arXiv subjects

Adalberto L. Simeone

Publications and source records attributed to Adalberto L. Simeone.

3 recordsLinked to original sources

arXiv2026

"What Can I Do for You'': How Should AI Companions Provide Assistance to Players in Virtual Reality Games

Recent advances in artificial intelligence (AI) have expanded the capabilities of non-player characters (NPCs), enabling them to perceive game states, perform in-game actions. In immersive virtual reality (VR) games, such assistance is not limited to providing hints or interface-level support: an AI companion can appear as a co-present character, share the player's spatial environment, and visibly act on game objects. This raises a design question for VR gameplay: how can AI companions best assist players while preserving their active participation in the virtual world? To explore this question, we developed a VR puzzle game for Apple Vision Pro featuring an AI companion across four gameplay modes: no assistance, command-based assistance, discussion-based assistance, and autonomous agent play. A within-subjects study with 24 participants showed that AI assistance significantly reduced players' workload. However, autonomous agent play, despite producing the lowest workload, substantially diminished player experience by reducing challenge, autonomy, immersion, and enjoyment. Qualitative analysis further showed that players evaluated the companion not only by its usefulness, but also by whether it felt like a tool, a teammate, or an integrated character in the game world. We categorised participants into four player types and summarised their expectations of AI companions. These findings provide design implications for AI companions as embodied participants in VR games.

cs.HC↗

arXiv2024

Focus Agent: LLM-Powered Virtual Focus Group

In the domain of Human-Computer Interaction, focus groups represent a widely utilised yet resource-intensive methodology, often demanding the expertise of skilled moderators and meticulous preparatory efforts. This study introduces the ``Focus Agent,'' a Large Language Model (LLM) powered framework that simulates both the focus group (for data collection) and acts as a moderator in a focus group setting with human participants. To assess the data quality derived from the Focus Agent, we ran five focus group sessions with a total of 23 human participants as well as deploying the Focus Agent to simulate these discussions with AI participants. Quantitative analysis indicates that Focus Agent can generate opinions similar to those of human participants. Furthermore, the research exposes some improvements associated with LLMs acting as moderators in focus group discussions that include human participants.

cs.HC↗

arXiv2021

SelectVisAR: Selective Visualisation of Virtual Environments in Augmented Reality

When establishing a visual connection between a virtual reality user and an augmented reality user, it is important to consider whether the augmented reality user faces a surplus of information. Augmented reality, compared to virtual reality, involves two, not one, planes of information: the physical and the virtual. We propose SelectVisAR, a selective visualisation system of virtual environments in augmented reality. Our system enables an augmented reality spectator to perceive a co-located virtual reality user in the context of four distinct visualisation conditions: Interactive, Proximity, Everything, and Dollhouse. We explore an additional two conditions, Context and Spotlight, in a follow-up study. Our design uses a human-centric approach to information filtering, selectively visualising only parts of the virtual environment related to the interactive possibilities of a virtual reality user. The research investigates how selective visualisations can be helpful or trivial for the augmented reality user when observing a virtual reality user.

cs.HC↗