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This study explored human-AI collaboration in chess puzzles. Findings reveal how AI performance changes impact user confidence and decision-making, offering insights for human-computer interaction.

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Area of Science:

  • Human-Computer Interaction
  • Cognitive Psychology
  • Artificial Intelligence

Background:

  • Human-AI collaboration is increasingly prevalent across various domains.
  • Understanding user interaction dynamics with AI is crucial for effective system design.
  • Confidence calibration in human-AI partnerships remains an active research area.

Purpose of the Study:

  • To investigate the impact of varying artificial intelligence (AI) performance trajectories on human participants' decision-making and confidence.
  • To collect a novel dataset detailing human-AI interaction during chess puzzle solving.
  • To provide a foundation for analyzing cognitive and behavioral patterns in human-AI collaboration.

Main Methods:

  • A human subjects study involving 100 participants solving chess puzzles with AI assistance.
  • Participants were assigned to conditions with AI performance changing from high-to-low or low-to-high.
  • Data collected included participant moves, AI suggestions, feedback, and confidence ratings.

Main Results:

  • The dataset captures detailed interaction logs for 100 participants across 33 problems each.
  • Analysis opportunities include cognitive load, trust calibration, and adaptive AI strategies.
  • The study design allows for examination of how AI performance shifts influence user behavior.

Conclusions:

  • The dataset provides a valuable resource for researching human-AI collaboration and confidence calibration.
  • Findings can inform the development of more intuitive and effective human-AI systems.
  • This work contributes to the fields of human-computer interaction, psychology, and computer science.