发表机构
BTPX Innovation Lab（BTPX创新实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究旨在设计人机人工智能研究平台（HARP），通过让参与者在可控模拟场景中与可配置实时人工智能代理互动，研究人员可控制多种因素并记录相关数据，能系统测试人工智能设计选择对用户的影响。
AI 中文摘要
大语言模型已将人机交互从‘传统’界面交互转向更多对话式交流。研究人机交互和用户界面的人员采用多种研究方式，但静态原型在研究与实时人工智能系统交互等方面存在局限，对话记录也无法揭示用户提交提示前的情况。我们设计了人机人工智能研究平台（HARP），让参与者处于可控模拟场景中与可配置的实时人工智能代理互动。研究人员能控制多种因素并触发调查、记录相关时间等。通过一项研究展示了HARP，它能系统测试人工智能设计选择对用户的影响。
英文摘要
Large language models (LLMs) have shifted human--computer interaction from `traditional'' interface journeys toward more conversational exchanges. Researchers studying HCI and UI use moderated usability sessions, interviews, surveys, transcript analysis, and static prototypes. However, static prototypes provide limited opportunities to study interaction with live AI systems or systematically control how an LLM behaves across participants and scenarios. Conversation transcripts reveal little about how users formulate, revise, and hesitate over prompts before submission. We designed the Human--AI Research Platform (HARP) for researchers, designers, and anyone who has ever wondered, `What if AI did this?' HARP places participants in controlled mock scenarios with live, configurable AI agents. Researchers can control agent prompts, model parameters, response characteristics, and experimental conditions; trigger surveys at predefined moments; and record prompt composition time, response latency, deletions, and keystroke pauses. Planned capabilities include voice, facial expression, gesture, and, where legally and ethically appropriate, emotion analysis. We illustrate HARP through a study examining how technical specificity and response length affect retention of LLM output. By pairing controllable live agents with behavioral and self-report measures, HARP enables systematic testing of how AI design choices affect users.
Comments5 pages, 3 figures, SAP Academic Community Conference North America, 2026
