In museums and historic sites, whether you’re designing school programs, workshops, docent training, or exhibitions, understanding the needs and interests of your audience is key to success. But how do you efficiently analyze diverse feedback and connect it to your goals? Recently, I experimented with a creative process that combined audience input and AI to revise the learning objectives for my graduate course, Creating Sustainable Museums. The results not only improved the course but also offered insights into how AI can be used to enhance museum work.  

This approach was inspired by research conducted by Conny Graft at Colonial Williamsburg decades ago, which revealed that the goals of museum educators for school field trips often didn’t align with those of teachers. When those misalignments went unaddressed, they could lead to disappointment for both parties. Graft’s work emphasized the importance of finding common ground between institutional goals and participant expectations—a principle that remains essential in museum work today.

Start with Your Audience’s Goals

My course revision process began with a pre-course online survey, asking students to share what they hoped to know, feel, and do by the end of the semester. Using GPT, I quickly synthesized and categorized their responses to reveal predominant interests in financial, social, and environmental sustainability, as well as a strong desire to gain practical, job-ready skills. This step is akin to understanding your audience in a museum setting: what do your participants want to know, feel, or do? Are they looking for historical context, practical skills, or a new way to connect with the past?