A four-day design sprint for AI in higher education.

The challenge

Trinity Western University wanted to use AI to improve graduation rates for TWU Online, a new asynchronous education program serving adult learners. These students are balancing significant work and life commitments alongside their coursework, and the resulting attrition risk had become a strategic concern for the university.

The leadership team didn't want a roadmap or a strategy deck. They wanted to move from concept to evidence quickly, and to test whether AI could meaningfully change the student experience for the better.

Our approach

We ran a five-day design sprint that compressed the entire arc of an AI engagement into a single working week:

• Day 1, Leadership problem definition. We aligned with TWU's leadership on the specific student-experience problems worth solving and the criteria for success.
• Days 2–3, Prototype development. We built working AI prototypes that addressed the highest-priority opportunities surfaced in problem definition.
• Day 4, Testing with students and faculty. Real users tested the prototypes in realistic scenarios. We captured both qualitative reactions and clear go/no-go signals on each concept.
• Day 5, Executive reporting. We presented the validated solutions, the testing evidence, and a clear implementation proposal to leadership the same week we started.

Solutions developed

The sprint produced four testable AI solutions, not concepts:

• Course-specific AI Assistant for interactive study support inside each course
• Personalized course completion plans that adapt to individual circumstances and pace
• "What's Next" focus view that reduces cognitive load by surfacing only the next action a student needs to take
• Multilingual video introductions from professors to help international students connect with their instructors

Outcomes

The sprint produced testable solutions rather than concepts. Leadership gained clarity on AI's alignment with TWU's institutional mission, with concrete prototypes to evaluate rather than abstract opportunities to imagine.

Just as importantly, the team experienced renewed energy and creative possibility around student-centered technology. The week-long format made AI feel actionable, not theoretical.

Why this approach worked

• Time-boxed risk. A five-day commitment is small enough to say yes to, even before the value is proven. By the end of the week, the evidence was there.
• Real users, real scenarios. The five students who tested the prototypes gave the team something a deck never could: actual feedback from the people the work was designed to serve.
• Mission alignment baked in. Because leadership defined the problem on Day 1, every prototype was built against TWU's institutional goals from the start.