A Responsible AI Checklist for Special Education Teachers
Before AI output touches anything a student experiences, run this short checklist. Six questions that keep AI use responsible.
The six questions
- Privacy: is student information inside an authenticated, education-grade system — and nowhere else?
- Review: will a qualified educator read and approve this output before it affects a student?
- Boundaries: is this a draft, summary, or suggestion — not a diagnosis, eligibility call, or consequence?
- Context: does the AI know this student's actual profile, or is it guessing from a generic prompt?
- Data: does the output match what my own data and observations say?
- Reversibility: if this suggestion is wrong, can I catch it and adjust quickly?
Green-light uses
- Drafting parent updates you edit before sending
- Summarizing your own logged data into trends
- Generating differentiated versions of materials you review
- Brainstorming strategy options to consider
Stop-and-think uses
- Anything feeding into an IEP team decision — drafts only, team review always
- Anything describing a behavior incident — verify the record matches what happened
- Anything a student or family will read — reread it in their voice
The habit that matters most
Responsible AI use isn't a policy document — it's a ten-second habit. Before approving any AI output, ask: would I be comfortable if my name were the only name on this? If yes, approve. If no, edit it until it is. Tools like Spark are built for exactly that rhythm: AI proposes, you dispose.
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