevOps

faster lesson planning across participating teachers

rubric-aligned feedback accuracy in reflection scoring

more engagement time vs. traditional lessons

The Challenge

Educators using IYKYK's LMS and Ethics4Kids curriculum needed support in three key areas: Planning standards-aligned lessons quickly. Providing personalized, rubric-aligned student feedback. Engaging K-5 learners in abstract topics like digital citizenship. With limited prep time and strict data policies, IYKYK required a secure, real-time AI assistant embedded in their platform that could support teachers and students without compromising privacy.

The Solution

IYKYK Education launched an AI Learning Assistant Suite, a set of modular tools integrated directly into their LMS and Ethics4Kids Exchange programs.

Conversation UX

Context-aware commands like /plan, /feedback, and /admin, plus persistent sidebar chat. Supports both voice and text.

Orchestration

LangGraph routes intends to:

Parameterized SQL (gradebook, roster)

RAG (lesson docs, standards)

LLMs for generative support

Data Security

Mongo RLS ensures teacher-specific access; student data never leaves the VNet.

Continuous Evaluation

Optik + Comet dashboards track latency, hallucination, spend, and rubric compliance. Nightly regression tests trigger rollback if drift occurs.

Key Features

Plan

AI-assisted lesson planning with standard-aligned outcomes.

Feedback

Instant, rubric-based feedback for student reflections.

Admin

Roster-aware commands for grade lookups and progress tracking.

Live Avatar Mentors

LLM-powered HeyGen characters answer student questions in real-time.

Rubric-Guarded Generation

Every AI output is aligned with educational objectives.

LMS Integration

Launch modules, generate printables, and track usage within your existing system.

Implementation Timeline

7 weeks from Statement of Work to full deployment

Week 1

System design + data schema alignment (Mongo + standards)

Week 2-3

Intent router + LangGraph orchestration setup

Week 4

Voice/text chat UX layer + LMS plugin integration

Week 5

Rubric tuning + teacher feedback loop

Week 6

Ethics avatar training + IEP personalization hooks

Week 7

Pilot launch with Exchange cohorts

Week 8

Full district rollout & dashboard handoff

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AI Learning Assistant Suite speeds up planning and deepens classroom engagement

Let's Take Your Businessto the Next Level

Let's Take Your Business
to the Next Level