Artificial Intelligence and Student Privacy: Building Trust ThroughResponsible Design

Artificial intelligence is reshaping education in profound ways: GenAI tools expand the palette of rich content aligned to standards. Teachers use conversational AI tools to plan lessons and identify nuances in student performance over time. Adaptive assessment systems analyze speech patterns—specifically phonemes and prosody—to provide educators with unprecedented insight into reading fluency and comprehension. Computer Vision technology allows paper assignments to become digital information. But as this technology becomes more sophisticated, our responsibility to protect student privacy becomes even more critical.

For those of us building and implementing learning systems, our mission is grounded in transparency, accountability, and an unwavering commitment to student safety and privacy.

Three Privacy Priorities for Schools Adopting AI

We’re in the early adoption phase of AI in education, a period marked by enthusiasm, experimentation, and rapid change. Our conversations with educators reveal genuine eagerness to integrate these tools into instruction. But alongside this interest, schools and districts are focused on keeping privacy safeguards front and center. This is a shared responsibility between schools and educational technology companies—the pace of innovation cannot outrun our obligation to protect students.

Here’s what matters most:

1. Licensing agreements shape data privacy

With off-the-shelf platform AI tools, the terms governing data use, retention, and model training vary significantly depending on which license a school purchases.

Schools must scrutinize vendor contracts to understand exactly what happens to student information, not just during active use, but also after a session ends or when a subscription lapses.

2. Configuration controls are critical

Many AI tools offer privacy-enhanced modes that schools should enable by default. Session-based configurations can delete data immediately after use, ensuring that sensitive information doesn’t persist on shared devices or in account histories. This feature is especially important in schools where students share computers, multiple educators access the same institutional license, or students use personal devices for schoolwork. The rapid pace of AI development means best practices are still emerging. Educators, technology coordinators, and district leaders must approach adoption thoughtfully, asking hard questions about data flows, retention policies, and access controls.

3. Vendor accountability must be contractual, not assumed, and regular audits are essential

Agreements should explicitly specify how data is de-identified, when it can be shared with third parties, and guarantee permanent deletion when relationships end. Providers must demonstrate compliance with federal privacy laws, such as FERPA and COPPA, as well as state-specific privacy regulations that often exceed federal requirements.

Beyond legal compliance, vendors should demonstrate evidence of robust technical safeguards, including encryption in transit and at rest, role-based access controls, regular third-party security audits, and incident response procedures. These expectations should align with established guidelines such as the NIST AI Risk Management Framework.

4. EdTech Vendors have a unique responsibility

Creating technology for learners requires commitments that protect students and strengthen trust. These two principles offer a blueprint for building safe, ethical, and high-quality learning tools. For those of us who are building our own AI tools or integrating AI platforms into our own applications, we have additional responsibilities:

Data minimization  
Collect only the information necessary to serve a clearly defined learning purpose – one that helps the teacher teach more effectively or drives improved student outcomes. Every new feature should begin with the question: What is the educational value of this data, and how do we protect it? Responsible systems link each data point directly to student benefit.

Privacy-by-design  
Privacy protections must be embedded throughout the entire software development lifecycle, from the first line of code to deployment and maintenance. Real trust comes from systems that respect student rights at the architectural level, making privacy violations technically difficult, not just contractually prohibited.

Moving Forward Together

Like previous technological shifts – from the widespread adoption of the Internet to the advent of tablet devices – AI holds tremendous potential to help teachers teach and improve student experiences and outcomes. The excitement around AI’s potential is real, and so are the concerns. AI holds immense promise for personalizing learning, enhancing assessment accuracy, and enabling educators to understand students more deeply. But these gains depend on building systems rooted in sound ethical and privacy practices.