HARP让研究人员能精准控制AI行为，系统研究人机交互体验。
HARP: The Human--AI Research Platform
- 构建可配置的实时AI代理，模拟真实交互场景
- 记录用户输入时间、删改行为和响应延迟等细微操作
- 适合人机交互、UI设计及认知心理学研究者使用
大型语言模型（LLMs）正将人机交互从传统界面转向对话式交流。当前人机交互与用户体验研究多依赖受控可用性测试、访谈、问卷、转录分析及静态原型，但静态原型难以研究与实时AI系统的互动，也无法在不同参与者和情境中系统调控LLM行为。对话转录也难以揭示用户在提交前如何构思、修改或犹豫提示词。为此，我们设计了人类-人工智能研究平台（HARP），供研究者、设计师及任何好奇“如果AI这样会怎样？”的人使用。HARP将参与者置于可控的模拟场景中，配备可配置的实时AI代理。研究者可控制代理提示词、模型参数、响应特征及实验条件，可在预设时刻触发问卷，并记录提示词撰写时间、响应延迟、删除次数与击键停顿。未来计划加入语音、面部表情、手势分析，以及在合法合规前提下的情绪识别。我们通过一项研究展示其应用：考察技术细节程度与响应长度对用户记忆保留的影响。结合可控的实时代理与行为数据及自报告测量，HARP支持系统性评估AI设计选择对用户的影响。
原文摘要 · Abstract (English)
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.
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