Share reflection-based decisions

  • 5 minutes

Sharing reflection helps teams learn faster because patterns become visible across roles and contexts. It also supports consistent safeguards because educators can align on what changes they'll test and why. When you share your decision log and your planned adjustment, you give colleagues a model for what honest, specific reflection actually looks like in practice.

Role-based reflection questions

Choose the reflection questions based on your role

For teachers

Think about the adjustment you chose and how it'll show up in your classroom from the student's perspective.

  • How would you explain your adjustment to your students in a way that supports their learning and gives them some agency in the process?
  • What classroom routine could make the adjustment easier to sustain over several weeks, rather than only when you remember to do it?

For coaches

Think about a teacher or team you support and how the reflection loop might change how you talk about AI decisions together.

  • What question would you ask to help a teacher identify the smallest adjustment that is still worth testing?
  • How would you help a team learn from outcomes without letting the conversation turn into blame for the person or the tool?

For administrators

Think about how AI-supported decisions are currently explained and documented across your school or district.

  • What transparency language would you want staff to use consistently when describing AI-supported work to families or the school board?
  • What pattern across multiple educators' decision logs would signal that a school-level adjustment is needed, rather than an individual one?

Share your learning

A decision log that stays in your notebook improves your own practice. A decision log that enters a team conversation has the potential to improve how a whole group approaches AI use. Reflection and adjustment only get better when they happen in the open. When you share what you noticed and what you changed, you make it safe for others to do the same—and that's how schools build consistent, trustworthy AI practices over time.

Share with a colleague

Share your decision log and ask your colleague what outcome they would observe and what adjustment they would test. You don't need them to agree with your choices. You need their perspective on your reasoning. Two educators comparing decision logs and talking through what they noticed is exactly how team-level reflection norms get built.

Share on a professional or social platform

Share one insight about reflection and safeguards, without naming students or sensitive details. Something as simple as "I noticed X, so I adjusted Y" is a powerful model for educators who're still treating AI use as all-or-nothing. ISTE Connect, LinkedIn, Bluesky, and X are all places where this kind of honest, practical voice is needed right now.