A dataset revealing high inter-designer disagreement on UI preferences motivates a sample-efficient method that personalizes generative interfaces by embedding new users in the space of prior designers, outperforming baselines in both modeling and user preference.
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Youth on Character.AI use chatbots for emotional restoration, creative exploration, and identity transformation, yielding a new three-intent framework and seven-archetype taxonomy from Discord discourse analysis.
A survey of 457 papers yields a six-dimensional design space for abstraction in interactive systems that reframes gulfs of execution and evaluation while articulating cognitive and design processes for bridging abstraction gaps.
Users adapt existing workflow patterns to create custom features in an AI email system via conversation, turning the inbox into a user-shaped flexible data layer while managing risks like mis-specified behavior through ongoing oversight.
AI alignment must move beyond assuming users have fully formed goals and instead provide active cognitive support to help form and refine intent over time.
Feasibility study shows simple VR objects can work for C-PTSD exposure, design process integrates into therapy, but developer involvement adds emotional burden; provides methodological recommendations for safe implementation.
Intent Lenses infer capture-time user intent from photos via LLMs to create dynamic, reusable interactive objects that generate and organize structured visual notes for later sensemaking.
Workshops with over 100 creative writers produced metaphors and four themes for language model governance that favor consent-driven, smaller open models encoding community values.
Generative interfaces let LLMs create task-specific UIs that users prefer up to 72% more than standard chat responses across tested tasks.
PSI uses a shared personal-context bus to publish state and write-back affordances, turning isolated AI-generated modules into synchronized, chat-accessible instruments.
Barriers like non-adaptable data formats, legacy security, and cognitive skill gaps hinder GenUI adoption, requiring new scientific methods for evaluation and usage tracking.
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Generative Interfaces for Language Models
Generative interfaces let LLMs create task-specific UIs that users prefer up to 72% more than standard chat responses across tested tasks.