Insights into User Interface Innovations from a Design Thinking Workshop at deRSE25

• URL: http://arxiv.org/abs/2508.18784v1
• Date: Tue, 26 Aug 2025 08:11:50 GMT
• Title: Insights into User Interface Innovations from a Design Thinking Workshop at deRSE25
• Authors: Maximilian Frank, Simon Lund,
• Abstract summary: We present insights from a design thinking workshop held at the deRSE25 conference aiming at collaboratively developing innovative user interface concepts for Large Language Models.<n>During the workshop, participants identified common use cases, evaluated the strengths and shortcomings of current LLM interfaces, and created visualizations of new interaction concepts.<n>We describe how these participant-generated ideas advanced our own whiteboard-based UI approach.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Large Language Models have become widely adopted tools due to their versatile capabilities, yet their user interfaces remain limited, often following rigid, linear interaction paradigms. In this paper, we present insights from a design thinking workshop held at the deRSE25 conference aiming at collaboratively developing innovative user interface concepts for LLMs. During the workshop, participants identified common use cases, evaluated the strengths and shortcomings of current LLM interfaces, and created visualizations of new interaction concepts emphasizing flexible context management, dynamic conversation branching, and enhanced mechanisms for user control. We describe how these participant-generated ideas advanced our own whiteboard-based UI approach. The ongoing development of this interface is guided by the human-centered design process - an iterative, user-focused methodology that emphasizes continuous refinement through user feedback. Broader implications for future LLM interface development are discussed, advocating for increased attention to UI innovation grounded in user-centered design principles.

Related papers

• Generative Interfaces for Language Models [70.25765232527762]
  We propose a paradigm in which large language models (LLMs) respond to user queries by proactively generating user interfaces (UIs)<n>Our framework leverages structured interface-specific representations and iterative refinements to translate user queries into task-specific UIs.<n>Results show that generative interfaces consistently outperform conversational ones, with humans preferring them in over 70% of cases.
  arXiv  Detail & Related papers  (2025-08-26T17:43:20Z)
• Dynamic Scoring with Enhanced Semantics for Training-Free Human-Object Interaction Detection [51.52749744031413]
  Human-Object Interaction (HOI) detection aims to identify humans and objects within images and interpret their interactions.<n>Existing HOI methods rely heavily on large datasets with manual annotations to learn interactions from visual cues.<n>We propose a novel training-free HOI detection framework for Dynamic Scoring with enhanced semantics.
  arXiv  Detail & Related papers  (2025-07-23T12:30:19Z)
• Interactive Reasoning: Visualizing and Controlling Chain-of-Thought Reasoning in Large Language Models [54.85405423240165]
  We introduce Interactive Reasoning, an interaction design that visualizes chain-of-thought outputs as a hierarchy of topics.<n>We implement interactive reasoning in Hippo, a prototype for AI-assisted decision making in the face of uncertain trade-offs.
  arXiv  Detail & Related papers  (2025-06-30T10:00:43Z)
• Designing Effective LLM-Assisted Interfaces for Curriculum Development [0.0]
  Large Language Models (LLMs) have the potential to transform the way a dynamic curriculum can be delivered.<n>This paper introduces two novel User Interface (UI) designs, UI Predefined and UI Open.<n>By reducing the reliance on intricate prompt engineering, these UIs improve usability, streamline interaction, and lower workload.
  arXiv  Detail & Related papers  (2025-06-13T13:21:53Z)
• MC-LLaVA: Multi-Concept Personalized Vision-Language Model [51.645660375766575]
  This paper proposes the first multi-concept personalization paradigm, MC-LLaVA.<n>MC-LLaVA employs a multi-concept instruction tuning strategy, effectively integrating multiple concepts in a single training step.<n> Comprehensive qualitative and quantitative experiments demonstrate that MC-LLaVA can achieve impressive multi-concept personalized responses.
  arXiv  Detail & Related papers  (2025-03-24T16:32:17Z)
• MC-LLaVA: Multi-Concept Personalized Vision-Language Model [51.645660375766575]
  This paper proposes the first multi-concept personalization paradigm, MC-LLaVA.<n>MC-LLaVA employs a multi-concept instruction tuning strategy, effectively integrating multiple concepts in a single training step.<n> Comprehensive qualitative and quantitative experiments demonstrate that MC-LLaVA can achieve impressive multi-concept personalized responses.
  arXiv  Detail & Related papers  (2024-11-18T16:33:52Z)
• Generative AI in Multimodal User Interfaces: Trends, Challenges, and Cross-Platform Adaptability [0.0]
  Generative AI emerges as a key driver in reshaping user interfaces. This paper explores the integration of generative AI in modern user interfaces. It focuses on multimodal interaction, cross-platform adaptability and dynamic personalization.
  arXiv  Detail & Related papers  (2024-11-15T14:49:58Z)
• Constraining Participation: Affordances of Feedback Features in Interfaces to Large Language Models [49.74265453289855]
  Large language models (LLMs) are now accessible to anyone with a computer, a web browser, and an internet connection via browser-based interfaces. This paper examines the affordances of interactive feedback features in ChatGPT's interface, analysing how they shape user input and participation in iteration.
  arXiv  Detail & Related papers  (2024-08-27T13:50:37Z)
• Online Training of Large Language Models: Learn while chatting [23.995637621755083]
  This paper introduces a novel interaction paradigm-'Online Training using External Interactions'-that merges the benefits of persistent, real-time model updates with the flexibility for individual customization.
  arXiv  Detail & Related papers  (2024-03-04T10:00:55Z)
• Large Language User Interfaces: Voice Interactive User Interfaces powered by LLMs [5.06113628525842]
  We present a framework that can serve as an intermediary between a user and their user interface (UI) We employ a system that stands upon textual semantic mappings of UI components, in the form of annotations. Our engine can classify the most appropriate application, extract relevant parameters, and subsequently execute precise predictions of the user's expected actions.
  arXiv  Detail & Related papers  (2024-02-07T21:08:49Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.