AI can help coalitions read and respond to the data faster. It should never decide what matters most for your community.
Bottom line
AI supports interpretation. Humans set priorities, hold relationships, and are accountable for what gets published, funded, and enacted. Use the guidance and checklist below before any AI-assisted output goes into a public report, grant application, or policy conversation.
What AI can help with — and what humans must decide
What AI can help with
Summarizing long report sections into plain language.
Drafting discussion questions, agendas, and starter messaging.
Surfacing patterns across many indicators a human might miss at first read.
Translating dense statistics into formats for different audiences.
Generating first drafts of grants, plans, and briefs to edit.
What humans must decide
Which issues become local priorities.
How funding and staff time are allocated.
Whether a finding is meaningful given community context.
What is said publicly on behalf of the coalition.
How youth, families, and impacted communities are engaged.
Privacy and sensitive-data cautions
Privacy cautions
Never paste student-identifying information into any AI tool — including names, school IDs, IEP status, free-and-reduced-lunch indicators, or any combination that could re-identify a student.
Treat small-cell counts as identifying. If a subgroup has fewer than ~10 students, do not share counts externally even if AI-generated text references them.
Review enterprise data agreements before pasting any non-public dataset into a consumer AI tool.
Sensitive-data cautions
Mental-health and suicide content: confirm crisis resources (988, local) are accurate and current before publishing anything an AI drafted.
Substance use: avoid stigmatizing framing (e.g., 'addicts') even if an AI tool suggests it.
Equity and identity: do not let AI infer or assign racial, gender, or socioeconomic explanations the data does not support.
Bullying and violence: do not name schools or individuals from AI summaries without independent verification.
Equity review questions
Ask these of any AI-generated finding before it leaves your coalition.
Five questions to ask
Whose experience is centered — and whose is missing — in the data the AI is summarizing?
Could the language the AI generated reinforce stereotypes about any community or group?
Does the framing focus on deficits or also recognize community strengths?
Were the most-impacted communities involved in deciding what the AI was asked to do?
If we acted on this AI-generated recommendation, who would benefit most — and who could be harmed?
Pre-publication review checklist
Use before AI-assisted content goes into a public report, grant, policy brief, or board presentation.
Review checklist
This checklist is a working aid, not a legal compliance document. Adapt it to your coalition's data-sharing agreements and any IRB or district approvals.
A note on tooling
Tools and models change quickly. The principles on this page — human accountability, privacy, equity, verifiability — are durable even as specific AI systems evolve. Revisit your coalition's AI-use guidance at least annually.