Roman vs. Greek Experimentation: Pilot-First Framework

Universities are rightly cautious about enterprise AI. Procurement cycles are long, governance is complex, and faculty buy-in is essential. Yet waiting for perfect information often means missing a moving target. In recent conversations with campus leaders, we’ve been using a simple lens to unblock progress: “Roman vs. Greek” experimentation.

• Greek: debate, theorize, and plan until consensus is complete.

• Roman: build something small, run it, measure it, and iterate based on what actually happens.

This isn’t a manifesto against deliberation. It’s a pragmatic way to gather evidence through action—with tight guardrails—so institutions can make informed decisions without committing to the wrong architecture, vendor, or operating model.

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What “Roman” Looks Like in Practice

Start with a contained, high-signal pilot—something that helps real learners or staff inside a single course, program, or service. In our work, common first steps include:

• Course mentors: instructor-configured AI that answers student questions with citations to the professor’s slides, readings, and policies.

• Prospective-student mentors: site-embedded Q&A trained on admissions pages and official documents to cut repetitive email volume.

• Faculty support assistants: a conversational pane that retrieves approved teaching resources, rubrics, and institutional policies on demand.

Each pilot is deliberately measurable and designed to respect governance boundaries.

Why Cautious Teams Favor This Approach

• Lower technical and institutional risk. You can deploy in your cloud or ours, use your own API keys if desired, and scope data access to a specific course or content set. If you decide not to scale, your materials stay under your control.

• Real signals, not hypotheticals. Pilots reveal how faculty, students, and IT actually use (or ignore) features—what needs better prompting, where safety rules bite, and which integrations are essential versus nice-to-have.

• Procurement-friendly. A short, time-boxed pilot with clear success criteria helps leadership compare platforms on architecture and outcomes rather than marketing slides.

• Expandable without rework. When pilots sit atop a flexible backbone—model-agnostic, LTI-capable, and API-driven—you can scale across departments without rebuilding from scratch.

A Pilot Blueprint That’s Worked on Real Campuses

From conversations and projects with institutions such as Syracuse University, George Washington University, NYU, SUNY, Morehouse College, and others, a durable pattern has emerged:

1. Choose the smallest unit that matters.

One course, one program page, or one student-service workflow. Define the content corpus (drag-and-drop files, specific URL paths, or LMS content via approved APIs) and the guardrails (what the mentor can and cannot answer).

2. Keep the human in the loop.

Instructors (or service owners) control tone, policies, and scope. They can inspect sources, review logs, and adjust prompts. Visibility—down to the question level—surfaces genuine learning gaps and improves trust.

3. Integrate where people already work.

Use LTI to place the assistant directly in an LMS as a right-hand side panel; embed admissions mentors on official pages; or expose features via a simple web app. No tab-hopping, no new portals.

4) Prove safety and alignment early.

Add a safety layer above the base model (e.g., limit responses to the course corpus; block off-topic queries). This demonstrates that institutional values and policies are enforceable in practice.

5) Measure what leaders care about.

Examples: time-on-task saved for faculty; reduction in repetitive email; percentage of answers with citations; student satisfaction; and cost per helpful interaction. These are the metrics that move decisions forward.

6) Offer multiple hosting and model paths.

Some teams want SaaS; others prefer to run in-house. Some use OpenAI; others prefer Gemini, Claude, or open-source models. A pilot should prove that you’re not locked in—you can right-size models by use case and change providers without re-platforming.

7) Support faculty like they’re customers.

Group onboarding plus one-on-one office hours removes technical barriers and accelerates real usage. (In our experience, this is often the difference between “interesting demo” and “kept using it after week two.”)

How ibl.ai Enables “Roman” Pilots Without Long-Term Lock-In

While every institution is different, we’ve standardized on a few architectural choices that make trial-and-learn easy for IT and comfortable for faculty:

• Model-agnostic layer: use OpenAI, Gemini, Claude, Llama, or campus-hosted models per course or use case.

• Code and data ownership options: deploy in your cloud or ours; bring your own keys if you wish.

• LTI-native: drop assistants into LMSs, SISs, and more.

• RAG by design: answers cite your materials (slides, PDFs, policies) rather than the open web.

• Safety on top of alignment: institution-specific filters and topic constraints, configurable by admins.

• APIs & SDKs: your developers can build apps directly against the same backbone our web apps use.

• Faculty support: onboarding, templates by discipline, and office hours to ensure the pilot yields real classroom value.

In several cases, campuses have run small, five-figure pilots (sometimes co-funded by cloud partners) to validate impact before expanding to a larger license. This keeps the decision rooted in data—not hype.

The Goal

The goal of a “Roman” pilot isn’t to solve AI for the whole university in six weeks. It’s to reduce uncertainty: Which models are cost-effective for everyday Q&A? Where do safety rules need tightening? How do faculty want to govern prompts? What integrations are non-negotiable? With those answers in hand, you can scale confidently—across advising, admissions, tutoring, content creation, and operations—without rewriting the playbook.

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Ready to Gather Evidence Through Action?

If your campus culture leans “Greek”—deliberative and thoughtful—consider pairing it with a Roman complement: a small, well-scoped pilot that earns trust, surfaces real constraints, and produces the kind of evidence committees actually need. We’re happy to collaborate on a plan that fits your governance model, your budget, and your timeline. Contact us at https://ibl.ai/connect