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

Mohammad Amin Samadi, Pedro Martins De Bastos, Jaeyoon Choi, Spencer JaQuay, Seehee Park +1 more
7/13/2026
cs.HCcs.AI

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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.

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Cite this paper

@article{samadi2026trail,
  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},
  year   = {2026},
  eprint = {2607.12180},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2607.12180v1}
}

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