interactive experience designers

Abstract: As AI enters reflection and emotional support, understanding how it can participate in personal meaning-making while preserving users' authority over interpretation is increasingly important. We present ClayFlect, a novel MLLM-powered system integrating tactile clay-making with conversational and visual generative AI, and report a mixed-methods study with 50 participants. Reflection developed across material, conversational, and generated forms rather than through AI interaction alone. Participants treated AI representations as provisional: they compared, redirected, reinterpreted, selectively incorporated, or left them aside as their artefacts and meanings evolved. Clay provided a directly manipulable space in which participants could continue developing meaning independently of the AI, while generated representations externalised possibilities beyond what they could readily make or visualise. We show how generative AI can participate through representations that remain negotiable, and derive implications for supporting movement across representations, preserving parallel sites of control, and allowing AI support to recede or deepen as reflection unfolds.

Abstract: Foundation models, alongside advances in learned game-world models, are reshaping AI across the game lifecycle. Beyond playing games, recent systems model players and game dynamics, support design and development, adapt player-facing experiences at runtime, and evaluate resulting artifacts. Yet these directions have evolved largely separately, obscuring which capabilities transfer across settings and which remain tied to particular games, engines, interfaces, or player populations. We organize the literature into six roles according to the immediate use of AI output: playing and acting; modeling players and games; designing games; building and maintaining games; generating and adapting at runtime; and testing and evaluating games. For each role, we examine what structure is supplied by the game or workflow, what AI learns or produces, which capabilities and artifacts transfer across settings and roles, and what evidence supports the claims. We identify cross-role connections: trajectories train world models, learned environments provide experience for agents, design specifications drive executable implementations, and play or testing feedback guides revision. However, control schemes, rules, engine interfaces, state representations, and player contexts often remain setting-specific, so downstream claims require validation in the target setting. Evaluation is most standardized for bounded game playing and selected learned environments, while persistent state in learned worlds, repeated software revision, validated player modeling, sustained runtime adaptation, and representative automated testing remain less established. The central challenge is to reuse or transfer outputs and capabilities across roles while re-establishing evidence for effectiveness in the game-specific contexts where they are used.

Abstract: 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.