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Wait Time on the Record
Coaches code a consented, timestamped classroom clip for questioning and wait time, test an AI summary against the timestamps, and write feedback a teacher can act on tomorrow.
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Never Paste This
A fast sort and a de-identification drill so data teams know exactly what student information must never go into an AI tool, and how to ask the question they need without it.
Overview
Data teams are the place where the temptation is strongest: a spreadsheet of scores, a chatbot that can "find the patterns," and fifteen minutes left in the meeting. This short opener, sized for the start of a PLC or data meeting, has teams sort realistic data items into three categories, then rewrite a set of raw data-team notes so the instructional question survives and the students become unrecognizable. Teams leave with a one-page "before you paste" check they can tape to the wall.
Objectives
- Participants will classify types of student data by whether they may go into an AI tool, and under what conditions.
- Participants will de-identify a set of notes by removing both direct identifiers and indirect details that could single out a student.
- Participants will rewrite a data question so an AI tool can help with instruction without receiving student information.
Materials
On paper
- Handout A: What Goes In? Sort (1 set per pair, cut apart, with a three-column mat)
- Handout B: Raw Data-Team Notes (1 per pair)
- Handout C: Before You Paste Checklist (1 per person, plus one enlarged for the wall)
- Red pens or markers
On screen
- Optional: your district's approved AI tool list and data privacy guidance, projected
- Optional: a facilitator-run demo in an approved chatbot using only the de-identified version
Before you start
- Cut Handout A into strips and clip one set per pair with a mat that shows the three headings.
- Find your district's list of approved AI tools and its guidance on student data. The facilitator key reflects common practice; your district's policy wins wherever they differ.
- Print one copy of Handout C large enough to post in the data room.
Step by step
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10–3 min
Hook
The fifteen-minute temptation
Say: "It's the end of a data meeting. Someone says, 'Let's just drop the spreadsheet into the chatbot and ask which kids need intervention.' What's your first reaction?" Take two quick answers. Then: "Most of us know the answer is 'not like that.' Today we get specific about what's never okay, what's okay with conditions, and how to still get the help."
Facilitator noteDon't shame anyone who has done this. Many tools make it easy, and many districts' guidance is new.
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23–11 min
Practice
Sort: what goes in?
Pairs sort the Handout A strips into Never into an AI tool, Only de-identified, in a district-approved tool, and Fine in an approved tool. Each pair must agree before a strip is placed. Any strip they argue about goes face down in the middle for the debrief.
Facilitator noteThe strips that cause the most debate are usually the "anonymous" ones: a class list with only first names, or a description of "the only student who…" Those are exactly where re-identification happens.
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311–15 min
Debrief
Check the key, argue the middle
Read the key for the face-down strips first. Name the principle: a student can be identified by a combination of details, not just a name. Grade, campus, program, and one unusual detail is often enough. Then show your district's approved tool list and point out any differences from the key.
Facilitator noteMention in passing that federal student privacy law (FERPA) and state law apply whether or not a tool uses AI. The AI part mostly raises the stakes: data may be stored, reviewed, or used for training.
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415–22 min
Practice
De-identify the notes
Pairs take Handout B and use red pens to strike every direct identifier (names, ID numbers, birthdates) and every indirect identifier (unique family details, rare programs in small groups, "the only one who…"). Then they rewrite the team's actual question at the bottom so it asks for instructional help, such as "Suggest three small-group activities for 3rd graders who decode single-syllable words but struggle with multisyllabic words," with no student data at all.
Facilitator noteLook-for: pairs realize the question rarely needs the student data. The AI is useful for strategies; the team is the one that knows the students.
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522–25 min
Transfer
Tape it to the wall
Post the enlarged Handout C in the data room. Ask each person to read it once and circle the item they are most likely to forget under time pressure. Close: "Next data meeting, whoever touches the keyboard reads this aloud first."
Facilitator noteCoaches: this checklist also makes a good opening for a one-on-one with a teacher who is excited about AI and data.
Paper or screen
Unplugged
The activity is designed as a paper activity: the sort, the red-pen de-identification, and the wall checklist need no devices. It fits in the opening 25 minutes of a data meeting.
Digital
Run the sort as a three-column drag-and-drop board and the de-identification in a shared document with suggesting mode, so edits are visible. If you demo AI, the facilitator uses only the rewritten, data-free question in a district-approved chatbot on the projector and shows that the strategies it returns are just as useful without any student information.
Does it need a screen? The optional demo proves the most persuasive point: the tool gives the same instructional help without student data. Everything else is better on paper, where red-pen strikes make the invisible identifiers visible.
Evidence of learning
What you should be able to see or collect if it worked.
- Pairs place the "only student who…" and small-group strips in the Never or de-identified columns, not in Fine.
- Red-pen edits on Handout B remove indirect identifiers as well as names and ID numbers.
- Rewritten questions ask for instructional strategies and contain no student information.
- Participants can name the district's approved AI tools, or name who to ask.
Adaptations
- Campus leaders
- Add strips about staff data (evaluation notes, medical leave) and discuss who approves AI tools for administrative use.
- Special education teams
- Add strips from IEP and 504 paperwork and practice rewriting accommodation questions without disability details.
- Higher Ed
- Swap in course roster, grade, and accommodation-letter examples, and name your institution's data classification levels as the categories.
Standards connections
Data teams de-identify student notes before any AI use, modeling the personal-information protection students learn in digital citizenship.
- TEKS
- privacy, safety, and security (c)(10)TEKS sections: Technology Applications §126.5–§126.7, §126.8–§126.10, §126.17–§126.19, high school Technology Applications courses (19 TAC Chapter 126)
Reflect
- How am I using data to inform and guide my coaching practices? (Coach ELE guiding question)
- In our data meetings, where is student information most likely to leave the room without anyone noticing?
- What instructional question could I ask an AI tool this week that needs no student data at all?
Take it to your students
At your next data meeting, read Handout C aloud before anyone opens an AI tool, and bring one rewritten, data-free question to test. Teach the same idea to students with the question: "Would you want this detail about you pasted somewhere you can't see?"
Pairs well with
- Generative AI Adoption ChecklistFor leaders deciding which AI tools are approved for data work.
- CYBERSMART Ready: Educator Cybersecurity TabletopA follow-up on protecting student data more broadly.
