Examining Autocompletion as a Basic Concept for Interaction with Generative AI
This conceptual analysis addresses interaction design problems for researchers and practitioners working with generative AI, but it is incremental as it builds on existing autocompletion ideas.
The paper proposes interpreting autocompletion as a fundamental interaction concept for human-AI interaction, analyzing its elements across domains like search engines and code completion to inspire design challenges.
Autocompletion is an approach that extends and continues partial user input. We propose to interpret autocompletion as a basic interaction concept in human-AI interaction. We first describe the concept of autocompletion and dissect its user interface and interaction elements, using the well-established textual autocompletion in search engines as an example. We then highlight how these elements reoccur in other application domains, such as code completion, GUI sketching, and layouting. This comparison and transfer highlights an inherent role of such intelligent systems to extend and complete user input, in particular useful for designing interactions with and for generative AI. We reflect on and discuss our conceptual analysis of autocompletion to provide inspiration and a conceptual lens on current challenges in designing for human-AI interaction.