Traditional eye tracking methodologies have largely focused on single-user data. The study of multi-user dynamics and social interaction requires a novel analysis framework, partially addressed in current research. In this study, we introduce Group Eye Tracking (GET) as a framework for simultaneously collecting and analyzing eye movement data from multiple participants to reveal group-level patterns of visual dynamics. We use a custom application, which synchronously records eye movements from multiple users performing tasks on separate computers, and a custom R package implementing Recurrence Quantification Analysis (RQA) for examining time-series recurrences of visual dynamics. By quantifying how eye movement patterns recur and align among group members, we potentially provide indicators of cognitive states in collaborative decision-making, within real-time group interactions. The resulting measures can also provide information about the role of task parameters, interface layouts, and team performance. This approach demonstrates how GET can serve for developing next-generation augmented cognition systems by integrating advanced analytics and real-time adaptivity by the analysis of collective task outcomes.

With artificial intelligence (AI) systems entering our working and leisure environments with increasing adaptation and learning capabilities, new opportunities arise for developing hybrid (human-AI) intelligence (HI) systems, comprising new ways of collaboration. However, there is not yet a structured way of specifying design solutions of collaboration for hybrid intelligence (HI) systems and there is a lack of best practices shared across application domains. We address this gap by investigating the generalization of specific design solutions into design patterns that can be shared and applied in different contexts. We present a human-centered bottom-up approach for the specification of design solutions and their abstraction into team design patterns. We apply the proposed approach for 4 concrete HI use cases and show the successful extraction of team design patterns that are generalizable, providing re-usable design components across various domains. This work advances previous research on team design patterns and designing applications of HI systems.