  With the emergence of deep learning techniques, smartphone apps are now
embedded on-device AI features for enabling advanced tasks like speech
translation, to attract users and increase market competitiveness. A good
interaction design is important to make an AI feature usable and
understandable. However, AI features have their unique challenges like
sensitiveness to the input, dynamic behaviours and output uncertainty. Existing
guidelines and tools either do not cover AI features or consider mobile apps
which are confirmed by our informal interview with professional designers. To
address these issues, we conducted the first empirical study to explore
user-AI-interaction in mobile apps. We aim to understand the status of
on-device AI usage by investigating 176 AI apps from 62,822 apps. We identified
255 AI features and summarised 759 implementations into three primary
interaction pattern types. We further implemented our findings into a
multi-faceted search-enabled gallery. The results of the user study demonstrate
the usefulness of our findings.
