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AI Risks & Concerns

Understanding and mitigating AI risks in education

AI is powerful, but it's not perfect. Understanding the risks helps you use it responsibly and maintain the trust of your community.

Hallucinations

The problem: AI can confidently state things that are completely false. It doesn't "know" when it's wrong.

Why it happens

AI generates text that is statistically likely to follow from your prompt, not necessarily true. It has no concept of truth, only patterns.

Education examples

  • Citing non-existent research studies
  • Attributing quotes to the wrong person
  • Inventing statistics that sound plausible
  • Creating fictional legal precedents

Mitigation

  • Always verify facts, citations, and statistics independently
  • Use AI for drafting and ideation, not as a source of truth
  • Ask AI to include sources, then check those sources exist
  • Be especially careful with numbers and dates

Bias

The problem: AI reflects biases present in its training data, which includes historical inequities and societal biases.

Manifestations

  • Stereotyping in generated content
  • Uneven quality of responses about different groups
  • Reinforcing historical inequities
  • Lack of diverse perspectives

Education implications

  • IEP language that inadvertently stereotypes
  • Curriculum suggestions that lack cultural diversity
  • Assessment questions with embedded bias
  • Communications that don't resonate with all families

Mitigation

  • Review AI outputs critically for bias
  • Provide diverse examples in your prompts
  • Include explicit instructions about inclusive language
  • Have diverse reviewers check important content

Privacy & Data Security

The problem: Information you share with AI may be stored, used for training, or potentially exposed.

Concerns

  • Student personally identifiable information (PII)
  • FERPA compliance
  • Staff personnel information
  • Sensitive district communications

Best practices

  • Never input student PII into general AI tools
  • Use enterprise versions with data protection agreements
  • Anonymize data before analysis
  • Understand your vendor's data retention policies
  • Follow your district's acceptable use policy

Over-reliance

The problem: AI can make us lazy thinkers if we stop engaging critically with its outputs.

Warning signs

  • Accepting first drafts without review
  • Stopping your own research process
  • Deferring to AI on judgment calls
  • Losing skills through disuse

Maintaining balance

  • Use AI as a starting point, not an endpoint
  • Continue developing your own expertise
  • Question AI recommendations
  • Maintain human decision-making authority

Academic Integrity

The problem: The line between AI assistance and AI replacement is unclear.

Considerations

  • When does AI help cross into AI doing the work?
  • How should policies address AI use?
  • What are the learning implications?
  • How do we prepare students for an AI-assisted world?

A framework

Instead of banning AI, consider:

  1. Define acceptable use clearly
  2. Focus on process, not just product
  3. Teach effective AI collaboration
  4. Assess understanding, not just output

AI Governance Framework

Effective AI governance isn't about control. It's about enabling responsible innovation while protecting your community.

Governance Principles

1. Transparency

  • Be open about where and how AI is used
  • Communicate clearly with stakeholders
  • Document AI-assisted decision-making

2. Accountability

  • Human oversight on all consequential decisions
  • Clear ownership of AI outputs
  • Defined escalation paths

3. Equity

  • Audit for bias regularly
  • Ensure equitable access to AI benefits
  • Monitor for disparate impacts

4. Privacy

  • Data minimization principles
  • Clear data handling procedures
  • Vendor due diligence

Policy Components

Your AI policy should address:

| Area | Key Questions | | ---------------------- | ------------------------------------------------------- | | Acceptable Use | What AI tools are approved? What data can be used? | | Student Data | How is FERPA compliance maintained? What is prohibited? | | Staff Use | What training is required? What disclosure is needed? | | Academic Integrity | How should students use AI? How do we assess fairly? | | Procurement | What vendor requirements exist? Who approves new tools? | | Incident Response | What happens when something goes wrong? |

Implementation Roadmap

Phase 1: Foundation (Months 1-3)

  • Form AI governance committee
  • Audit current AI tool usage
  • Develop draft acceptable use policy
  • Identify training needs

Phase 2: Pilot (Months 4-6)

  • Select pilot use cases
  • Train pilot participants
  • Establish feedback mechanisms
  • Refine policy based on learnings

Phase 3: Scale (Months 7-12)

  • Roll out approved tools broadly
  • Implement ongoing training program
  • Establish regular policy review cycle
  • Share learnings with community

Board-Level Considerations

Superintendents should prepare boards for:

  • Budget implications: AI tools have costs, but also ROI potential
  • Liability questions: Who is responsible for AI errors?
  • Community concerns: Parents will have questions
  • Competitive positioning: How are peer districts approaching AI?
  • Staff impact: How will roles evolve?

Red Lines

Some things should never involve AI without explicit human review:

  • Special education placement decisions
  • Disciplinary actions
  • Hiring/firing recommendations
  • Budget allocations affecting students
  • Communications to families about sensitive matters
  • Legal or compliance documentation

The Balanced Approach

Risk doesn't mean avoidance. It means thoughtful adoption with appropriate safeguards.

For every AI use case, ask:

  1. What could go wrong?
  2. What's the worst-case impact?
  3. What verification steps are needed?
  4. Who needs to review the output?
  5. What's our fallback if AI fails?

The goal is confident, responsible use, not fearful avoidance or reckless adoption.

Resources for Leaders

Sample Policies

Further Reading