1 Executive Summary
This POC delivers two categories of AI-powered solutions for Georgetown University: AI Advisors (knowledge-based assistants for students and faculty) and an AI Teaching Agent (an autonomous agent that administers day-to-day course operations). The goal is to maximize educational impact while maintaining full faculty oversight.
Target: Build & test through summer → Deploy for one course in Fall 2026 semester → Validate → Scale university-wide.
2 System Architecture
👤 Users
Students
Web app, mobile, LMS integration
Faculty
Dashboard, review portal, config panel
Administrators
Analytics, compliance, system management
↕
🖥️ Application Layer
Student Advisor UI
Chat interface, course explorer, degree planner
Faculty Advisor UI
Protocol assistant, prep tools, collateral builder
Teaching Agent Dashboard
Course admin, grading queue, escalation center
↕
🤖 AI / Agent Layer
RAG Engine
Retrieval-augmented generation over Georgetown knowledge bases
LLM Orchestrator
Prompt routing, context management, guardrails
Agent Runtime
Task execution, workflow automation, human-in-the-loop
↕
💾 Data & Knowledge Layer
Course Knowledge Base
Syllabi, materials, lecture content, assignments
Institutional KB
Policies, protocols, degree requirements, catalogs
Student Records
Progress, grades, interactions (privacy-gated)
↕
🔌 Integration & Infrastructure
Georgetown LMS
Canvas / Blackboard integration
SSO / Auth
Georgetown NetID, SAML/OAuth
Cloud Infrastructure
Hosting, vector DB, monitoring, logging
3 Solutions & Feature Breakdown
P1 POC Core
P2 Phase 2
🎓 AI Advisors
GENERATIVE AI · RAG
Georgetown-specific conversational AI that enables students and faculty to navigate academic life — course selection, content Q&A, protocol guidance, and collateral creation.
Student Advisor
- P1
Course Q&A Chat
Ask questions about any course — content, difficulty, prerequisites, workload
- P1
Content Summarization
Summarize last N weeks of lectures, readings, or discussions
- P1
Degree Journey Navigator
Course recommendations based on degree requirements, interests, career goals
- P1
Related Topic Explorer
Surface adjacent concepts that integrate with classroom material
- P2
Career & Employment Insights
Post-degree outcomes, career path mapping based on course selections
- P2
Study Group Matching
Connect students studying similar topics or preparing for exams
Faculty Advisor
- P1
Protocol & Policy Assistant
Grade dispute resolution, incident navigation, academic integrity procedures
- P1
Course Prep Assistant
Help faculty prepare lectures, discussion topics, and reading lists
- P1
Collateral Builder
Generate slides, handouts, quizzes, and supplementary materials from topic inputs
- P2
Student Engagement Analytics
Insights into which students are struggling, engagement patterns, at-risk flags
- P2
Cross-Course Intelligence
What's working in other courses, best practices from peers
🤖 AI Teaching Agent
AUTONOMOUS AGENT · HUMAN-IN-THE-LOOP
An autonomous agent that administers day-to-day course operations — grading, communications, content delivery — with minimal faculty involvement but maximum faculty oversight and escalation controls.
Core Capabilities
- P1
Assignment Grading Engine
AI-assisted grading with rubric adherence, consistency checks, faculty approval queue
- P1
Student Communication Hub
Automated announcements, deadline reminders, Q&A responses on behalf of course
- P1
Course Content Delivery
Scheduled release of materials, adaptive content suggestions based on progress
- P1
Escalation & Override System
Auto-flag disputes, integrity concerns, and edge cases for faculty review
- P1
Faculty Oversight Dashboard
Real-time view of all agent actions, approval queues, override controls
- P2
Adaptive Learning Paths
Personalized content sequencing based on student performance and engagement
- P2
Office Hours Agent
Virtual office hours that handle common questions, escalate complex ones to faculty
- P2
Exam Proctor Assistant
Automated exam administration, time management, accommodation handling
Key Principle: The agent acts autonomously on routine tasks but NEVER overrides faculty judgment. All consequential decisions (final grades, disciplinary actions, policy exceptions) require explicit faculty approval.
4 Knowledge Management Architecture
Both solutions are powered by a unified knowledge management layer. Content flows through ingestion, indexing, and retrieval pipelines to ensure accurate, up-to-date, Georgetown-specific responses.
Ingestion Pipeline
Source Content
Syllabi, policies, lecture notes, catalogs, LMS data
→
Processing
Chunking, embedding, metadata tagging, deduplication
→
Vector Store
Indexed embeddings with semantic search + hybrid retrieval
↕
Retrieval & Generation
User Query
Natural language question or task request
→
RAG Pipeline
Query → Retrieve → Rerank → Augment → Generate
→
Response
Cited, contextual answer with source attribution
5 Project Timeline
Phase 1: Foundation
Mar — Apr 2026
- Architecture finalization
- LMS integration discovery
- Knowledge base ingestion pipeline
- Auth & SSO setup
- Core RAG engine build
Phase 2: Build
May — Jun 2026
- Student & Faculty Advisor UIs
- Teaching Agent core logic
- Grading engine prototype
- Escalation workflows
- Faculty dashboard
Phase 3: Test
Jul — Aug 2026
- Internal QA & load testing
- Faculty UAT sessions
- Content accuracy validation
- Security & compliance review
- Campus integration testing
Phase 4: Pilot
Sep — Dec 2026
- Single course deployment
- Real student & faculty usage
- Feedback collection & iteration
- Performance metrics tracking
- Go/no-go for university rollout
6 Recommended Tech Stack
LLM Provider
OpenAI GPT-4o / Anthropic Claude
Multi-model with fallback; evaluate cost vs quality per use case
Vector Database
Pinecone / Weaviate / pgvector
Semantic search + hybrid retrieval for knowledge bases
Orchestration
LangChain / LangGraph
Agent workflows, tool calling, human-in-the-loop patterns
Frontend
Next.js / React
Student & faculty portals, dashboards, chat interfaces
Backend
Node.js / Python (FastAPI)
API layer, LMS integrations, background jobs
LMS Integration
LTI 1.3 / Canvas REST API
Deep integration with Georgetown's LMS
Auth
SAML 2.0 / Georgetown SSO
NetID authentication, role-based access control
Infrastructure
AWS / Azure (Georgetown's cloud)
FERPA-compliant hosting, data residency requirements
7 Key Risks & Mitigations
HIGH
AI Accuracy & Hallucination
Incorrect academic advice could impact student outcomes. Mitigation: RAG-grounded responses with source citations, confidence scoring, mandatory human review for consequential outputs.
HIGH
FERPA / Data Privacy Compliance
Student records are federally protected. Mitigation: Data minimization, role-based access, encryption at rest/transit, Georgetown IT security review, no PII in LLM training.
MED
Faculty Adoption & Trust
Faculty may resist AI involvement in teaching. Mitigation: Faculty-first design, full override controls, transparent agent actions, gradual autonomy ramp-up.
MED
Knowledge Base Freshness
Stale content leads to wrong answers. Mitigation: Automated ingestion pipelines with change detection, faculty content review workflows, version-controlled knowledge bases.
LOW
LLM Cost at Scale
University-wide deployment could be expensive. Mitigation: Caching, smaller models for simple queries, usage-based throttling, cost monitoring dashboards.
8 Success Metrics (Fall Pilot)
Student Engagement
70%+ adoption rate
Students in pilot course actively using advisor weekly
Response Accuracy
95%+ factual accuracy
Validated against faculty spot-checks and student reports
Faculty Time Saved
30%+ reduction in admin tasks
Measured via faculty surveys and task completion logs
Escalation Rate
<10% of agent actions
Low escalation = agent handling routine tasks effectively
Student Satisfaction
4.0+ / 5.0 rating
End-of-semester survey on AI tools usefulness
Zero Privacy Incidents
0 FERPA violations
No unauthorized data exposure during entire pilot
9 Immediate Next Steps
- Align on POC scope — confirm P1 features with Georgetown stakeholders
- Identify the pilot course and faculty champion
- Discovery: Georgetown LMS platform, SSO/auth, data access & FERPA review
- Content audit — what knowledge bases exist and in what format
- Tech stack decision & infrastructure provisioning
- Team allocation & sprint planning (target: 2-week sprints)