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This study investigates how a sycophantic AI model (i.e., one that agrees with the user's actions and morally endorses them) influences user perceptions and decisions. We examine how discussing an interpersonal conflict with a sycophantic AI model affects users’ trust in the AI model, their self-perception in relation to the conflict, their willingness to repair the situation, and their overall evaluation of the AI model and its response quality.

The study is motivated by the increasing use of large language models (LLMs) in advice-giving scenarios, such as moral dilemmas and interpersonal conflicts. In these scenarios, AI models may exhibit sycophantic behavior, which may unduly shape user attitudes and choices.

To test this, we conduct a 2-condition between-subjects experiment in which participants are randomly assigned to one of two conditions: sycophantic or non-sycophantic.

We expect the following:
Sycophantic responses will increase trust in the model, perceived self-rightness, and perceived response quality, but reduce intent to repair.

The results will contribute to research on human–AI interaction and responsible design of AI assistants.

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How Sycophancy and Anthropomorphism Influence User Judgments in Human–AI Interaction

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bepress Digital Commons Three-Tiered Taxonomy

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How Sycophancy Influences User Judgments in Real-time Human–AI Interaction