Georgetown Wicked Problems Lab: a classroom where AI assists but never decides

The situation

Georgetown's School of Foreign Service teaches security studies students to reason about wicked problems. These are complex policy challenges where the right answer depends on judgment, framing, and willingness to sit with ambiguity. Off-the-shelf AI tools are eager to give students the answer. That's exactly the wrong behavior for teaching policy reasoning. An AI that writes the student's analysis teaches the student nothing except how to paste.

The faculty wanted something different. They wanted a teaming platform where the AI helps with the mechanical parts of analysis (retrieving sources, surfacing counterexamples, flagging weak evidence) while leaving every real judgment where it belongs.

What we built

The Wicked Problems Lab. Student teams work through a structured four-phase sequence. The four phases are problem articulation, refinement, solution planning, and pressure testing. Each team has a project, a chat surface, and an orchestrator running in the background. The orchestrator listens to the team's conversation (and, optionally, their voice), classifies intent, and decides which of eight specialized analysis agents to dispatch.

The agents run in three tiers. A fast tier handles intake, triage, and framing. A deeper tier does synthesis and analysis. A challenge tier pressure-tests the team's reasoning, surfaces gaps, and runs adversarial review. Every agent surfaces to the team only if its output clears an importance threshold, so students aren't drowned in low-value chatter. Instructors get cross-team visibility, voice controls, and intervention tools.

The delegation contract

The platform's most important code is the smallest. It's a delegation contract, a short set of rules that constrains what the AI is allowed to do inside the learning loop.

The AI cannot verify the team's assumptions. If a student states an assumption, the system records it as an open assumption and surfaces it for pressure testing. The AI never marks an assumption as "verified" on the student's behalf.

The AI cannot define success criteria. Students decide what a good answer looks like for their project. The AI can suggest criteria to consider, but it cannot write them into the project record.

The AI cannot advance the team past a phase gate. The platform enforces phase transitions at the database level. An agent cannot call "you're done, move on." Only the team's human decisions, captured through specific UI actions, can trigger a transition.

Three short rules. They change the whole shape of the product. A platform that enforces them turns the AI into a teaching tool, and a platform without that enforcement would amount to a chatbot subscription with extra steps.

What it replaced

Two alternatives, both bad. The first is keeping AI out of the classroom entirely, which misses the chance to teach students how to work with these tools in a professional setting. The second is letting a generic assistant write the analysis, which misses the point of the class. The Wicked Problems Lab is the third option. AI in the room, under discipline.

What a similar engagement looks like

Fourteen to eighteen weeks for a first deployment. We need faculty time to define the phase sequence, the success-gate criteria, and the agent library for the domain. You get a deployed platform with the orchestrator, the agent library, the gate enforcement, instructor tooling, and a voice subsystem if the classroom wants it.

The classroom exists to develop the student's judgment. Output is a side effect. The job of the student is to do the thinking, badly at first and better over time. An AI that does the thinking on the student's behalf forecloses the development the classroom exists to produce. The platform's discipline is the product.

It fits universities, executive education programs, professional training, and any setting whose purpose is the development of human judgment. It's the wrong product if you want an AI that does the work and hands you the answer.