TRAIL: A Platform for Configurable Human--AI Teaming Experiments
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.
Cite
@article{arxiv.2607.12180,
title = {TRAIL: A Platform for Configurable Human--AI Teaming Experiments},
author = {Mohammad Amin Samadi and Pedro Martins De Bastos and Jaeyoon Choi and Spencer JaQuay and Seehee Park and Nia Nixon},
journal= {arXiv preprint arXiv:2607.12180},
year = {2026}
}