Persona Matrix
These evidence-informed patterns help teams plan inclusive engagement. They are not demographic categories, diagnoses, or labels for individual people. A participant may reflect several patterns or none of them.
Persona Advisor
Describe the engagement situation—not a person—to receive a suggested starting approach. The result is a planning hypothesis that should be validated with participants.
Accessibility-Powered Achiever
Uses AI to reduce access barriers and manage demanding work, learning, or community responsibilities.
- Goals
- Work independently; process information efficiently; communicate clearly; preserve authentic voice.
- Concerns
- Robotic output, over-reliance, inaccessible source materials, data exposure, and erosion of critical-thinking skills.
- Trust grows through
- User control, editable outputs, privacy-protective tools, transparent limitations, and accessibility-centered demonstrations.
- Engagement approach
- Frame AI as optional assistive scaffolding. Show how to verify, revise, and disclose its use while preserving the participant’s own judgment and voice.
Evidence base: 2 summarized records across 2 source types.
Labor and Learning Guardian
Protects livelihoods, professional judgment, and the human relationships essential to learning.
- Goals
- Keep humans accountable; protect jobs and professional standards; establish enforceable safeguards.
- Concerns
- Job displacement, fabricated authority, automated evaluation, cognitive atrophy, surveillance, and decisions without recourse.
- Trust grows through
- Binding policies, worker and educator participation, auditability, narrow use cases, and clear prohibitions.
- Engagement approach
- Begin with listening rather than tool promotion. Document what will never be automated, define appeal and oversight paths, and invite labor representatives into governance.
Evidence base: 2 summarized records across 2 source types.
Community Builder
Sees AI as a practical amplifier for small organizations, entrepreneurs, and neighborhood problem-solving.
- Goals
- Launch ideas faster; reduce administrative burden; connect residents to resources; strengthen neighborhood capacity.
- Concerns
- Unequal access to paid tools, solutions imposed without local context, unreliable data, and benefits flowing away from residents.
- Trust grows through
- Community-defined problems, hands-on workshops, trusted local partners, shared tools, and visible follow-through.
- Engagement approach
- Use challenge-based learning around real local needs. Pair technical assistance with resource referrals and publish what was heard, what happened, and what changed.
Evidence base: 2 summarized records across 2 source types.
Responsible Systems Steward
Supports strategic AI adoption when privacy, transparency, equity, and human accountability are designed in.
- Goals
- Expand equitable access; modernize institutions; build trustworthy infrastructure; move from experimentation to deliberate use.
- Concerns
- Black-box models, weak governance, biased or low-quality data, environmental costs, unsafe deployment, and unverified outputs.
- Trust grows through
- Secure sanctioned platforms, documented data practices, independent review, measurable outcomes, and human-in-the-loop decisions.
- Engagement approach
- Provide implementation standards, evaluation criteria, and transparent reporting. Make uncertainty and limitations visible and never present participation data as population-wide truth.
Evidence base: 2 summarized records across 2 source types.