The emergence of deep learning techniques has led to the integration of on-device AI features in smartphone apps. These AI features enable advanced tasks such as speech translation, which not only attract users but also increase market competitiveness. However, designing a good interaction for these AI features is crucial to ensure their usability and understandability. AI features pose unique challenges, including sensitiveness to input, dynamic behaviors, and output uncertainty. Unfortunately, existing guidelines and tools do not adequately address these challenges. This was confirmed through informal interviews with professional designers who pointed out that current guidelines either do not cover AI features or are focused on mobile apps in general. To address these issues, the authors conducted the first empirical study to explore user-AI interactions specifically in mobile apps. Their objective was to understand the current status of on-device AI usage by investigating 176 AI apps out of a total of 62,822 apps. Through their analysis, they identified 255 distinct AI features and summarized 759 implementations into three primary interaction pattern types. Building upon their findings, the authors developed a multi-faceted search-enabled gallery that incorporates their insights. The results of a user study conducted on this gallery demonstrated the usefulness and effectiveness of their findings. Overall, this research sheds light on the importance of designing effective interactions for on-device AI features in mobile apps. By addressing the unique challenges associated with these features – such as sensitiveness to input, dynamic behaviors and output uncertainty – developers can enhance user experience and make their apps more competitive in the market.
Deep learning techniques have made it possible for smartphones to have special AI features. These AI features can do advanced tasks like translating speech, which makes people want to use them and helps the phone companies compete with each other. It's really important that these AI features are easy to use and understand. Right now, there aren't good rules or tools for making them work well. Some scientists did a study to learn more about how people use AI on their phones. They found 255 different AI features and put them into three groups based on how people interact with them. Then they made a special search tool based on what they learned. They tested it with some users and found that it was helpful and worked well. When the interactions with AI features on phones are good, it makes people like using the apps more and helps the phone companies do better in the market."
Definitions
- Deep learning techniques: Advanced methods used by computers to learn from data and make decisions without being explicitly programmed.
- On-device: Referring to something that is done or stored directly on a device, such as a smartphone.
- AI (Artificial Intelligence) features: Special abilities or functions in technology that mimic human intelligence, such as understanding speech or recognizing images.
- Usability: How easy something is to use or understand.
- Empirical study: A research study that uses observations or experiments to gather information.
- Interaction pattern types: Different ways that people can interact with something, like tapping buttons or speaking commands.
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Understanding User-AI Interactions in Mobile Apps: An Empirical Study
The emergence of deep learning techniques has sparked a revolution in the way we interact with technology. On-device AI features have been integrated into smartphone apps, enabling advanced tasks such as speech translation and making them more attractive to users. However, designing effective interactions for these AI features is essential to ensure their usability and understandability. Unfortunately, existing guidelines and tools do not adequately address the unique challenges posed by AI features – such as sensitiveness to input, dynamic behaviors and output uncertainty. To fill this gap in knowledge, researchers conducted an empirical study on user-AI interactions specifically in mobile apps.
Exploring User-AI Interactions
The authors of this research paper set out to explore user-AI interactions specifically in mobile apps by conducting the first empirical study of its kind. They analyzed 176 AI apps out of a total of 62,822 apps and identified 255 distinct AI features which were then summarized into three primary interaction pattern types. Through their analysis they developed a multi-faceted search-enabled gallery that incorporates their insights.
Testing the Gallery
To test the effectiveness of their findings, the authors conducted a user study on their gallery which demonstrated its usefulness and effectiveness. The results showed that developers can enhance user experience by addressing the unique challenges associated with on-device AI features – such as sensitiveness to input, dynamic behaviors and output uncertainty – thus making their apps more competitive in the market.
Conclusion
This research sheds light on the importance of designing effective interactions for on-device AI features in mobile apps. By understanding how users interact with these features through empirical studies like this one, developers can create better experiences for users while also increasing market competitiveness for their products.
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.
