Education technology companies are facing a genuinely active wave of student data privacy litigation this year, with several cases working through the courts simultaneously and new filings as recent as late August. One prominent case involves a major K-12 assessment and instructional platform, where plaintiffs allege the company gained extensive access to sensitive student information, including demographic and disability status, and shared it broadly with third parties beyond what families reasonably expected when the product was adopted by their school. The company involved has denied the allegations and stated it doesn't sell student data or use it for advertising purposes.

A separate case, filed in late August against a school district and its leadership, alleges violations of parents' constitutional rights specifically related to decisions about their children's education and privacy, illustrating that this litigation risk extends beyond just the vendors themselves to the districts that adopt their products. Districts in several states, reportedly including large systems on both coasts, have already begun scaling back or reconsidering specific edtech products in apparent response to this growing wave of parent and legal advocacy scrutiny.

For districts and departments evaluating any AI-assisted tool this year, including grading tools specifically, this litigation wave offers concrete, current lessons worth taking seriously, not abstract compliance theory but real cases illustrating exactly what kind of data practices are currently drawing legal challenge.

What these cases have in common

Across the active litigation this year, a consistent pattern emerges in the specific allegations: concerns about the breadth of data collected relative to what's genuinely necessary for the product's educational function, questions about third-party data sharing that wasn't clearly disclosed or consented to, and a broader theme of transparency gaps between what a product actually does with student data and what families and even school officials reasonably understood when adopting it. None of these are exotic or hard-to-anticipate concerns; they're the same fundamentals any careful data privacy evaluation should already be checking.

• Request explicit, written confirmation of exactly what data any AI grading tool collects and why each category is genuinely necessary
• Ask directly whether student data is shared with third parties, and if so, under what specific terms and consent process
• Confirm in writing whether a vendor uses student submissions to train or improve models beyond your own district's use
• Check whether a vendor's practices have been named in any current litigation or regulatory action before adopting their product
• Treat vendor transparency and responsiveness to these specific questions as a meaningful signal in your evaluation process

Why this matters even for products with strong stated privacy commitments

Notably, the companies facing current litigation have generally issued strong public statements affirming their commitment to student data privacy and denying the specific allegations against them, which is a useful reminder that a vendor's stated privacy commitments alone aren't sufficient for a district's own due diligence. Districts evaluating any AI grading tool benefit from requesting specific, written contractual commitments, not just marketing language, and from independently verifying claims where possible rather than relying entirely on a vendor's own public statements.

This is particularly relevant for AI-assisted grading tools specifically, since these products by definition process substantial student writing, which can include personally revealing content well beyond a typical assessment score, making the stakes of any data practice gap genuinely higher than for some other categories of edtech product.

What responsible procurement looks like this year

Given the active litigation landscape, districts have real reason to treat AI grading tool procurement with the same rigor currently being applied, sometimes after the fact through litigation, to other categories of edtech. Asking the specific, pointed questions this year's cases have surfaced, before a contract is signed rather than after a problem emerges, is a considerably less costly and less disruptive way to arrive at the same due diligence these lawsuits are now forcing retroactively elsewhere.