TRAIL: A Platform for Configurable Human--AI Teaming Experiments

2026-07-13

AI Summary

This study addresses the lack of infrastructure supporting reproducible, longitudinal, and real-time human–AI collaboration experiments, which has hindered systematic investigation into how design attributes of AI teammates influence team trust, coordination, and decision-making. To bridge this gap, the authors introduce TRAIL, a novel platform that embeds configurable and reproducible AI teammates within an instrumented, authentic collaborative environment, enabling longitudinal experimentation and behavioral analysis. TRAIL innovatively integrates the Big Five personality model, selective messaging channels, a dual-memory architecture, chained experimental scheduling, and textual similarity analysis tools to systematically modulate AI personality, communication timing, and interaction style. In a six-round classroom study with 51 students, TRAIL sustained stable AI collaboration and revealed significant differential effects of AI personality on team perceptions of contribution, linguistic alignment, group atmosphere, and reliance on the AI teammate.

Abstract

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.

Problem

• human-AI teaming
• configurable AI teammate
• longitudinal collaboration
• trust and coordination
• reproducible experimentation

Innovation

• configurable AI teammate
• longitudinal human-AI collaboration
• Big Five persona
• selective-participation messaging
• reproducible experimental platform