The University of Baltimore

Persona Matrix and Advisor

Journey C · Understand and engage

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.

Evidence boundary: The matrix summarizes prior interviews, focus groups, and surveys. It does not expose raw interview notes or private community submissions.
needs guidance

Practical Navigator

Wants useful help with everyday tasks but needs a clear, trusted starting point.

Goals
Solve a concrete problem; understand which tool fits the task; gain confidence without needing technical expertise.
Concerns
Misinformation, scams, privacy loss, confusing choices, and being left behind by rapid change.
Trust grows through
Plain language, local examples, visible human support, and delivery through familiar community organizations.
Engagement approach
Lead with one practical outcome. Demonstrate the tool in a low-risk setting, explain what data not to share, and provide a human follow-up path.

Evidence base: 2 summarized records across 2 source types; reported sample-size entries sum to 35 and may overlap.

active adopter

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; reported sample-size entries sum to 1 and may overlap.

protective skeptic

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; reported sample-size entries sum to 31 and may overlap.

opportunity focused

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; reported sample-size entries sum to 1 and may overlap.

critical adopter

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; reported sample-size entries sum to 8 and may overlap.

Planning aid

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.