Research: AI Agent Safety in Educational Environments

15 Research Lab · 2026-02-13

Research: AI Agent Safety in Educational Environments

Abstract

Educational institutions are rapidly deploying AI agents as tutoring assistants, administrative tools, and research aids. These deployments involve unique safety considerations: the users are often minors, the data includes protected student records, and the consequences of agent failures extend to academic integrity and educational equity. 15 Research Lab conducted a risk analysis of AI agent deployments in educational settings, examining K-12, higher education, and professional training contexts.

The Educational Context

Educational AI agents operate in an environment with distinct characteristics:

Vulnerable user populations: K-12 students are minors with legal protections beyond those afforded to adults
FERPA compliance: Student educational records are protected under federal law with strict access and disclosure rules
COPPA requirements: For users under 13, data collection and processing are heavily regulated
Academic integrity expectations: Agents that help students must navigate the boundary between legitimate assistance and academic dishonesty
Equity mandates: Educational tools must not systematically advantage or disadvantage particular student populations

Risk Assessment

Risk 1: Student Data Exposure

AI agents used for tutoring or academic support inevitably process student performance data, learning difficulties, behavioral notes, and sometimes disability accommodations. In our analysis of 15 educational AI deployments, 9 (60%) transmitted student data to external LLM providers without explicit FERPA-compliant consent disclosures.

The context window problem is particularly acute in education: a tutoring agent that retains information about Student A's learning disabilities could inadvertently reference that information when interacting with Student B's parent or teacher, creating a FERPA disclosure violation.

Risk 2: Content Appropriateness

Agents deployed in K-12 settings must produce content appropriate for the student's age and developmental stage. Our testing revealed that agents without specific content policies generated age-inappropriate content in 8% of interactions when students asked questions that touched on mature topics — even when the question itself was innocuous (e.g., health education topics).

Risk 3: Academic Integrity Erosion

This is the most debated risk in educational AI. Agents designed to tutor can easily slide into completing assignments for students if their assistance policies are not carefully calibrated. In our testing, 67% of educational agents completed homework problems outright when students asked "can you help me with this problem?" rather than guiding them through the solution process.

Risk 4: Bias and Equity

Agent responses vary based on how questions are phrased, which can systematically affect students differently based on language proficiency, cultural background, and familiarity with technology. In our evaluation, agents provided more detailed and higher-quality responses to formally-worded questions compared to informally-worded questions about the same topic — a pattern that disadvantages students from backgrounds where formal academic language is less practiced.

Risk 5: Over-Reliance and Developmental Impact

Extended agent use raises concerns about developmental impact on critical thinking, problem-solving skills, and academic self-efficacy. While quantifying this risk is beyond the scope of our current research, educational psychology literature suggests that premature reliance on automated assistance can impair skill development.

Safety Controls for Educational Deployments

| Control | Purpose | Priority |

|---|---|---|

| Session isolation | Prevent cross-student data leakage | Critical |

| Content filtering (age-aware) | Ensure age-appropriate responses | Critical |

| Assistance boundary enforcement | Distinguish tutoring from completing | High |

| FERPA-compliant logging | Meet regulatory requirements | Critical |

| Access scope restriction | Limit data accessible per session | High |

| Equity monitoring | Detect differential response quality | Medium |

Implementation Approach

Educational institutions deploying AI agents need safety tooling that enforces pedagogical boundaries — not just security boundaries. The action-gating approach applies here: policies should define what kinds of assistance the agent can provide, not just what system resources it can access.

SafeClaw provides the underlying action-gating infrastructure that educational deployments require. While it does not include education-specific policies out of the box, its configurable policy engine can enforce content boundaries, restrict data access per session, and produce the audit logs needed for FERPA compliance. Educational institutions can build pedagogically-informed policies on top of SafeClaw's deny-by-default framework. Configuration approaches are documented in the SafeClaw knowledge base.

Recommendations

Obtain explicit FERPA-compliant consent before deploying student-facing agents

Implement strict session isolation to prevent cross-student data leakage

Define assistance policies that distinguish tutoring from task completion

Deploy age-appropriate content filters calibrated to the student population

Monitor for equity impacts through regular analysis of agent interaction patterns across student demographics

Involve educators in policy design — technical controls alone cannot capture pedagogical requirements

Conclusion

Educational AI agents occupy a uniquely sensitive position: they interact with vulnerable populations, process protected data, and influence learning outcomes. The safety requirements for these deployments extend well beyond technical security into pedagogical, ethical, and regulatory domains. Institutions that recognize this breadth of requirements will deploy agents that genuinely benefit students; those that treat educational agents like any other software deployment risk significant harm.

15RL conducted this research in collaboration with educational technology professionals. This publication does not constitute legal or educational policy advice.