INNOVATION HUB · DESIGN THINKING

Human Skills and AI in Design Thinking

People first. AI on purpose.

Design Thinking has always been about people. You understand a problem by listening to the people who live with it. You solve it by working with others who see things differently than you do. That part of the work has not changed.

What has changed is AI. It can now draft, sort, summarize, and come up with ideas faster than any team. That makes the human side of design thinking matter more, not less. Someone still has to listen, notice what is really going on, disagree well, and decide what is true. AI cannot do that part for us.

This page is for students and instructors at the Innovation Hub. It walks through each stage of our design thinking process. At every stage, it highlights relevant human skills and how AI can help when used intentionally to augment what we already bring to the process. Instructors can use it to plan lessons. Student teams can use it as a guide throughout their design thinking project.

Working with people. Working with AI.

Human skills

How we work with people. In design thinking, people come first — both the people we design for and the people we design with. This is a key part of the work AI cannot do for us. That means naming what we and others are feeling, listening to understand, noticing when stress pulls us off course, being honest about our own part when conflict comes up, and staying present with a team when the work gets hard.

AI Fluency

How we work with AI. In our classes, we think of AI as the last team member. It joins after the people, and the team decides together what it does. That means deciding when to use it, asking for what we need clearly, checking what it gives back, and being open about how we used it. Used this way, AI adds to what the team brings instead of replacing it.

The 4 Ds of AI Fluency

AI Fluency means using AI well. That covers getting good results, working efficiently, and using it ethically and safely. Sometimes AI does a task for us, sometimes it works alongside us, and sometimes it acts on its own. In each case, the human skills stay the same.

For this, we use the 4D AI Fluency Framework. The four Ds are not steps you do once. Teams move through them again and again as a project goes on. The examples below follow a team whose project focuses on how first-year students keep track of deadlines. The 4D AI Fluency Framework was developed by Prof. Rick Dakan and Prof. Joseph Feller in partnership with Anthropic.

D1 · Delegation

Deciding whether AI should help with a task at all, and what to keep for ourselves. Some tasks go faster with AI. Others only work when people do them, because the value comes from doing the work ourselves. Delegation means making that choice on purpose before the work starts, and checking it again as the work goes on.

In design thinking. At the Empathize stage, the team could ask AI to describe how first-year students feel about deadlines. It would answer quickly, but it would be a guess, not something real students said. So the team interviews six students themselves. Later, at the Synthesize stage, they give AI their interview notes to help sort them into early themes.

Ask yourself. If AI did this step for us, what would we stop noticing, learning, or understanding?

D2 · Description

Giving AI enough background to be useful. That means saying what the goal is, who it is for, what you already know, and what a good answer looks like. If the first answer misses, you explain what to change and try again.

In design thinking.‍ ‍At the Ideate stage, a weak prompt is "Give us ideas for a study app." A stronger prompt uses what the team learned in earlier stages. "We interviewed six first-year students. They told us they lose track of deadlines across different classes, and that they already have too many apps to keep up with. Give us ten ideas that do not add another app."

Ask yourself. Did we tell AI what we learned from the people we talked to?

D3 · Delegation

Checking what AI gives back before we use it. AI can sound confident and still be wrong, leave people out, or repeat common assumptions. Discernment means reading AI's answers the way we would read a classmate's draft, carefully and with the evidence in front of us.

In design thinking. At the Synthesize stage, the team asks AI to look over its notes for themes it might have missed. AI suggests that students want more reminders on their phones. The team goes back to its notes and finds that two students said the opposite. They already have too many apps and notifications to keep up with. The team drops the suggestion and adds a theme that matches what the students actually said.,

Ask yourself. Can we point to where someone actually said this?

D4 · Diligence

Taking responsibility for how we use AI and being open about it. The team, not AI, is responsible for every claim in the final work. Diligence means keeping track of where AI helped and making sure nothing it made up gets presented as something people told us.

In design thinking.‍ At the Pitch stage, AI can help design the slides. In one draft, it adds the line "Most first-year students miss at least one deadline a semester." None of the six students interviewed actually said that, and the team has no source for it, so they cut it. On the last slide, the team notes where AI helped. It checked the team's themes, suggested ideas, and polished the wording.

Ask yourself. Can we point to the person or source behind every claim in our final pitch?

Discernment and Diligence depend most on human skills. AI does not replace any stage of design thinking. It adds a new habit inside every stage. People do the work first, AI adds to it, and then people check what it gives back. AI can help point out what to check, but it cannot be the final judge. It was not in the room, and it does not answer for the work. When we check, we speak for the people we interviewed, making sure their voices are not replaced by a confident guess.

The ten design thinking stages, with people and AI

For each stage of the Innovation Hub design thinking process, here is the human skill that matters most, where AI Fluency comes in, and one idea for instructors.

1 · How Might We

Turn a broad topic into a question worth working on.

Human skill. Curiosity. Staying with the question before rushing to answers. This matters more with AI, because AI will answer whatever question it is given, even a weak one. Ask it "How might we build an app to help students track deadlines?" and it will hand back ten apps without asking whether students want another app. "How might we help first-year students keep track of deadlines across their classes?" leaves room to find out what they actually need. AI can produce answers. Deciding which question is worth asking is still the team's job.

AI Fluency. Delegation and Description. Each team member writes three How Might We questions on their own first. The team picks its strongest one and asks AI to rewrite it five ways: broader, narrower, from a student's point of view, from an instructor's point of view, and without assuming a solution. The team compares the versions and chooses. AI does not choose.

For instructors. Have each student write three questions before anyone opens AI. Then give AI one of the weaker questions and read its answers together. Ask, "What did AI assume because of how the question was worded?" Students see that AI answers the question it is given without questioning it. Questioning the question is a human skill, and it matters more as AI becomes part of the workflow.

2 · Team

Turn a broad topic into a question worth working on.

Human skill. Saying how you work best and what you need from others. For example, "I think best on my own first, so please send the agenda the night before," or "I go quiet when I disagree, so please ask me directly." This matters more with AI, because it is easy for each person to go off and work with AI alone. The team can end up with four separate projects instead of one. AI cannot tell you what your teammates need. Only they can, and only if someone asks.

AI Fluency. Delegation and Diligence. AI stays out of getting to know each other. Before the project starts, the team writes down how it will use AI. For example, "We do our own interviews. We write our own ideas before asking AI. We note every time AI helps."

For instructors. Have each person finish two sentences out loud. "When we use AI, I need the team to..." and "I will know we are leaning on AI too much when..." Teams add their answers to their team agreement. Check back mid-project and ask whether anyone has been working with AI alone instead of with the team.