One of the most persistent challenges in graphic design education is teaching students how to think critically about information, not just how to visualize it. As design problems increasingly intersect with complex industries—and as AI tools confidently generate plausible but flawed research—students must learn to question inputs before translating them into visual outcomes.
To address this gap, I developed a senior-level assignment structured around a simple but powerful premise: designers often receive incomplete, contradictory, or incorrect information, and their professional responsibility is to identify and resolve those inconsistencies before they become embedded in design decisions.
The project, titled Design Under Uncertainty, places students in the role of a designer working for a real-world industry client—in one implementation, a plumbing company. Students are provided with industry-specific training papers, background documents, and client context. They are also encouraged to use AI tools to assist with research and synthesis.
What students are not told is that somewhere within the provided information—or within commonly surfaced AI output—there exists a plausible but incorrect claim that directly contradicts prior lecture material or established industry norms.
This omission is intentional.
Rather than framing the project as an error-detection exercise, the assignment evaluates how students reason through ambiguity, verify claims, and justify decisions. Students are never graded on whether they “found the wrong fact.” Instead, they are assessed on how they determine which information to trust, which assumptions to question, and how clearly they can defend the logic behind their design choices.
This mirrors professional design practice more accurately than traditional briefs. In real client work, designers routinely encounter outdated documents, misunderstood metrics, biased stakeholders, and confidently delivered misinformation—often without knowing it at the outset. The ability to cross-check, triangulate sources, and reconcile contradictions is as critical as visual skill.
The design component of the assignment remains flexible. Depending on the course focus, students may produce brand systems, marketing materials, websites, or service-oriented design artifacts. What remains constant is the requirement that every major decision be grounded in validated reasoning, not surface-level acceptance of provided inputs.
AI plays a deliberate role in the project. Students are encouraged to use it, but are held accountable for its output. This reframes AI from a shortcut into a stress test: tools that generate fluent, authoritative-sounding content become part of the problem space students must navigate critically. In this context, skepticism becomes a design skill.
One of the strengths of this assignment is its adaptability. While plumbing serves as an accessible starting industry—familiar, tangible, and service-oriented—the structure can be applied to healthcare, construction, finance, education, logistics, or nonprofit sectors with minimal adjustment. The core learning outcome remains the same: designers must learn to operate when information is uncertain, incomplete, or wrong.
The assignment also scales well across student ability levels. Stronger students often surface uncertainty explicitly and document their verification process. Less confident students may initially rely too heavily on provided materials, revealing gaps in critical thinking that can be addressed constructively through feedback.
Ultimately, Design Under Uncertainty shifts design education away from polish-first evaluation and toward judgment-centered assessment. It prioritizes thinking over execution, reasoning over assumption, and responsibility over obedience. In an era where information abundance and automation blur the line between accuracy and plausibility, teaching students how to decide what to trust may be one of the most important design skills we can cultivate.
Links to the Description and Master Prompt here.