Sequential AI advice boosts thoughtful human decisions
Thinking Twice: A Sequential Approach to Nudge Towards Reflective Judgment in GenAI-Assisted Decision Making
Human-Computer Interaction
Summary
People often rely on quick gut feelings when using AI to help make decisions, which can lead to mistakes. The authors tested a method where people first make their own decision before seeing AI advice, encouraging them to think more carefully. This approach helped people spot when their initial choice conflicted with the AI, leading to better decisions. It also made people use AI suggestions more effectively.
What this means in practice
- •For decision support developers: Create AI tools that first collect user decisions before giving advice to encourage more analytical thinking and improve outcomes.
- •For risk management teams: Design workflows that separate human judgment and AI input in sequence to reduce errors caused by too quick reliance on AI recommendations.
Authors
Hüseyin Hussein Keke, Daniel Eisenhardt, Christian Meske
Abstract
Humans increasingly use generative AI (GenAI) for decision-making assistance, yet tend to engage in such situations using quick, intuitive (System 1) rather than deliberate, analytical assessment (System 2) as reflected in Dual Process Theory. Such engagement reduces the effectiveness of human-GenAI interactions and increases associated risks. Our research investigates how to design human-GenAI interactions that promote the transition from System 1 to System 2 thinking in decision-making. Based on the Three-Stage-Model, which suggests humans switch to System 2 when conflicts are detected, we conducted an experiment (n=130) testing sequential human-GenAI interaction as a cognitive forcing nudge during problem-solving tasks. This approach, where humans decide first before receiving AI assistance, enables detection of conflicting decisions. Compared to non-sequential settings (concurrent or no AI assistance), results show the sequential condition significantly enhanced both decision performance and GenAI prompt utilization, providing valuable insights for designing more reflective and thus effective human-GenAI interactions.