A data-driven method adaptively selects the number of LLM-simulated responses to form confidence sets with nominal coverage for human survey parameters and equates that number to the LLM's effective human-equivalent sample size.
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Among novice programmers using AI code generators, trust did not predict compliance with suggestions, while performance correlated with both compliance and increased subsequent trust.
Large-scale data from an AI platform confirms students have consistent learning rates (IQR 7.01-8.25 opportunities to 80% mastery) despite variable starting knowledge, replicating prior findings with automated knowledge components.
citing papers explorer
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How Many Human Survey Respondents is a Large Language Model Worth? An Uncertainty Quantification Perspective
A data-driven method adaptively selects the number of LLM-simulated responses to form confidence sets with nominal coverage for human survey parameters and equates that number to the LLM's effective human-equivalent sample size.
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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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Personalized AI Practice Replicates Learning Rate Regularity at Scale
Large-scale data from an AI platform confirms students have consistent learning rates (IQR 7.01-8.25 opportunities to 80% mastery) despite variable starting knowledge, replicating prior findings with automated knowledge components.