An Exploratory Study on AI-driven Visualisation Techniques on Decision Making in Extended Reality
Ze Dong, Binyang Han, Jingjing Zhang, Ruoyu Wen, Barrett Ens, Adrian Clark, Tham Piumsomboon

TL;DR
This study investigates how four AI-driven visualisation techniques in XR influence user decision-making, highlighting the importance of autonomy, transparency, and context for effective design.
Contribution
It introduces and evaluates four novel AI-driven visualisation techniques in XR, providing insights into their impact on user preferences and decision-making.
Findings
Maintaining user autonomy enhances decision-making.
AI transparency builds user trust.
Context-aware visualisation improves user experience.
Abstract
The integration of extended reality (XR) with artificial intelligence (AI) introduces a new paradigm for user interaction, enabling AI to perceive user intent, stimulate the senses, and influence decision-making. We explored the impact of four AI-driven visualisation techniques -- `Inform,' `Nudge,' `Recommend,' and `Instruct' -- on user decision-making in XR using the Meta Quest Pro. To test these techniques, we used a pre-recorded 360-degree video of a supermarket, overlaying each technique through a virtual interface. We aimed to investigate how these different visualisation techniques with different levels of user autonomy impact preferences and decision-making. An exploratory study with semi-structured interviews provided feedback and design recommendations. Our findings emphasise the importance of maintaining user autonomy, enhancing AI transparency to build trust, and considering…
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Taxonomy
TopicsTechnology and Data Analysis · Impact of AI and Big Data on Business and Society · Diverse Topics in Contemporary Research
