Intelligent student & institution insights powered by Azure AI. Crawl, index, and reason over LMS activity, curriculum documents, policies, research, and performance analytics to deliver personalized AI applications for higher education.
Higher education institutions sit on a goldmine of untapped data - scattered across LMS platforms, student information systems, research repositories, policy documents, and financial systems.
The Education Platform uses Azure AI crawlers (aligned to the Work IQ / Foundry IQ / Fabric IQ paradigm) to ingest, index, and reason over this data, producing actionable intelligence for students, advisors, and administrators.
The platform is powered by three complementary intelligence engines, each crawling and indexing a distinct data domain. Together they form a 360° knowledge fabric over the institution.
Crawls and indexes behavioral and interaction data from Learning Management Systems, collaboration platforms, and student engagement tools.
Crawls and indexes institutional knowledge - academic catalogs, policy handbooks, research publications, and accreditation documents.
Crawls and indexes quantitative performance data - grades, retention metrics, graduation rates, and institutional KPIs from the SIS and data warehouse.
graph LR
subgraph Sources["Data Sources"]
LMS["LMS\nCanvas / Blackboard"]
SIS["Student Info System"]
DOCS["Catalogs &\nPolicy Docs"]
RES["Research Repos"]
DW["Data Warehouse"]
end
subgraph Crawlers["AI Crawler Layer"]
WC["Work IQ\nCrawler"]
FC["Foundry IQ\nCrawler"]
FBC["Fabric IQ\nCrawler"]
end
subgraph Index["Azure AI Search\nVector Index"]
WI["Behavioral\nEmbeddings"]
FI["Institutional\nKnowledge Base"]
FBI["Performance\nAnalytics Store"]
end
LMS --> WC
SIS --> WC
SIS --> FBC
DOCS --> FC
RES --> FC
DW --> FBC
WC --> WI
FC --> FI
FBC --> FBI
WI --> APPS["AI Applications"]
FI --> APPS
FBI --> APPS
style WC fill:#0d6efd,color:#fff
style FC fill:#198754,color:#fff
style FBC fill:#ffc107,color:#000
style APPS fill:#6f42c1,color:#fff
Three production-ready AI apps built on top of the IQ engines, each serving a distinct persona and use case.
A conversational AI advisor that provides personalized academic guidance by reasoning over the student's activity history, institutional policies, degree requirements, and career outcome data.
A predictive ML model that identifies at-risk students early by combining behavioral signals (Work IQ) with performance analytics (Fabric IQ), enabling proactive intervention.
Generates individualized semester-by-semester learning plans that adapt to the student's pace, strengths, and goals - using curriculum knowledge from Foundry IQ and performance insights from Fabric IQ.
| Semester | Courses | Credits | Difficulty | Notes |
|---|---|---|---|---|
| Fall 2026 | CS 301, MATH 320, CS 350, GEN ED | 15 | Medium | CS 301 prerequisite for ML track |
| Spring 2027 | CS 410 (ML), CS 360, STAT 400, CS 399 (Research) | 14 | High | Lighter load: STAT 400 has 18% DFW |
| Summer 2027 | Internship (ML Engineer @ Partner Co.) | 3 | Low | Co-curricular credit |
graph TB
subgraph DataSources["Institutional Data Sources"]
direction LR
LMS["LMS<br/>Canvas / Blackboard<br/>Moodle"]
SIS2["Student Information<br/>System"]
CATALOG["Academic Catalog<br/>& Policies"]
RESEARCH["Research<br/>Repositories"]
DW2["Data Warehouse<br/>& BI"]
end
subgraph CrawlerLayer["AI Crawler Layer"]
direction LR
W_CRAWL["Work IQ<br/>Crawler"]
FB_CRAWL["Fabric IQ<br/>Crawler"]
F_CRAWL["Foundry IQ<br/>Crawler"]
end
subgraph AzureAI["Azure AI Platform"]
subgraph Ingest["Ingestion & Processing"]
direction LR
ADF2["Data Factory<br/>Pipelines"]
DBX2["Databricks<br/>Feature Engineering"]
end
subgraph Storage2["Storage Layer"]
direction LR
ADLS2["ADLS Gen2<br/>Medallion Architecture"]
COSMOS["Cosmos DB<br/>Student Profiles"]
end
subgraph Intelligence["Intelligence Layer"]
direction LR
SEARCH["AI Search<br/>Vector + Semantic"]
AOAI3["Azure OpenAI<br/>GPT-4o + Embeddings"]
AML3["Azure ML<br/>Risk Models"]
end
subgraph Apps2["AI Applications"]
direction LR
APP_ADV["AI Academic<br/>Advisor"]
APP_RISK["Dropout Risk<br/>Predictor"]
APP_PLAN["Learning<br/>Planner"]
end
end
subgraph Consumers["End Users"]
direction LR
STUDENTS["Students"]
ADVISORS["Academic<br/>Advisors"]
ADMIN["Administrators"]
end
LMS --> W_CRAWL
SIS2 --> W_CRAWL
CATALOG --> F_CRAWL
RESEARCH --> F_CRAWL
SIS2 --> FB_CRAWL
DW2 --> FB_CRAWL
W_CRAWL --> ADF2
FB_CRAWL --> ADF2
F_CRAWL --> ADF2
ADF2 --> DBX2
DBX2 --> ADLS2
ADF2 --> ADLS2
ADLS2 --> SEARCH
ADLS2 --> AML3
COSMOS --> APP_ADV
SEARCH --> AOAI3
AOAI3 --> APP_ADV
AOAI3 --> APP_PLAN
AML3 --> APP_RISK
SEARCH --> APP_PLAN
APP_ADV --> STUDENTS
APP_PLAN --> STUDENTS
APP_RISK --> ADVISORS
APP_PLAN --> ADVISORS
APP_RISK --> ADMIN
style W_CRAWL fill:#0d6efd,color:#fff,stroke:#0d6efd
style F_CRAWL fill:#198754,color:#fff,stroke:#198754
style FB_CRAWL fill:#e8a317,color:#fff,stroke:#e8a317
style APP_ADV fill:#0d6efd,color:#fff,stroke:#0d6efd
style APP_RISK fill:#dc3545,color:#fff,stroke:#dc3545
style APP_PLAN fill:#198754,color:#fff,stroke:#198754
style AOAI3 fill:#6f42c1,color:#fff,stroke:#6f42c1
style SEARCH fill:#0dcaf0,color:#000,stroke:#0dcaf0
style AML3 fill:#fd7e14,color:#fff,stroke:#fd7e14
sequenceDiagram
participant LMS as LMS / SIS
participant Crawler as IQ Crawlers
participant ADF as Data Factory
participant ADLS as ADLS Gen2
participant DBX as Databricks
participant Search as AI Search
participant AOAI as Azure OpenAI
participant App as AI App
participant User as Student / Advisor
LMS->>Crawler: Raw events & documents
Crawler->>ADF: Structured + unstructured data
ADF->>ADLS: Bronze (raw) layer
ADLS->>DBX: Feature engineering
DBX->>ADLS: Silver + Gold layers
ADLS->>Search: Chunked docs + embeddings
User->>App: "What courses for ML career?"
App->>Search: Hybrid query (vector + keyword)
Search->>AOAI: Top-K chunks + student context
AOAI->>App: Grounded response
App->>User: Personalized recommendation
The Education Platform crawlers are directly aligned to the Work IQ / Foundry IQ / Fabric IQ paradigm, leveraging the same Azure-native crawler framework used across all verticals.
| Crawler Component | Work IQ | Foundry IQ | Fabric IQ |
|---|---|---|---|
| Connector Type | REST APIs (LMS, SIS), Graph API (Teams) | SharePoint, Blob Storage, HTTP (catalogs) | SQL (Data Warehouse), REST (SIS APIs) |
| Crawl Frequency | Near real-time (event-driven) | Daily (document corpus) | Hourly (metrics) + Weekly (batch scores) |
| Document Processing | JSON events → tabular features | PDF/DOCX → chunked text + embeddings | SQL rows → aggregated metrics |
| Embedding Model | text-embedding-3-large (activity vectors) | text-embedding-3-large (document vectors) | Tabular embeddings (custom model) |
| Index Strategy | Per-student activity index | Institutional knowledge graph index | Per-student performance time-series |
| Retention | Rolling 2 years (FERPA) | Permanent (institutional records) | Rolling 7 years (outcomes tracking) |
| Service | Role | SKU / Config | Est. Monthly Cost |
|---|---|---|---|
| Azure OpenAI | LLM inference (Advisor, Planner) + embeddings | S0, GPT-4o + text-embedding-3-large | $1,200 — $3,000 |
| Azure AI Search | Vector + semantic index across all IQ engines | Standard S1 (25 GB) | $250 |
| Azure Machine Learning | Dropout risk model training + managed endpoints | Workspace + Standard_DS3_v2 compute | $350 — $800 |
| Azure Databricks | Feature engineering, ETL pipelines | Premium, 4-8 nodes | $600 — $1,500 |
| ADLS Gen2 | Medallion data lake (Raw, Curated, Serving) | 3 accounts, HNS enabled | $150 — $400 |
| Cosmos DB | Student profiles, chat history, session state | Serverless or 1000 RU/s provisioned | $50 — $200 |
| Data Factory | Orchestrate crawler pipelines, data movement | V2, managed VNet IR | $100 — $300 |
| App Service | Web front-end for AI apps | Premium v3 P1v3 | $120 |
| Key Vault | Secrets, certificates, encryption keys | Standard | $5 |
| Application Insights | APM, request tracing, custom metrics | Workspace-based | $25 — $75 |
| Power BI | At-risk dashboards, institutional analytics | Pro licenses (10 advisors) | $100 |
| Estimated Total | $2,950 — $6,600 / mo | ||
gantt
title Education Platform - Implementation Roadmap
dateFormat YYYY-MM-DD
axisFormat %b %Y
section Foundation
Landing Zone & Networking :done, lz, 2026-04-01, 3w
IAM & Governance Policies :done, iam, 2026-04-01, 2w
ADLS Gen2 Medallion Setup :done, adls, 2026-04-15, 2w
section Crawlers
Work IQ Crawler (LMS + SIS) :active, wiq, 2026-05-01, 4w
Foundry IQ Crawler (Catalog + Docs) :fiq, 2026-05-01, 4w
Fabric IQ Crawler (DW + Metrics) :fbiq, 2026-05-15, 3w
AI Search Index Build :idx, after wiq, 2w
section AI Models
Dropout Risk Model V1 Training :ml1, 2026-06-01, 4w
Model Validation & Bias Audit :ml2, after ml1, 2w
section Applications
AI Academic Advisor MVP :app1, 2026-06-15, 5w
Dropout Risk Dashboard :app2, 2026-07-01, 4w
Learning Planner V1 :app3, 2026-07-15, 5w
section Launch
Pilot (100 students) :pilot, 2026-08-15, 4w
Full Rollout (Fall Semester) :launch, 2026-09-15, 2w