Human-AI Collaboration Dynamics
Articles on why human pushback, negative signaling, and relational regard drive better decisions than algorithmic agreement.
Parallel Learning Systems in Humans and AI: In-Context and In-Weight Trade-offs
Researchers found that humans and AI share a similar interplay between two learning systems: flexible, quick in-context learning and gradual incremental learning. Experiments showed that AI could develop in-context learning abilities after extensive incremental practice, much like humans do. Both display trade-offs between flexibility and retention.
Human Perception Outpaces AI in Interpreting Dynamic Social Interactions
Research demonstrates humans significantly outperform AI models in interpreting dynamic social interactions, with implications for autonomous vehicles and assistive robots. Study shows 350+ AI models fail to match human accuracy or brain response predictions.
The Three-Prompt Trap: How Students Lose Ownership to AI (And How to Stop It)
Research on how students can maintain their intellectual agency and creative ownership when working with generative AI, focusing on the "deep idea integration" approach that requires original ideas at every prompt rather than just at the start.