Learnability through Adaptive Discovery Tools in Voice User Interfaces

The invisible nature of VUIs has been attributed to challenging discoverability with VUIs. Low discoverability often leads to learnability issues. Researchers have designed visual tools for VUIs to help users learn as they go. However, few have used adaptation to ensure that learnability with the help of these tools extends beyond initial use. We designed DiscoverCal, a calendar application designed using adaptive discovery tools to improve learnability in VUIs. In this paper, we identify key characteristics of existing discovery tools. We present our design of a VUI that adapts based on contextual relevance and user performance in order to extend learnability beyond initial use. We briefly discuss our user study design.