40 citations · 64 across the 11 of their papers we have counts for
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cs.CL2026
Why LLMs Give In: Conversational Factors and Reasoning Behind Medical Sycophancy
Kaike Ping, Buse Çarık, Caleb Wohn +3
Large language models can answer a medical question correctly and still abandon that answer when a user pushes back. We study this failure as medical sycophancy and ask when models…
cs.HC2026
"Are we writing an advice column for Spock here?" Understanding Stereotypes in AI Advice for Autistic Users
Caleb Wohn, Buse Çarık, Xiaohan Ding +3
Autistic individuals sometimes disclose autism when asking LLMs for social advice, hoping for more personalized responses. However, they also recognize that these systems may repro…
cs.HC2026
Human-Human-AI Triadic Programming: Uncovering the Role of AI Agent and the Value of Human Partner in Collaborative Learning
Taufiq Daryanto, Xiaohan Ding, Kaike Ping +4
As AI assistance becomes embedded in programming practice, researchers have increasingly examined how these systems help learners generate code and work more efficiently. However,…