Advancing social-scientific research of human-AI interaction dynamics and outcomes often requires researchers to deliver experiences with live large-language models (LLMs) to participants through online survey platforms. However, technical and practical challenges (from logging chat data to manipulating AI behaviors for experimental designs) often inhibit survey-based deployment of AI stimuli. We developed DiSCoKit--an open-source toolkit for deploying live LLM experiences (e.g., ones based on models delivered through Microsoft Azure portal) through JavaScript-enabled survey platforms (e.g., Qualtrics). This paper introduces that toolkit, explaining its scientific impetus, describes its architecture and operation, as well as its deployment possibilities and limitations.

翻译：推进人机交互动态与结果的社会科学研究，通常需要研究者通过在线调查平台向参与者提供实时大语言模型（LLM）的交互体验。然而，技术及实践层面的挑战（从聊天数据记录到为实验设计操控AI行为）常常阻碍了基于调查的AI刺激部署。我们开发了DiSCoKit——一个用于通过支持JavaScript的调查平台（例如Qualtrics）部署实时LLM体验（例如基于通过Microsoft Azure门户交付的模型）的开源工具包。本文介绍了该工具包，阐述了其科学动因，描述了其架构与运行方式，并探讨了其部署的可能性与局限性。