Examining Autocompletion as a Basic Concept for Interaction with Generative AI
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Abstract (arXiv)
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.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 18 Jan 2022.
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Citing papers per year: 2022: 1; 2023: 1; 2024: 1; 2025: 1.
4 papers.
- 2022An Equivalence Between Data Poisoning and Byzantine Gradient Attackscited by 16
- 2023CatAlyst: Domain-Extensible Intervention for Preventing Task Procrastination Using Large Generative Modelscited by 10
- 2024ChaI-TeA: A Benchmark for Evaluating Autocompletion of Interactions with LLM-based Chatbotscited by 1
- 2025Proactive Agentic Whiteboards: Enhancing Diagrammatic Learningcited by 0
- 2025Proactive Agentic Whiteboards: Enhancing Diagrammatic Learning1 Dec 2025
- 2024ChaI-TeA: A Benchmark for Evaluating Autocompletion of Interactions with LLM-based Chatbots24 Dec 2024
- 2023CatAlyst: Domain-Extensible Intervention for Preventing Task Procrastination Using Large Generative Models11 Feb 2023
- 2022An Equivalence Between Data Poisoning and Byzantine Gradient Attacks17 Feb 2022