Human Intelligence Engineering
Understanding human intelligence in the age of AI.
HIE Lab is an evidence-based research system for understanding how human intelligence develops, adapts, and works with artificial intelligence.
Explore current research →The HIE research loop
From discovery to an inspectable framework.
AI helps discover and assess research. Source scope, model provenance, and confidence stay visible. Human review is a separate status, never implied by AI assessment.
- 01Discover
- 02Evaluate
- 03Claims
- 04Evidence
- 05Human Review
- 06Framework
- Papers
- 13
- Claims
- 4
- Evidence Relationships
- 12
- Topics
- 20
- Human-reviewed items
- 0
Public content is currently served from the versioned research snapshot.
Current Research
Recently published records in the evidence library.
- 2025
Generative AI at Work
A staggered rollout to 5,172 customer-support agents increased issues resolved per hour by 15% on average. Less experienced workers gained most, while the most experienced saw smaller gains and slight quality declines.
Read assessment → - 2024
How human–AI feedback loops alter human perceptual, emotional and social judgements
Across experiments with 1,401 participants, biased AI altered human perceptual, emotional, and social judgements and could amplify bias over time. Participants often underestimated the system influence.
Read assessment → - 2024
When combinations of humans and AI are useful: A systematic review and meta-analysis
This preregistered synthesis of 106 experiments and 370 effect sizes found that human-AI combinations underperformed the better standalone agent on average. Outcomes varied materially by task, with losses in decisions and gains in content creation.
Read assessment →
Open Questions
Claims that remain emerging or contested.
- contestedHuman-AI collaboration does not reliably outperform the better standalone human or AI; results depend strongly on the task and interaction design.
- emergingSmartphone presence or use has a small negative average association with cognitive performance, but the effect varies substantially by domain and context.
- emergingOnline search can inflate perceived internal understanding by blurring the boundary between accessible external information and knowledge held in memory.
Latest Claims
Normalized propositions that can accumulate, lose, or change evidence.
- 4 relationshipsHuman-AI collaboration does not reliably outperform the better standalone human or AI; results depend strongly on the task and interaction design.
- 2 relationshipsExpected access to external digital information changes memory strategy, reducing memory for content while preserving where it can be found.
- 3 relationshipsSmartphone presence or use has a small negative average association with cognitive performance, but the effect varies substantially by domain and context.
- 3 relationshipsOnline search can inflate perceived internal understanding by blurring the boundary between accessible external information and knowledge held in memory.
Latest Evidence
The newest visible paper-to-claim relationships.
- qualifies
- supports
- supports
- qualifies
How HIE Works
No Claim Without Evidence.
AI helps discover and assess research. Source scope, model provenance, and confidence stay visible. Human review is a separate status, never implied by AI assessment.
Explore the framework →