A studio course in which cross-disciplinary teams respond to real-world product challenges submitted by partner organizations — from paper sketch to working, validated AI-enabled product.
Build Studio trains students to translate ambiguous organizational challenges into working, validated technology products. Teams apply IS frameworks — design science, data strategy, platform theory, and human+AI collaboration — to build real products for real partner organizations.
By the end of the semester, every team has conducted iterative user research, built a working AI-enabled prototype, tested it with real users, and presented a validated product to their partner organization.
"This is not a planning course; it is a build course."
Every team moves through the same structured arc: stand up a narrative, validate it with real users, then deliver a working product to a partner organization.
By the end of Build Studio, every student will have demonstrated these capabilities with a real product for a real organization.
Translate an ambiguous organizational challenge into a testable product concept using design science methodology.
Conduct structured user research and synthesize findings into system requirements and user stories.
Build progressively higher-fidelity prototypes — from paper sketch to working AI-integrated demo — across three sprint cycles.
Apply IS frameworks (platform theory, data strategy, human+AI collaboration) to product design decisions.
Validate a product concept with real users and articulate evidence-based learnings.
Present a product narrative and working demo to technical and non-technical stakeholders.
Collaborate effectively in a multidisciplinary team with students from IS/Analytics, Management, and Marketing.
| Week | Phase | Studio Activity | Key Deliverable |
|---|---|---|---|
| 1 | Unpack | Challenge reveal · System diagram: users, data flows, boundaries | System diagram draft |
| 2 | Research | User interview sprint (n=5+) · Affinity mapping · Initial personas | Interview log · Personas (v1) |
| 3 | Sketch | 10 individual concepts · Team convergence · AI feature scoping | 10 sketches · AI rationale memo |
| 4 | Select | Core product direction · Lo-fi prototype (paper or Figma) · 3 user stories | Lo-fi prototype · User stories · Updated system diagram |
| 5 | SPRINT 1 | Stand Up Your Narrative — 3-minute presentation + lo-fi demo | Sprint 1 deck + lo-fi demo · Interview log (n=5+) |
| 6 | Standup | Agile/scrum methodology · No-code/low-code AI tools (v0, Cursor, Vercel AI SDK) | Sprint plan (weeks 6–9) |
| 7 | Build | First working prototype — narrative-complete product (NCP) end-to-end | Working prototype (v1) · Daily standup log |
| 8 | Test | Structured user testing (n=5+) · "Smile test" · User findings log | User findings log (n=10+ cumulative) |
| 9 | Iterate | Scope triage · Data architecture workshop · Privacy implications | Data architecture diagram · Updated prototype |
| 10 | SPRINT 2 | Validate Your Narrative — 4-minute presentation + working demo | Sprint 2 deck + demo · Findings log (n=10+) · Data diagram |
| 11 | Elaborate | Tech Talk: IS students lead 4-min architecture walkthrough · Stack decisions | Tech Talk (recorded) · Architecture diagram (final) |
| 12 | Design | Human+AI interface design · Communicating AI outputs · Uncertainty & explainability | Updated prototype with UI polish · HCI reflection note |
| 13 | Prove | Final validation sprint (n=3+) · Feature-complete NCP · External crit session | NCP (feature-complete) · Findings log (n=15+) · Crit summary |
| 14 | Communicate | Critique and persuasion · Presenting to non-technical audiences · Reflection articles | Reflection article (individual) |
| 15 | FINAL | Deliver — 6-minute final presentation to partner organization | Final deck + NCP demo · Full user findings log · Team poster · Handoff package |
Grades reflect the build, not just the idea. Teams are evaluated on the quality of their product, the rigor of their user research, and the strength of their presentation.
Partner organizations submit a short brief describing a real business problem or opportunity where an AI-enabled system could create value. A cross-disciplinary student team will work on your challenge across the full semester.
At the end, your team receives a working prototype, validated with real users, and a full product handoff package — plus a final presentation from the student team.
Submit a Challenge Brief