Virtual reality (VR) theatre artists are combining theatre production and game development practices to create live performances in VR. To date, little is known about VR theatre creators’ experiences of this process or how staging a play in VR might affect the audience’s experience. To capture the experience of developing a VR theatre production we interviewed the production team behind the VR play You Should Have Stayed Home. Members of this team felt the process was a learning experience and shared the lessons they plan to incorporate into their future work. We report on the team’s efforts to understand the VR theatre medium, how this team was constructed, and challenges that they encountered. In this paper we present the opportunities that the production team members identified for creating novel experiences for VR audiences, and their own needs as creators.

Partial automation makes digital games simpler by performing game actions for players. It may simplify gameplay for non-gamers who have difficulty controlling and understanding games. However, the automation may make players confused about what they control and what the automation controls. To describe and explain non-gamers’ experiences of automation confusion, we analyzed gameplay, think-aloud, and interview data from ten non-gamer participants who played two partially automated games. Our results demonstrate how incorrect mental models, behaviours resulting from those models, and players’ attitudes towards the games led to different levels and types of confusion.

Play is a valuable context for investigating how users understand AI systems. This paper offers a case study that explores how users’ interactions with AI in other domains can be integrated into a game. Through this transformation, researchers and practitioners may uncover players’ misconceptions and learn how they evolve over time. In our case study, we adapt Stable Diffusion for use in a social human-AI interaction (HAI) game. The game introduces a challenging twist to a typical HAI and promotes discussion of users’ mental models with each other. We offer design considerations to help researchers design playful and social interactions that yield insights into players’ mental models.

Shared control is an emerging interaction paradigm in which a human and an AI partner collaboratively control a system. Shared control unifies human and artificial intelligence, making the human’s interactions with computers more accessible, safe, precise, effective, creative, and playful. This form of interaction has independently emerged in contexts as varied as mobility assistance, driving, surgery, and digital games. These domains each have their own problems, terminology, and design philosophies. Without a common language for describing interactions in shared control, it is difficult for designers working in one domain to share their knowledge with designers working in another. To address this problem, we present a dimension space for shared control, based on a survey of 55 shared control systems from six different problem domains. This design space analysis tool enables designers to classify existing systems, make comparisons between them, identify higher-level design patterns, and imagine solutions to novel problems.

Rehabilitation gaming—play of digital games that incorporate rehabilitation exercises—is a well-known and broadly applicable way to make physical rehabilitation more fun. It can motivate patients with spinal cord injury to engage in exercises that they find boring and can be as effective as traditional physiotherapy. However, patients’ needs are not only physical. Rehabilitation also needs to help patients overcome the psychological trauma of spinal cord injury. For patients coping with disability, hopelessness, depression, anxiety, or a loss of identity, rehabilitation gaming may provide benefits beyond making exercise more fun. We asked six participants with spinal cord injury to play three cycling-based rehabilitation games to determine how play might change their experiences of rehabilitation. They said that rehabilitation games may be able to help patients to actively participate in their rehabilitation, help them to rediscover who they are, and show them a better future living with spinal cord injury.

Self-driving cars are around the corner, yet little is known about how users of self-driving cars will react to the car’s driving style, and whether the driver’s mood affects their driving style preference. This paper explores the impact of users’ mood on driving style preference in self-driving cars. An experiment was conducted online (N=182) to investigate participants’ preference for three driving styles (conservative, moderate, aggressive) under three induced moods (calm, neutral, excited). Measures of arousal, valence, and driving satisfaction were recorded. Overall, participants scored the aggressive driving style lowest, irrespective of driver mood. Participants’ mood impacted preference, where a mismatch between driving style and mood induced prior to the driving style predicted lower driver satisfaction scores. We conclude with the design recommendation that driving styles in self-driving cars should not be overly aggressive, and drivers’ mood should be taken into consideration when designing driving styles.

Digital games are designed to be controlled using hardware devices such as gamepads, keyboards, and cameras. Some device inputs may be inaccessible to players with motor impairments, rendering them unable to play. Games and devices can be adapted to enable play, but for some players these adaptations may not go far enough. Games may require inputs that some players cannot provide with any device. To address this problem, we introduce partial automation, an accessibility technique that delegates control of inaccessible game inputs to an AI partner. Partial automation complements and builds on other approaches to improving games' accessibility, including universal design, player balancing, and interface adaptation. We have demonstrated partial automation in two games for the rehabilitation of spinal cord injury. Six study participants with vastly different motor abilities were able to play both games. Participants liked the increased personalization that partial automation affords, although some participants were confused by aspects of the AI's behaviour.