构建可配置的人机协作实验平台，研究AI角色设计对团队影响。
TRAIL: A Platform for Configurable Human--AI Teaming Experiments
- 用人格特质与选择性发言机制配置AI队友，实现可复现的协作实验。
- 六轮课堂实验中AI保持低发言占比，促进团队认知支持与情感氛围。
- 实证发现不同AI角色显著影响贡献度、语言一致性和团队依赖度。
AI队友的设计特性（如人格、沟通方式、发言时机）会深刻影响团队的信任、协调与决策。现有工具无法提供可复现、嵌入实时协作并持续长期运行的实验基础设施。本文提出团队研究与人工智能融合实验室（TRAIL），一个基于网页的平台，将AI队友作为可配置、可复现的设计对象，集成大五人格模型、选择性参与消息管道、双重记忆机制、链式纵向实验设计及导出式分析功能。在一次包含约51名学生的六轮真实课堂部署中，TRAIL成功维持了纵向实验链条，确保AI始终处于对话中的少数地位，并支持导出驱动的文本相似性分析。单次盲态人格切换产生设计一致的双分离效应：认知支架型AI获得更高贡献评分与更强语言一致性；社交支持型AI则带来更温暖的团队氛围和更低的过度依赖。
原文摘要 · Abstract (English)
An AI teammate's design properties (personality, communication style, when it speaks) can shape a team's trust, coordination, and decisions. Studying this rigorously demands infrastructure no existing tool provides: reproducible configuration of an AI teammate embedded in instrumented, real-time collaboration sustained over time. We present the Team Research and AI Integration Lab (TRAIL), a web platform that makes the AI teammate a configurable, reproducible design object, pairing a Big Five persona with a selective-participation message pipeline, dual memory, chained longitudinal experiments, and export-ready analytics. In a real six-session classroom deployment (about 51 students), TRAIL sustained longitudinal chaining, held the AI to a stable minority of the conversation, and enabled export-driven AI-human text-similarity analysis. A single blind persona change produced a design-consistent double dissociation: a cognitive-scaffolding agent drew stronger contribution ratings and closer linguistic alignment; a socially-supportive agent, a warmer team climate and lower over-reliance.
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