Personalizing Personal Health Interfaces: Co-Design with Generative AI
- URL: http://arxiv.org/abs/2609.15046v1
- Date: Mon, 14 Sep 2026 05:06:24 GMT
- Title: Personalizing Personal Health Interfaces: Co-Design with Generative AI
- Abstract summary: We conducted a co-design study where 14 participants redesigned Google and Apple Health interfaces using Figma Make.<n>Generative co-design let participants create interfaces directly, blurring the boundary between intentions and model defaults.<n>We discuss implications for preserving agency and flexible user-directed interfaces.
- Score: 11.084544865606034
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Personal health interfaces present wellbeing data through standardized dashboards that rarely fit how people interpret or act on it. Personalizing them to what people would like to see for themselves often requires design and technical expertise, a barrier that generative AI may potentially lower. Therefore, we ask what designs emerge and how it enables and constrains the design process. We conducted a co-design study where 14 participants redesigned Google and Apple Health interfaces using Figma Make. Participants reimagined interfaces that supported personal context, future planning, and interactive experiences, yet conversational AI designs converged around chat-window conventions. AI helped materialize loosely articulated ideas, but model defaults and generation latency shaped iteration. The process more readily operationalized interpretability and accountability than privacy, trust, and emotional safety. Generative co-design let participants create interfaces directly, blurring the boundary between intentions and model defaults. We discuss implications for preserving agency and flexible user-directed interfaces.
Related papers
- Solaris: Towards Interfaces That Are Generated, Not Coded [61.80943145058668]
We introduce Solaris, an interface world model that generates an interactive UI directly, frame by frame, in response to user actions.<n> Solaris treats mouse interactions as conditioning signals and autoregressively synthesizes the resulting visual state at interactive speeds.<n>A language model complements the visual world model by interpreting user intent and specifying how interactions should affect the generated environment.
arXiv Detail & Related papers (2026-09-01T06:10:49Z) - Exploring the "Banality" of Deception in Generative AI [2.706390545056605]
Simone Natale explores banality as a lens through which to reason through deception in generative AI experiences.<n>We argue that this perspective could lead to future work for introducing friction to safeguard users from deception in generative AI interactions.
arXiv Detail & Related papers (2026-05-07T22:49:55Z) - Human-AI Coordination Zones: A Framework for Designing Human-in-the-Loop Experiences with Agentic AI [6.1337557618676195]
We introduce a framework defining human-AI coordination as the interplay of three dimensions.<n>We contribute mid-level tools including coordination zones, an input taxonomy, and design patterns.<n>The framework can be applied generatively to design experiences, analytically to evaluate existing ones, and communicatively to articulate ideas across stakeholders.
arXiv Detail & Related papers (2026-05-01T01:03:08Z) - Resisting Humanization: Ethical Front-End Design Choices in AI for Sensitive Contexts [1.1565654851982565]
We argue that humanization in AI front-end design is a value-driven choice that profoundly shapes users' mental models.<n>We discuss two AI systems developed by Chayn, a nonprofit organization supporting survivors of gender-based violence.
arXiv Detail & Related papers (2026-03-25T22:39:16Z) - Neural Transparency: Mechanistic Interpretability Interfaces for Anticipating Model Behaviors for Personalized AI [9.383958408772694]
We introduce an interface that enables neural transparency by exposing language model internals during chatbots design.<n>Our approach extracts behavioral trait vectors by computing differences in neural activations between contrastive system prompts that elicit opposing behaviors.<n>This work offers a path for how interpretability can be operationalized for non-technical users, establishing a foundation for safer, more aligned human-AI interactions.
arXiv Detail & Related papers (2025-10-31T20:03:52Z) - 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 up to a 72% improvement in human preference.
arXiv Detail & Related papers (2025-08-26T17:43:20Z) - ThematicPlane: Bridging Tacit User Intent and Latent Spaces for Image Generation [49.805992099208595]
We introduce ThematicPlane, a system that enables users to navigate and manipulate high-level semantic concepts.<n>This interface bridges the gap between tacit creative intent and system control.
arXiv Detail & Related papers (2025-08-08T06:57:14Z) - 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) - Survey of User Interface Design and Interaction Techniques in Generative AI Applications [79.55963742878684]
We aim to create a compendium of different user-interaction patterns that can be used as a reference for designers and developers alike.
We also strive to lower the entry barrier for those attempting to learn more about the design of generative AI applications.
arXiv Detail & Related papers (2024-10-28T23:10:06Z) - The System Model and the User Model: Exploring AI Dashboard Design [79.81291473899591]
We argue that sophisticated AI systems should have dashboards, just like all other complicated devices.
We conjecture that, for many systems, the two most important models will be of the user and of the system itself.
Finding ways to identify, interpret, and display these two models should be a core part of interface research for AI.
arXiv Detail & Related papers (2023-05-04T00:22:49Z)
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.