Reza Habibi

Reza Habibi, Johannes Pfau, Jonattan Holmes et al.

Failure and resilience are important aspects of gameplay. This is especially important for serious and competitive games, where players need to adapt and cope with failure frequently. In such situations, emotion regulation -- the active process of modulating ones' emotions to cope and adapt to challenging situations -- becomes essential. It is one of the prominent aspects of human intelligence and promotes mental health and well-being. While there has been work on developing artificial emotional regulation assistants to help users cope with emotion regulation in the field of Intelligent Tutoring systems, little is done to incorporate such systems or ideas into (serious) video games. In this paper, we introduce a data-driven 6-phase approach to establish empathetic artificial intelligence (EAI), which operates on raw chat log data to detect key affective states, identify common sequences and emotion regulation strategies and generalizes these to make them applicable for intervention systems.

Reza Habibi, Seung Wan Ha, Zhiyu Lin et al.

Meaningful human-AI collaboration requires more than processing language; it demands a deeper understanding of symbols and their socially constructed meanings. While humans naturally interpret symbols through social interaction, AI systems often miss the dynamic interpretations that emerge in conversation. Drawing on Symbolic Interactionism theory, we conducted two studies to investigate how humans and AI co-construct symbols and their meanings. Findings provide empirical insights into how humans and conversational AI agents collaboratively shape meanings during interaction. We show how participants shift their initial definitions of meaning in response to the symbols and interpretations suggested by the conversational AI agents, especially when social context is introduced. We also observe how participants project their personal and social values into these interactions, refining meanings over time. These findings reveal that shared understanding does not emerge from mere agreement but from the bi-directional exchange and reinterpretation of symbols, suggesting new paradigms for human-AI interaction design.