Executive-leader interviews
Surfaced governance choices, organizational capacity, risk ownership, and strategic priorities.
AI & Education ResearcherAll work ↗
Case study 02Applied district research
Designing responsible AI learning at scale.
How district-wide mixed-methods research became a cross-role architecture for governance, professional learning, and continuous implementation.
Explore the ecosystem ↓Responsible AI is not a single lesson. It is a system that helps every role make better decisions.
OrganizationChicago Public Schools
Timeframe2025–present
My roleGenAI Research Fellow · PI
ScopeCentral office + school-based roles
FocusGovernance · learning · equity
01 The systems challenge
The problem was not adoption alone
A district can publish an AI policy and still leave people unsure how to act.
Generative AI arrived as a classroom tool, a data-governance issue, a procurement decision, a professional-learning need, and an equity question—all at once. Each role encountered a different part of the system, while district coherence depended on those parts working together.
The design challenge was to move from broad principles to learnable practice: concrete decisions, shared routines, role-specific supports, and feedback structures that could evolve as the technology changed.
How might weTranslate evidence from many roles into a coherent AI learning system—without flattening their different needs?
02 Research at district scale
Listen before designing
Three evidence streams made invisible implementation needs visible.
Instead of treating stakeholders as one audience, the inquiry examined how responsibility, opportunity, and uncertainty looked from different positions in the system.
Survey responses analyzed
Identified patterns in use, readiness, concern, confidence, and support needs across the district.
Role-specific focus groups
Created space for participants to make sense of findings with peers in similar roles.
Focus-group participants
Added context, contradictions, examples, and design implications that a survey alone could not provide.
Gather signals
Survey, interview, and focus-group protocols built for different roles.
Compare perspectives
Look for shared needs, role-specific tensions, and contradictions—not only averages.
Translate findings
Convert themes into governance decisions, professional-learning priorities, and implementation supports.
Keep listening
Cross-role feedback structures make implementation a learning process rather than a one-time rollout.
A coherent architecture
Responsible practice depends on four conditions developing together.
Student agency
Learners understand, verify, disclose, and retain ownership of consequential thinking.
Human capacity
Adults get time, practice, examples, and communities—not only policy documents.
Infrastructure
Approved tools, data protections, procurement, and technical support make safe practice possible.
Governance
Clear responsibility, feedback loops, and revision processes keep implementation accountable.
Interactive system map
One district. Four learning realities.
Select each role to see how the same AI strategy becomes a different decision environment, learning need, and evidence signal.
Explore each role1/4 viewed
01School leader
How do we create consistent conditions without freezing local judgment?
Decisions this role makes
- Set expectations
- Allocate time and support
- Escalate risk
- Learn from implementation
Learning support
Implementation routines, decision guides, leader learning community, and clear escalation paths.
Evidence of readiness
Leaders can explain not only the rule, but how their school will learn and adapt.
03 Research translated into action
Protocols that hear different parts of the system.
Survey, interview, and role-specific focus-group designs created comparable evidence without erasing position and context.
Reports that help stakeholders see themselves—and each other.
Findings were translated into stakeholder-ready language, cross-role feedback structures, and an advisory model.
Professional-learning priorities grounded in real decisions.
Support needs were framed around the work people actually do, from instructional design to leadership and governance.
Recommendations that connect values to implementation.
Decision-ready guidance addressed organizational capacity, accountability, equity, and the conditions required for responsible adoption.
04 Evidence & boundaries
What the work demonstrates
Strong learning strategy begins with institutional listening.
The work produced a 50,000-word policy and implementation synthesis, governance recommendations, professional-learning priorities, implementation strategy, and cross-role feedback structures.
Evidence boundary
This is evidence of system design—not proof of learner outcomes.
The research documents stakeholder perspectives, institutional architecture, and implementation needs. It does not claim that a completed curriculum rollout caused changes in student learning or educator practice.
Documented
Research reach, protocols, synthesis, translated artifacts, and governance design.
Next evidence layer
Use in practice, quality of role-based learning, implementation variation, and learner-level outcomes.
What this project demonstrates
At scale, learning design is the architecture that connects evidence, decisions, support, and accountability.
Mixed-methods researchLearning ecosystemsAdult learningImplementation strategyAI governance