Wildfire preparedness is a deeply social challenge that depends on shared understanding, coordinated action, and community-level decision-making. Serious games have emerged as promising tools for supporting preparedness learning, yet their evaluation often relies on individual outcomes or surveys, leaving interactional learning processes underexplored. This paper addresses this gap by analyzing how players from wildfire-affected communities collaboratively constructed preparedness knowledge while playing Firesafe Friends, a two-player serious game. Using a Discourse Acts approach, we analyzed dialogue from onsite and remote sessions (n = 20) and triangulated the findings with pre/post surveys and observational notes. Our analysis reveals distinct phases of collaborative learning – orientation, strategy formation, collaboration, and reflection – marked by shifts in cognitive, social, and integrated discourse acts. Budget constraints prompted negotiation, contested upgrades supported collective reasoning, and reflective dialogue helped players connect in-game decisions to real-world preparedness. We contribute (1) an empirical analysis of discourse as a mechanism for resilience learning, (2) a discourse-centered triangulated evaluation method, and (3) design implications for resilience games that scaffold negotiation and reflection to strengthen community preparedness.

As wildfires increasingly threaten communities worldwide, those at risk can benefit from community-centric approaches to designing interventions. A multi-layered approach can make members of the community powerful co-designers by incorporating their ideas alongside insights from experts and designers to successfully create and deploy an intervention. We took a community-centered Research through Design approach to develop a Serious Game to promote wildfire preparedness in two communities. Participatory Design workshops, interviews, and targeted user studies allowed us to leverage local knowledge of residents and expertise of stakeholders at distinct stages where their insight could be best utilized. Based on this work, we propose a process for designing Serious Games as interventions for complex societal challenges by utilizing community and expert involvement throughout iteration. Our findings suggest that the games we designed following this process lead to concrete learning and opportunities for discussion, and offer flexibility for integration with other community initiatives.

This work presents an autonomous approach that explores the dynamic generation of relaxing soundscapes for games and artistic installations. Differently from past works, this system can generate music and images simultaneously, preserving human intent and coherency. We present our algorithm for the generation of audiovisual instances and also a system based on this approach, verifying the quality of the outcomes it can produce in light of current approaches for the generation of images and music. We also instigate the discussion around the new paradigm in arts, where the creative process is delegated to autonomous systems, with limited human participation. Our user study (N=74) shows that our approach overcomes current deep learning models in terms of quality, being recognized as human production, as if the outcome were being generated out of an endless musical improvisation performance.

The concept of gamified interactive models and its novel extensions, such as playification, has been widely approached in order to engage users in many fields. In fields such as HCI and AI, however, these approaches were not yet employed for supporting users to create different forms of artworks, like a musical corpus. While allowing novel forms of interactivity with partially-autonomous systems, these techniques could also foster the emergence of artworks not limited to experts. Hence, in this paper we introduce the concept of meta-interactivity for compositional interfaces, which extends an individual's capabilities by the translation of an effort into a proficiency. We present how this approach can be effective through a novel system that enables non-experts to compose coherent musical pieces through the use of imagetic elements in a virtual environment. We conduct experiments with a population of musical experts and non-experts, showing that non-experts were able to learn and create high-quality musical productions through our interactive approach.

AI is typically applied in video games in the creation of artificial opponents, in order to make them strong, realistic or even fallible (for the game to be "enjoyable" by human players). We offer a different perspective: we present the concept of "Art Games", a view that opens up many possibilities for AI research and applications. Conference participants will play Jikan to Kukan, an art game where the player dynamically creates the soundtrack with the AI system, while developing her experience in the unconscious world of a character.