In argumentative writing, writers must brainstorm hierarchical writing goals, ensure the persuasiveness of their arguments, and revise and organize their plans through drafting. Recent advances in large language models (LLMs) have made interactive text generation through a chat interface (e.g., ChatGPT) possible. However, this approach often neglects implicit writing context and user intent, lacks support for user control and autonomy, and provides limited assistance for sensemaking and revising writing plans. To address these challenges, we introduce VISAR, an AI-enabled writing assistant system designed to help writers brainstorm and revise hierarchical goals within their writing context, organize argument structures through synchronized text editing and visual programming, and enhance persuasiveness with argumentation spark recommendations. VISAR allows users to explore, experiment with, and validate their writing plans using automatic draft prototyping. A controlled lab study confirmed the usability and effectiveness of VISAR in facilitating the argumentative writing planning process.

pdf/visar-a-human-ai-argumentative-writing-assistant-with-visual-29vmz6or.pdf

VISAR: A Human-AI Argumentative Writing Assistant with Visual Programming and Rapid Draft Prototyping

In argumentative writing, writers must brainstorm hierarchical writing goals, ensure the persuasiveness of their arguments, and revise and organize their plans through drafting. Recent advances in large language models (LLMs) have made interactive text generation through a chat interface (e.g., ChatGPT) possible. However, this approach often neglects implicit writing context and user intent, lacks support for user control and autonomy, and provides limited assistance for sensemaking and revising writing plans. To address these challenges, we introduce VISAR, an AI-enabled writing assistant system designed to help writers brainstorm and revise hierarchical goals within their writing context, organize argument structures through synchronized text editing and visual programming, and enhance persuasiveness with argumentation spark recommendations. VISAR allows users to explore, experiment with, and validate their writing plans using automatic draft prototyping. A controlled lab study confirmed the usability and effectiveness of VISAR in facilitating the argumentative writing planning process. 

pdf/visar-a-human-ai-argumentative-writing-assistant-with-visual-5a5fmvyyme.pdf

Thanks to their generative capabilities, large language models (LLMs) have become an invaluable tool for creative processes. These models have the capacity to produce hundreds and thousands of visual and textual outputs, offering abundant inspiration for creative endeavors. But are we harnessing their full potential? We argue that current interaction paradigms fall short, guiding users towards rapid convergence on a limited set of ideas, rather than empowering them to explore the vast latent design space in generative models. To address this limitation, we propose a framework that facilitates the structured generation of design space in which users can seamlessly explore, evaluate, and synthesize a multitude of responses. We demonstrate the feasibility and usefulness of this framework through the design and development of an interactive system, Luminate, and a user study with 14 professional writers. Our work advances how we interact with LLMs for creative tasks, introducing a way to harness the creative potential of LLMs.

pdf/luminate-structured-generation-and-exploration-of-design-20cr7l576b.pdf

Luminate: Structured Generation and Exploration of Design Space with Large Language Models for Human-AI Co-Creation

Among creative professionals, Generative Artificial Intelligence (GenAI) has sparked excitement over its capabilities and fear over unanticipated consequences. How does GenAI impact User Experience Design (UXD) practice, and are fears warranted? We interviewed 20 UX Designers, with diverse experience and across companies (startups to large enterprises). We probed them to characterize their practices, and sample their attitudes, concerns, and expectations. We found that experienced designers are confident in their originality, creativity, and empathic skills, and find GenAI's role as assistive. They emphasized the unique human factors of "enjoyment" and "agency", where humans remain the arbiters of "AI alignment''. However, skill degradation, job replacement, and creativity exhaustion can adversely impact junior designers. We discuss implications for human-GenAI collaboration, specifically copyright and ownership, human creativity and agency, and AI literacy and access. Through the lens of responsible and participatory AI, we contribute a deeper understanding of GenAI fears and opportunities for UXD. 

User Experience Design Professionals’ Perceptions of Generative Artificial Intelligence

Recently, artificial intelligence (AI) has been introduced into a variety of consumer applications for creative work. Although AI-driven features in design tooling are nascent, there is growing interest in utilizing AI to support user experience (UX) workflows. In this case study, we surveyed industry UX professionals to understand how they perceive AI-driven assists in their tools, their concerns about accepting AI in design tools and which design-related workflows could be promising for future research. Our results suggest that UX professionals are overall positive about AI-driven features in design tools; looking to AI as a creative partner to iterate with and as an assistant with mundane tasks. We offer practical directions for the future of AI in UX tooling, but caution against developing tools that do not sufficiently address UX professionals’ concerns around bias and trust.

Exploring the future of design tooling: The role of artificial intelligence in tools for user experience professionals

User interface (UI) and user experience (UX) design have become an indispensable part of today’s tech industry. Recently, much progress has been made in machine-learning-enabled design support tools for UX designers. However, few of these tools have been adopted by practitioners. To learn the underlying reasons and understand user needs for bridging this gap, we conducted a retrospective analysis with 8 UX professionals to understand their practice and identify opportunities for future research. We found that the current AI-enabled systems to support UX work mainly work on graphical interface elements, while design activities that involve more ‘design thinking” such as user interviews and user testings are more helpful for designers. Many current systems were also designed for overly-simplistic and generic use scenarios. We identified 4 areas in the UX workflow that can benefit from additional AI-enabled assistance: design inspiration search, design alternative exploration, design system customization, and design guideline violation check.

Bridging the Gap Between UX Practitioners’ Work Practices and AI-Enabled Design Support Tools

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Bridging the Gap Between UX Practitioners’ Work Practices and AI-Enabled Design Support Tools