Research

Studying the human decisions that shape AI outcomes.

Our research connects behavioral insight, sustainability, AI literacy, governance, and practical adoption.

Research roadmap

Current

Green AI

Testing how human interaction can reduce unnecessary computation.

In development

Decision Point Framework

A practical model for deciding when AI should assist and when human judgment should lead.

Planned

Human–AI Decision Making

Behavior, trust, learning, accountability, and responsible adoption.

01

Initial research initiative

Green AI

Green AI explores whether more intentional human interaction can reduce unnecessary computation while maintaining—or improving—the usefulness of AI outputs.

  • Prompt clarity and unnecessary repetition
  • Resource-aware AI habits
  • Quality, trust, and sustainability trade-offs
  • Practical guidance for individuals and organizations
02

Framework in development

The Decision Point Framework

A practical model for examining what a person is trying to achieve, what role AI should play, what judgment remains human, and how the quality and cost of the outcome can improve.

IntentRole of AIHuman judgmentOutcomeCostLearning
03

Broader agenda

Human–AI Decision Making

We study how people decide what to ask, what to trust, what to verify, and when to rely on AI. These choices influence learning, productivity, accountability, trust, and societal outcomes.

  • AI literacy and confidence
  • Behavioral patterns in AI use
  • Responsible adoption and governance
  • Human-centered decision design