Bounded
Start with a real role and problem
Each offering defines the audience, use case, evidence boundary, desired outcome, and limits before content is built.
Research Area
Education and Training
Hii develops practical learning frameworks for clinicians, educators, leaders, families, and communities navigating AI-mediated change without surrendering human judgment.
Focus: shared language, role-specific practice, bounded claims, agency, safety, and usable decision frameworks.
Central Question
What does a person need to understand before AI becomes ordinary in their role?
People do not need the same training. A clinician, parent, educator, executive, and public official encounter different responsibilities, risks, and decisions. Education should be practical, evidence-aware, and specific to the human context.
Learning Design
Every program should help someone orient, decide, or act.
Relational
Include the human system
Training covers trust, dependency, continuity, identity, agency, and downstream effects rather than focusing only on prompts and productivity.
Practical
Give people language they can use
Participants leave with questions, frameworks, warning signs, examples, and decisions that fit their work or family context.
Accountable
Separate education from proof
Programs distinguish institutional positions, research findings, informed judgment, hypotheses, and open questions.
Current Work
The first public pathway begins with clinicians.
Founding pilot
When Clients Bring AI Into the Room
A bounded 60-minute clinician education session with neutral intake language, a three-lens assessment frame, proposed benefit and risk signals, and structured feedback.
View the pilotPublic literacy
Human-AI Relationship Literacy
A developing framework for helping people recognize trust, dependency, collaboration, emotional regulation, identity, continuity, rupture, and repair.
Explore public understandingBoundary
Education should not create false confidence.
Hii does not present emerging evidence as settled fact or turn one framework into a universal answer. Programs should make uncertainty and referral boundaries clearer, not hide them.