Data Minimization. Only collect and use data that you need to analyze. Limit disclosure of personally identifiable information (PII). Only use enough information to get the job done in alignment with FERPA.
Apply De-Identification Techniques. De-identify PII wherever you can. Make sure individual students are not identifiable. Remove direct identifiers and aggregate data without compromising your efforts for analysis. Err on the side of data privacy.
Ensure Policy Compliance. Work with your school organization to put data governance policies in place. Align these policies with FERPA and the TASB policies already in effect. If working with a specific AI solution provider, require written agreements for safeguarding data.
Form small groups (2-3 people)
Each group selects 2-3 current vendor solutions to evaluate
Groups review vendor privacy policies and terms of service
Apply the PROTECT rubric to each selected vendor
Score vendors on each rubric criteria (out of 14 total points)
Groups share their findings, focusing on:
Highest and lowest scoring vendors
Common privacy concerns across vendors
Surprising discoveries or areas needing clarification
Identify vendors that may need further review or contract adjustments
Discuss potential policy changes to enhance student data privacy
Find a list of solution providers with privacy policies and/or terms of service on this website. You can, of course, go to a vendor's website on your own and find their privacy policy and/or terms of service on your own.