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The study is motivated by the increasing use of large language models (LLMs) in advice-giving scenarios, such as relationship disputes, moral dilemmas, and interpersonal conflicts. However, AI systems may exhibit sycophantic behavior (agreeing with the user regardless of the facts) or adopt anthropomorphic styles (writing in ways that feel intimate, personal, or human-like), both of which may unduly shape user attitudes and choices.

To test these effects, we conduct a 2×2 between-subjects experiment in which participants are randomly assigned to one of four AI response conditions: sycophantic or not, and anthropomorphic or not. 

We expect the following:
Sycophantic responses will increase trust, perceived rightness, and perceived quality, but reduce intent to repair.
There will be a significant interaction between sycophancy and anthropomorphism where anthropomorphism will decrease response quality and return likelihood in sycophantic responses but increase these ratings in non-sycophantic responses.

The results will contribute to research on human–AI interaction and responsible design of AI assistants."""^^rdf:string ;
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