DoubleAgents shows that a distributed-cognition design with coordination agent, dashboard, and policy module increases user comfort and reliance on AI agents for coordination tasks over time.
org/10.2307/258792 McCraw, B
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Trust in social LLM chatbots is a dynamic, situated user state that evolves through ongoing interactions rather than forming as a stable one-time judgment.
Among novice programmers using AI code generators, trust did not predict compliance with suggestions, while performance correlated with both compliance and increased subsequent trust.
Survey and experiment with 606 analysts show regularization adoption depends on usability and community norms, not formal recommendations.
citing papers explorer
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DoubleAgents: Human-Agent Alignment in a Socially Embedded Workflow
DoubleAgents shows that a distributed-cognition design with coordination agent, dashboard, and policy module increases user comfort and reliance on AI agents for coordination tasks over time.
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Trust as a Situated User State in Social LLM-Based Chatbots: A Longitudinal Study of Snapchat's My AI
Trust in social LLM chatbots is a dynamic, situated user state that evolves through ongoing interactions rather than forming as a stable one-time judgment.
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Relationships Between Trust, Compliance, and Performance for Novice Programmers Using AI Code Generation
Among novice programmers using AI code generators, trust did not predict compliance with suggestions, while performance correlated with both compliance and increased subsequent trust.
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Why is Regularization Underused? An Empirical Study on Trust and Adoption of Statistical Methods
Survey and experiment with 606 analysts show regularization adoption depends on usability and community norms, not formal recommendations.
- DialToM: A Theory of Mind Benchmark for Forecasting State-Driven Dialogue Trajectories