October 20th, 2026
One reality of being a teacher, especially one working in the arts, is that I am regularly expected to do things I have never formally learned. On any given day, I might teach English, direct a performance, troubleshoot a projector, set up microphones, adjust stage lights, or try to make a sound system work properly. Often, there is no time to take a course or read an entire manual. I need to learn enough to solve the immediate problem while students wait for me to do it.
Recently, I used AI to help me make adjustments to a soundboard in our auditorium. I took a photograph of the controls and asked what each one did and what reasonable default settings might be. Because the AI could examine the image, it was able to identify the visible controls and suggest some initial adjustments. This made the information much more accessible than searching through technical manuals or trying to find a video featuring the exact same equipment.
The immediate results were obvious. I could make a few adjustments quickly and improve the sound for students using the space. However, simply copying the suggested settings only solved that particular problem. Sure, I might have ended up with better sound, but I did not necessarily understand why it sounded better or what to do next time. Instead, after the quick fix, I continued asking questions. Why did it recommend that setting? What did each control actually adjust? What would happen if I moved it higher or lower? How might the settings change depending on the microphone, speaker, or performer? The AI’s first response gave me a quick answer and easy solution, but the follow-up questions allowed me to begin developing a working understanding of the soundboard.
This distinction seems important in our discussion around AI. There is a difference between using AI to complete a task and using AI to support learning. If I had simply applied the recommended settings and stopped there, the technology would have done all of the thinking for me. However, by asking follow-up questions, testing the suggestions, listening to the results, and connecting the explanations to what I heard, I was still part of the process. AI could suggest where to begin, but I still had to decide whether the sound was actually improving. This process also made technical knowledge feel more approachable. Sound equipment can be intimidating because it is covered in technical labels, numbers, knobs, and sliders. Scouring the internet for help can sometimes feel like I already need to know the vocabulary to explain the problem. With AI, I could begin with a photograph and an imperfect question. I could ask for clarification without searching for hours or trudging through a technical manual, and I could keep asking follow-up questions until the explanation made sense to me.
What began as a small technical problem became a useful example of the kind of learning we regularly ask students to do. We want them to ask the right questions, dig deeper, test ideas, and internalize their learning. We also know that a good assessment of learning is whether they can explain what they have learned to someone else. In this case, my goal was not only to make the soundboard work better. I wanted to understand it well enough that I could use this knowledge without looking up the answers in the future and begin teaching students what I was learning. AI helped me learn quickly, but speed was not the most important outcome. Its greater value was giving me an accessible entry point into knowledge that previously felt technical and distant. The learning became meaningful when I moved beyond “What settings should I use?” and began asking “Why?” and “What happens if?” That is also where I see a potential role for AI in education. It can provide an immediate starting point, respond to follow-up questions, and help provide clarification. However, the learner still needs to test the information, make judgments, connect it to experience, and eventually explain it independently. The goal is not simply to get the soundboard settings right. The goal is to learn enough that the next time the sound is wrong, I have a better idea of what to listen for, what to adjust, and what questions to ask.
