More within my reach.
Drafting and revising emails. Working through the back-and-forth. Changing documents. AI touches everything I produce, from the small tasks to the finished work.
An AI field note
From everyday work to deeper learningAustin · Workshop edition
A personal perspective
From everyday work to deeper learningAustin · Workshop edition
A personal perspective
A team
of one.
More within
reach.
Everything I produce at work has been touched by AI. It’s part of how I organize, communicate, and get things done. That experience shapes what I want for learners.
Here’s what I’m learning1Human judgment
The back-and-forthEmails ↗DocumentsEveryday lifePlanning
A bigger toolkit. Still my judgment.
What I get from AI. What I want to carry forward.
A reflection in three parts01 Everyday · 02 Limits · 03 Learners
01My everyday
AI is woven into
the whole day.
I use it constantly. Being a team of one means the organizational back-and-forth, the emails, and the document changes all land with me. AI is part of how I handle that work.
The everyday, too.
Planning, shopping, travel planning, photo editing. My personal use is practical and ordinary: AI is another tool I reach for as I figure things out and make plans.
02The catch
When it misses,
I look at the ask.
Sometimes I’m disappointed. Often, I can trace it back to my prompting or to asking for too much at once. That makes using AI an ongoing practice in being clearer about what I need.
Was I clear enough?
Have I given it the context and direction the task needs?
Was I asking for too much?
Would breaking the problem into smaller steps help?
I still have to read it, question it, and make the call.
03For learners
More AI.
More need for literacy.
What I want to carry forward is the ability to think critically, understand what’s in front of you, and work through the problem. To me, AI makes those abilities even more important.
Read for meaning.
Understand what a response actually says. Have the literacy to ask a clear question and make sense of the answer.
Question what comes back.
Look at the reasoning. Notice assumptions. Ask whether the explanation makes sense and what would support it.
Work through the problem.
Stay involved in the steps. Try an approach, explain your thinking, and recognize where you need more help.
A small experiment · Try a different ask
Scripted prompt examplesWhat are we asking AI to do?
No live AI connection
Choose an approach
The prompt
“Solve this problem and give me the answer.”
What’s visible?A finished result. I still need a way to tell whether the learner understands how to get there.
The prompt
“Ask me what I’ve tried. Give me one hint at a time, and let me explain my next step.”
What’s visible?The learner’s approach, choices, and reasoning as they work through the problem.
The prompt
“Give me a similar problem. Let me try it, then ask me to explain why my approach works.”
What’s visible?An opportunity to see what the learner can apply and explain in their own words.
These examples illustrate a learning intention. A prompt can invite reasoning; seeing and checking that reasoning is still part of the work.
↗A question for the room
How do we make understanding as visible as the finished work?
I want learners to leave with a stronger ability to read, question, and work things out. That’s what I want to keep building toward.
Revisit the prompt experiment