Artificial Intelligence has rapidly entered teacher preparation programs. Preservice teachers are increasingly using generative AI tools to brainstorm lesson ideas, differentiate instruction, create assessments, draft parent communication, and generate instructional materials (Celik, Dindar, & Jarvela, 2022; Tunjera & Chigona, 2025). In many ways, this shift mirrors earlier waves of educational technology integration: the tools arrive quickly, enthusiasm grows rapidly, and educators work to determine how these technologies can meaningfully support teaching and learning within their individual environments (Cuban, 2018; Violanti, 2023).
But amid this enthusiasm surrounding AI, one issue deserves far more attention in teacher preparation programs: student privacy.
Many preservice teachers have little experience navigating the legal and ethical responsibilities surrounding student data. As a result, they may unknowingly enter personally identifiable information (PII) into public AI systems without understanding the implications. Teacher education programs therefore face an important challenge to not simply teach future educators how to use AI, but to help them understand when AI use becomes inappropriate, unethical, or potentially unlawful within K-12 settings.
The conversation surrounding AI in education cannot focus solely on efficiency or innovation, it must also center on professional responsibility.
The Problem Many Preservice Teachers Do Not Recognize
Today’s teacher candidates are often highly comfortable with digital tools. However, assumptions that they fully understand the implications of these tools are often unfounded
Consider a few common scenarios:
- A preservice teacher uploads a struggling student’s writing sample into an AI chatbot and asks for feedback suggestions.
- A candidate enters information about a student’s learning disability while seeking differentiation ideas.
- A student teacher copies behavioral concerns into an AI platform to generate a parent email draft.
- A candidate uploads assessment data to help AI create small-group interventions.
In each case, the preservice teacher may believe they are trying to help students. However, they may also be disclosing protected student information into systems that have not been approved by a school district or institution. Many teacher candidates simply do not realize this is problematic.
Why This Matters in K-12 Education
K-12 educators have long held ethical and legal responsibilities surrounding student records and confidentiality. AI tools do not eliminate those responsibilities.
In New York State, Education Law §2-d establishes protections surrounding the privacy and security of student data (The New York State Senate, 2024)
Student information should never be entered into AI systems without careful consideration of privacy, district policy, appropriate legislation, and platform protections.
The challenge is that many public AI tools are not designed specifically for K-12 educational compliance. Some platforms may store prompts, use uploaded information for system improvement, or lack safeguards expected in school environments. Even when names are removed, contextual details may still make students identifiable, particularly in small schools, communities, or specialized classroom environments.
Teacher education programs therefore face a significant task: helping future educators understand not only how to use AI, but when its use may become inappropriate, unethical, or even unlawful in K–12 settings.
Moving Beyond “How to Prompt”
Much of the current AI conversation in higher education focuses on prompting techniques and workflow strategies (Celik, Dindar, & Jarvela, 2022; Lucas, Bem-haja, Zhang, Llorente-Cejudo, & Palacios-Rodriguez, 2025). Those conversations are useful, but they are incomplete. Preservice teachers also need explicit instruction in ethical AI use. Teacher preparation programs should begin discussing questions such as:
- What information should never be entered into an AI system?
- What counts as personally identifiable information?
- How do district-approved AI tools differ from public platforms?
- When should AI not be used at all?
- How can educators protect student confidentiality while still benefiting from AI-supported instructional planning?
These conversations are increasingly essential components of professional preparation. If we teach preservice teachers only how to use AI tools, but not how to evaluate them responsibly, we risk normalizing unsafe instructional habits before candidates even enter the profession.
Practical Ways Teacher Educators Can Address Student Privacy
Fortunately, teacher educators do not need to redesign entire programs to begin addressing these concerns. Small instructional shifts can make a significant difference.
1. Model Safe AI Practices During Instruction
Faculty should consistently demonstrate responsible use when modeling AI tools within class. For example:
- Use fictional student examples instead of real cases.
- Remove identifying details from classroom scenarios.
- Demonstrate how to redact information before using AI tools.
- Explain why certain information should never be uploaded.
These small modeling decisions communicate professional expectations clearly and consistently.
2. Teach Candidates to Pause Before Uploading Information
One simple strategy is encouraging preservice teachers to ask themselves a short series of questions before uploading any materials to AI:
- Does this contain identifiable student information?
- Would I be comfortable sharing this publicly?
- Is this tool approved by a district or institution?
- Am I protecting student dignity and confidentiality?
- Could this information unintentionally identify a child?
Building this reflective pause into coursework helps future educators develop habits of professional judgment rather than impulsive use of technology.
3. Incorporate AI Ethics into Assignments and Discussions
Teacher educators can integrate ethical reflection into existing coursework without sacrificing instructional time. For example:
- Have students analyze AI-use scenarios for ethical concerns.
- Ask candidates to identify unsafe prompting practices.
- Include privacy considerations within lesson-planning assignments.
- Discuss how AI intersects with professional teaching standards.
These discussions help candidates recognize that technology integration is not simply technical but also grounded in pedagogy and ethics.
4. Emphasize District Policies and Approved Platforms
Many preservice teachers assume all AI tools function similarly. They often do not understand that districts may have approved platforms with specific privacy agreements in place, a practice mandated for example, by NY Education Law §2-d. Teacher education programs should therefore help candidates understand:
- The importance of district policies.
- Approved educational tools.
- Data protection expectations.
- Professional accountability during field placements.
This is especially important as school districts continue to develop their own AI guidelines and policies.
5. Frame Student Privacy as an Issue of Trust
Perhaps most importantly, teacher educators should frame student privacy as more than compliance. At its core, protecting student information is about trust. Families trust educators with sensitive academic, health, behavioral, and personal information. Students trust teachers to handle their experiences responsibly and professionally. AI does not diminish that obligation. Rather, the rapid growth of AI tools makes this relationship of trust and corresponding ethical decision-making even more critical. Future educators must understand that innovation should never come at the expense of student dignity or confidentiality.
Preparing Teachers for Responsible AI Integration
AI will almost certainly remain a growing part of K-12 education, and teacher preparation programs have a critical opportunity to shape how future educators approach those tools. The goal should not be fear or avoidance. AI can absolutely support creativity, differentiation, organization and reformative instructional design. But responsible integration requires more than technological fluency, it requires appropriate professional judgment.
As colleges and universities prepare future teachers for AI-enhanced classrooms, student privacy cannot become an afterthought. Long after candidates forget the perfect prompt structure on the latest AI platform, they must remember a far more important lesson: each technological decision educators make should protect the trust, dignity, and well-being of the students they serve.
Before joining higher education, he spent more than 31 years as a secondary social studies teacher, department chair, and instructional technology leader in New York State public schools. During that time, he led curriculum redesign initiatives, supported district technology adoption, and facilitated professional learning communities focused on instructional design and ethical technology use.
At Niagara University, Dr. Violanti teaches foundations and pedagogy courses and works to integrate AI literacy and student-centered technology practices into teacher preparation. His work focuses on helping instructors use emerging technologies in ways that strengthen relationships, deepen learning, and support all students.
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