This month’s field note explores how understanding learners’ starting points can lead to more intentional learning design.

📓 Field Note: Participants rarely enter learning experiences from the same place.
📍 Sparked by: Sessions and participant responses at the TCEA AI Conference
💡 Big idea: Understanding where learners are helps us design more meaningful pathways forward.

I spend a lot of time with educators who are curious about technology. They test new tools, share ideas online, and happily experiment with emerging technologies. Many of my closest professional friends fall into that category. After a while, it’s easy to assume that most educators are further along than they actually are.

The recent TCEA AI Conference reminded me otherwise.

Throughout the conference, I noticed an enormous range of experience levels. In some sessions, presenters moved quickly through advanced AI workflows. In others, presenters carefully introduced tools feature by feature. During my own Google Vids session, I used a quick fist-to-five self-assessment to gauge participants’ familiarity with the tool. I expected a mix of responses, but I was still surprised by the number of zeros and ones in the Zoom room.

That moment reminded me of something Julie Dirksen writes in Design for How People Learn: “Whenever you have a lot of knowledge about something, you have a picture in your mind, and your learners may not.”

My conference takeaway wasn’t really about AI. It was a reminder of a challenge that exists in every learning experience: participants rarely enter from the same starting point.

Whether we’re designing for new teachers, campus leaders, conference attendees, or educators exploring AI, the labels we use often hide a wide range of experiences, expectations, and needs.

As adult learning designers, that raises an important question:

One reason this challenge shows up so often in professional learning is that participants don’t all arrive with the same experiences, knowledge, or expectations.

That gap matters because it influences how learners make sense of new information.

In Training Design, Delivery, and Diplomacy, A. Keith Young and Tamarra Osborne explain that different learning goals often require different approaches. When learners need to develop foundational skills, procedures, or routines, explicit instruction may be the best fit. More conceptual learning often benefits from opportunities to explore, discover patterns, and construct meaning. However, those experiences still depend on what learners already know and understand.

If prior knowledge influences how people learn, then understanding our audience becomes an important part of the design process.

This is where formative assessment becomes so valuable.

In Embedded Formative Assessment, Dylan Wiliam describes formative assessment as information that helps teachers make real-time adjustments to better meet learners’ needs. The goal isn’t simply collecting data. The goal is using that information to inform what happens next.

That’s why I still love simple strategies like fist-to-five. In less than a minute, I can learn something about participants’ prior experiences, confidence levels, and perceived expertise. More importantly, that information helps me make decisions as a facilitator.

The challenge isn’t designing for an “average” participant. It’s recognizing that there often isn’t one.

One lesson I’ve learned over the years is that broad questions often produce broad answers.

When participants tell me they’re “comfortable with AI,” “new to teaching,” or “experienced with Google tools,” I often discover those labels mean very different things from person to person.

Take new teacher training, for example. A “new teacher” might be a recent college graduate with an education degree, a veteran teacher who has moved to a new district, or someone entering the profession through an alternative certification pathway. While they may share the same label, their experiences, needs, and starting points can be very different.

Instead of relying on general descriptions, I’ve started looking for ways to get more specific.

Rather than asking whether participants are comfortable with AI, I might ask:

Questions like these help paint a clearer picture of who is actually in the room.

That same specificity can show up before participants ever arrive.

Priya Parker writes that gathering with purpose often requires “closing doors with purpose.” As learning designers, we sometimes hesitate to narrow our focus because we want everyone to feel welcome. But clearer boundaries often create better learning experiences.

A session described as “New Teacher Training” could attract participants with very different backgrounds and needs. A session described as “Classroom Management for First-Year Teachers” or “Getting Started for Alternative Certification Teachers” sends a much clearer signal about who the experience is designed to support.

Specificity doesn’t exclude learners.

It helps learners find experiences that match their starting points and helps designers create learning that better serves the people who are actually in the room.

As you design your next learning experience, consider:

Small adjustments like these can help us move beyond designing for an imagined audience and toward designing for the learners who are actually in the room.

What information about your learners would most improve your next learning experience?

How do you currently learn about participants’ experiences, expectations, or prior knowledge?

Where might greater specificity help you better serve your audience?

What small design move could help you (and your participants) better understand who is in the room?

Noting, reflecting, connecting,
Kathryn

PS: This reflection connects closely to an earlier Field Note, Designing Intentional Learning Pathways, where I explored how helping participants find an entry point into the learning can reduce overwhelm and support meaningful engagement. This month’s reflection focuses on the design work that comes before that: understanding where learners are starting in the first place.

This newsletter is inspired by the idea of self-anthropology from Tiny Experiments by Anne-Laure Le Cunff. Just like field notes help researchers make sense of what they observe, these monthly reflections help me capture ideas, tools, and questions from my own work designing professional learning. I share them here in the spirit of experimentation, connection, and ongoing refinement.

🤖 AI helped polish this post, but the ideas, intention, curation, and care are all mine.