Traditional educational software delivers content and records structured inputs. A reading program presents a passage and records whether the student answered comprehension questions correctly. A math platform tracks which problems were attempted, which were right, and how long each took. The software follows defined rules: if the student scores above a threshold, advance; if below, repeat.
The data these tools collect is predictable and bounded. You know what it is, where it goes, and what it is used for. The tool does not change its own behavior based on what it learns from aggregated student interactions. Its logic was set at the time it was built.
Student privacy risks still exist with traditional edtech. In 2023, the FTC obtained a $6 million order against Edmodo, a traditional classroom platform, for collecting children's data without proper parental consent and using it to target students with advertising, which is a serious violation of COPPA.1 But the risk profile of traditional edtech is one that most districts have frameworks to manage.
AI-powered edtech collects the same structured inputs as traditional software — and then goes further. It also collects behavioral signals: how a student reads through content, where they pause, what they skip, how they phrase open-ended responses, which paths through material they take and abandon. From these signals, the system builds inferences: this student struggles with inferential questions, this one disengages when content is text-heavy, this one accelerates when given choice.
Those inferences then drive the tool's behavior. The AI adapts pacing, selects content, and in some systems surfaces recommendations to teachers about which students may need intervention. This is the core promise of adaptive AI edtech, and it is a genuine improvement over one-size-fits-all delivery.
But it introduces three questions that traditional edtech evaluation frameworks were not designed to answer.
The first is the model training question. The Future of Privacy Forum's guidance on vetting AI tools in schools identifies this as the critical issue that most district reviews miss: will student data be used to train or improve the underlying AI model? Some vendors explicitly prohibit this. Others are ambiguous. If a student's written responses, behavioral patterns, or reading history are fed back into the model, that data may persist in the system indefinitely and could surface in outputs for other users. A traditional edtech privacy review does not ask this question because traditional tools do not have underlying models to train.
The second is the transparency and explainability question. When an AI system recommends that a student be assigned to an intervention pathway, or flags a student as at risk of falling behind, who can explain how that recommendation was made? Traditional software follows explicit rules that a teacher or administrator can trace. AI systems often work through statistical patterns that even the vendor may not be able to articulate fully. The FPF guidance notes that some proposed AI use cases involve substantive decision-making affecting students' educational trajectories, and that these warrant particular scrutiny, in some cases requiring human oversight or parental consent before the AI recommendation carries weight.2
The third is the equity question. AI systems learn from data, and the data they learn from reflects the students and contexts in which they were trained. A tool trained predominantly on data from well-resourced suburban classrooms may perform differently, and potentially less accurately, for students in other contexts. The American Association of School Administrators identifies this as one of the primary concerns districts should raise with AI vendors: what datasets were used to train the AI, and was the system tested for performance across different student subpopulations?3
None of this is a reason to default to caution or avoid AI-powered tools. Many of them offer meaningful benefits that traditional edtech cannot: genuinely personalized pacing, early identification of students who need support, reduced administrative work for teachers. The point is that a traditional app vetting checklist is not sufficient to evaluate them.
Three questions belong in any AI edtech review that are not standard in traditional edtech review. Does student interaction data train the underlying model, and if so, how is it de-identified and who has access? How does the system make its recommendations, and can it explain them to teachers and parents in plain terms and how was the AI trained and tested? More specifically, was it evaluated for accuracy and fairness across different student populations, including those who look like your students?
These are not questions a vendor should struggle to answer. If they do, that itself is useful information.
