2025 ASEE Annual Conference & Exposition

This Great Ideas For Teaching (and Talking with) Students (GIFTS) paper introduces a classroom activity that leverages generative AI to engage first-year engineering students in client-centered design through the creation of a custom drink coaster. The activity emphasizes human-centered design principles, providing students with a structured yet creative introduction to using AI as a tool in the engineering design process.

First-year engineering students often arrive from high school environments where generative AI is discouraged or outright banned, leaving them uncertain of AI's role in their education and as an engineering tool. There is a growing need to help these students explore AI in a meaningful and productive way. This requires not only introducing them to the power of AI but also scaffolding their understanding so that they maintain agency, creativity, and decision-making within the engineering design process. This activity fills that need by providing a guided, hands-on experience where students use AI to brainstorm, generate ideas, and iterate on design choices, all while focusing on client needs and preferences.

The 75-minute classroom session is structured around the Stanford d-school's Design Process, particularly focusing on empathy and iterative design. Students begin by brainstorming interview questions for a historical or fictional client, with generative AI assisting them in refining those questions. This exercise immediately highlights how effective prompting can lead to more insightful answers, emphasizing that students need to remain thoughtful, critical, and curious about their interactions with AI. The students then conduct a simulated interview with their client using AI, which embodies the historical or fictional client, and use the conversation to extract design-relevant information. They document key insights, use AI to summarize conversations, and finally, employ generative AI to visualize and iterate on the design of a drink coaster that meets their client’s needs.

Presented are several examples of student work, showcasing the different outputs students created, from insightful interview questions to final coaster designs. We explore the kinds of choices students made in their prompts, client selection, and interview approach, analyzing how these decisions led to either productive or less effective outcomes. This work examines how certain student approaches were less aligned with the activity’s learning objectives and how others successfully fostered engagement with the design process, both leading to suggestions for scaffolding and improvements to the activity design to focus students on more effective pathways.

A culminating instructor-led class wide reflection (following suggested instructor prompts included in the paper) further probes students' understanding of and reflection on the use of AI. While the activity itself is scaffolded, these follow-up questions help instructors guide discussions on creativity, decision-making, and AI's role in the engineering workflow, as well as touching on some problematic and ethical considerations of generative AI. All together, this activity provides first-year engineering students with hands-on, scaffolded experiences that introduce them to the potential of generative AI without simply providing "the answers." It offers them a space to practice interacting with AI, including priming and prompt engineering for generative AI systems, in a low-stakes environment that supports productive client based engineering design and prepares them for more advanced, in-depth applications of generative AI throughout their later studies.