Teaching AI Without Losing the Human Question

A common language for a fast-moving field

Artificial intelligence now appears in courses from introductory composition to clinical decision-making. Summit's faculty working group began with a practical question: what should every graduate understand about these systems, regardless of major?

The resulting framework combines technical literacy with verification, attribution, privacy, bias, and the social consequences of automated decisions.

“The most important AI skill is not prompting. It is knowing which human judgment should never be delegated.”

Learning by examining limits

Students compare model outputs with primary sources, document failure modes, and explain when a tool should not be used. In design studios, they interview the people most affected before defining a technical solution.

Assignments reward transparent process over polished output. That means showing sources, testing claims, and describing where uncertainty remains.

• Verify generated claims against credible primary evidence
• Disclose when and how automated tools shape submitted work
• Evaluate downstream effects on privacy, access, and fairness

Preparing adaptable graduates

Specific tools will change. The durable skills are asking better questions, interpreting evidence, collaborating across specialties, and taking responsibility for a decision.

By placing those habits at the center, Summit is preparing students to shape AI-enabled workplaces rather than simply react to them.