The Wizard of Oz (WoZ) method is a common and popular approach for simulating interactive systems in Human-Computer Interaction (HCI). Running such studies is demanding for researchers because the human wizard must manage human–agent interactions in real time while keeping participants safe and the interaction natural. Many WoZ systems struggle to reproduce complex agent behaviours without minimal delays or heavy workload for the moderator. We introduce AI of Oz, a framework that uses large language models to support researchers by monitoring ongoing interactions, detecting sensitive moments, and suggesting contextually appropriate responses. In a study with 20 HCI-related researchers, the system improved participants’ ability to manage interactions and maintain control compared to a version without AI support. We outline implications for WoZ research and note current limitations and future directions.

The emergence of generative artificial intelligence (GenAI), combined with immersive virtual reality (VR), enables the rapid creation of personalised virtual content from simple text prompts, holding potential for emotional support. However, most current VR systems rely on pre-authored content and limit user agency in designing emotionally meaningful experiences. We introduce OasisMind, an AI-assisted VR system that empowers users to co-create 360° environments, corresponding ambient soundscapes, and context-aware digital companions through natural language prompts. In a user study (N=24), we observed how participants constructed virtual worlds for emotionally meaningful use cases and compared their creations to validated, pre-defined VR scenes recommended by previous research. Our results indicate a subjective preference for self-created environments, while no significant differences were observed in perceived satisfaction or presence between conditions. These findings suggest that user agency contributes to the emotional resonance of virtual experiences and inform the design of future personalised companion systems.

This study investigates Embodied Virtual Agents (EVAs) driven by Large Language Models (LLMs) as mediators in triadic collaboration where two users with conflicting goals must work towards a shared objective. We developed and evaluated an XR system where pairs (n=24) collaborated on an office design task, assessing the impact of agent presence and embodiment. Our findings reveal that agent embodiment significantly enhanced co-presence, which in turn fostered interactions that led to better perceived collaboration and higher user preference, whereas frequent interactions with the disembodied agent was associated with lower user preference. Conversely, agent presence did not improve task efficiency or satisfaction. Notably, our qualitative analysis revealed that the LLM-driven agent spontaneously adopted emergent facilitative and evaluative mediation strategies that align with collaborating and compromising conflict-resolving modes, showcasing its potential as an adaptive collaborative aid without explicit programming. These results highlight that the value of EVAs in complex collaboration lies in their ability to shape social dynamics and provide nuanced, context-aware mediation, a different form of value than traditional productivity enhancement.

Unsustainable behaviors are challenging to prevent due to their long-term, often unclear consequences. Serious games offer a promising solution by creating artificial environments where players can immediately experience the outcomes of their actions. To explore this potential, we developed EcoEcho, a GenAI-powered game leveraging multimodal agents to raise sustainability awareness. These agents engage players in natural conversations, prompting them to take in-game actions that lead to visible environmental impacts. We evaluated EcoEcho using a mixed-methods approach with 23 participants. Results show a significant increase in intended sustainable behaviors post-game, although attitudes towards sustainability had only marginal effects, suggesting that in-game actions likely can motivate intended real world behaviors despite similar opinions on sustainability. This finding highlights multimodal agents and action-consequence mechanics to effectively raising sustainability awareness and the potential of motivating real-world behavioral change. © 2025 Copyright held by the owner/author(s).

Character design in games involves interdisciplinary collaborations, typically between designers who create the narrative content, and illustrators who realize the design vision. However, traditional workflows face challenges in communication due to the differing backgrounds of illustrators and designers, the latter with limited artistic abilities. To overcome these challenges, we created Sketchar, a Generative AI (GenAI) tool that allows designers to prototype game characters and generate images based on conceptual input, providing visual outcomes that can give immediate feedback and enhance communication with illustrators' next step in the design cycle. We conducted a mixed-method study to evaluate the interaction between game designers and Sketchar. We showed that the reference images generated in co-creating with Sketchar fostered refinement of design details and can be incorporated into real-world workflows. Moreover, designers without artistic backgrounds found the Sketchar workflow to be more expressive and worthwhile. This research demonstrates the potential of GenAI in enhancing interdisciplinary collaboration in the game industry, enabling designers to interact beyond their own limited expertise.