As large language models (LLMs) become increasingly prevalent, understanding human-LLM interactions is emerging as a central priority in psychological research. Online experiments offer an efficient means to study human-LLM interactions, yet integrating LLMs into established survey platforms remains technically demanding, particularly when aiming for ecologically valid, real-time conversational experiences with strong experimental control. We introduce Simple Chat, an open-source, research-focused chat interface that streamlines LLM integration for platforms such as Qualtrics, oTree, and LimeSurvey, while presenting a unified participant experience across conditions. Simple Chat connects to both commercial providers and open-weights models, supports streaming responses to preserve conversational flow, and offers an administrative interface for fine-grained control of prompts and interface features. By reducing technical barriers, standardizing interfaces, and improving participant experience, Simple Chat helps advance the study of human-LLM interaction. In this article, we outline Simple Chat's key features, provide a step-by-step tutorial, and demonstrate its utility through two illustrative case studies.

翻译：随着大型语言模型（LLMs）日益普及，理解人类与LLMs的互动已成为心理学研究的核心议题。在线实验为研究人类-LLM交互提供了高效途径，然而将LLMs整合到成熟的调查平台（如Qualtrics、oTree和LimeSurvey）仍面临技术挑战，尤其是在追求生态效度高、实时对话体验与严格实验控制兼具的情况下。本文介绍Simple Chat——一个开源、专注于研究的聊天界面，它简化了LLMs在上述平台的集成流程，同时为不同实验条件下的参与者提供统一的交互体验。Simple Chat支持连接商业API与开源权重模型，具备流式响应功能以保持对话流畅性，并提供管理界面用于精细控制提示词与界面特性。通过降低技术门槛、标准化交互界面及优化参与者体验，Simple Chat有助于推动人类-LLM交互研究的发展。本文详细阐述Simple Chat的核心功能，提供逐步操作教程，并通过两个案例研究展示其实际应用价值。