A between-subjects experiment (N=192) finds that token-level uncertainty increases agreement with LLM answers while relation-level uncertainty reduces external verification in medical decision tasks.
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RelianceScope is a new analytical framework that maps AI reliance into nine engagement patterns across help-seeking and response-use, situated in students' prior knowledge and instructional context, validated on programming course logs.
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Not All Uncertainty Is Equal: How Uncertainty Granularity Shapes Human Verification in LLM-Assisted Decision Making
A between-subjects experiment (N=192) finds that token-level uncertainty increases agreement with LLM answers while relation-level uncertainty reduces external verification in medical decision tasks.
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RelianceScope: An Analytical Framework for Examining Students' Reliance on Generative AI Chatbots in Problem Solving
RelianceScope is a new analytical framework that maps AI reliance into nine engagement patterns across help-seeking and response-use, situated in students' prior knowledge and instructional context, validated on programming course logs.