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:
- Define acceptable use clearly
- Focus on process, not just product
- Teach effective AI collaboration
- Assess understanding, not just output
The Balanced Approach
Risk doesn't mean avoidance. It means thoughtful adoption with appropriate safeguards.
For every AI use case, ask:
- What could go wrong?
- What's the worst-case impact?
- What verification steps are needed?
- Who needs to review the output?
- What's our fallback if AI fails?
The goal is confident, responsible use, not fearful avoidance or reckless adoption.