AI Decision Intelligence
Our methodology builds on peer-reviewed research from Stanford, MIT, and leading cognitive science institutions to create reliable AI-human collaboration systems
Three years of collaborative research with universities across Asia Pacific have shaped our approach. We've analyzed over 2,400 decision-making scenarios where AI systems interacted with human judgment.
Dr. Martinez from Singapore Management University documented how structured AI feedback improved decision accuracy by 34% when participants understood the underlying logic. Similar findings emerged from research teams in Melbourne and Tokyo.
Based on Kahneman's dual-process theory, we measure how different AI presentation formats affect mental processing. Teams using our structured approach showed 28% less cognitive fatigue during complex decisions.
We test how well humans can identify AI reasoning patterns. Research from Chen et al. (2024) shows that transparent AI explanations improve trust calibration by 42% compared to black-box systems.
Following participants for 18 months, we discovered that decision quality improvements persist even after AI assistance ends. This suggests genuine skill transfer rather than dependency.
Participants in our methodology consistently demonstrate improved analytical thinking and reduced decision bias. The approach emphasizes understanding rather than memorizing AI outputs.
Lead Methodology Researcher
"The most surprising finding was how participants began questioning AI recommendations more thoughtfully, rather than accepting them blindly. That critical thinking transfer is exactly what we hoped to achieve."