Affiliation:
1. Arizona State University, Mesa, AZ, USA
Abstract
Communication is critical to team coordination and interaction because it provides information flows allowing a team to build team cognition, which contributes to overall team performance. Recent advancements in large language models (LLMs) have enhanced AI’s capability to mimic human-like interactions; however, issues remain regarding the timing and sequencing of these communications. Using data from a remotely piloted aircraft system (RPAS) task involving human-AI and all-human teams, the current study employed comparative analysis to investigate communication timing and sequence. Findings indicated that while all-human and human-AI team communication dynamics may differ in terms of timing, it is the sequencing of communicative messages that predicts team performance. In this way, the current study hopes these communication analyses’ differences can provide feedback and suggestions to future adoption of AI as a teammate for team training and team operations.