When you hear “generative AI”, you probably think about getting things done for you. And generally that’s what we use computers for: getting things done faster or more accurately. But I argue there’s another valuable use of computing that you should consider: reflecting on your own work.
I’ve written about some concrete examples in this series:
Other things we could be reflecting on:
- in teaching:
- for a specific assignment: what will likely confuse students? what clarifying questions will they have? where might they get stuck?
- for a unit: how do my materials line up with the learning objectives? what learning objectives will students connect with most or least?
- in research:
- what about this paper will confuse reviewers?
- what part of my experiment design makes least sense? is most likely to fail?
- what assumptions am I making that I haven’t written down?
- in presentations
- what will the audience be most interested in? what will they be most confused by?
- is anything I’m saying likely to be misinterpreted or to offend someone?
(many more are possible; just ask an AI to continue this list!)
We were made to be makers–not just of things or text, but of ideas, questions, hypotheses, observations.
Ultimately, it’s not about getting the right answers faster. It’s about asking better questions, the sort of questions that will help us act virtuously.
Let’s use computing power to help us think better.
Related: AI Should Challenge, Not Obey