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Zhuohao Jerry Zhang

Publications and source records attributed to Zhuohao Jerry Zhang.

5 recordsLinked to original sources

A11yShape: AI-Assisted 3-D Modeling for Blind and Low-Vision Programmers

Building 3-D models is challenging for blind and low-vision (BLV) users due to the inherent complexity of 3-D models and the lack of support for non-visual interaction in existing tools. To address this issue, we introduce A11yShape, a novel system designed to help BLV users who possess basic programming skills understand, modify, and iterate on 3-D models. A11yShape leverages LLMs and integrates with OpenSCAD, a popular open-source editor that generates 3-D models from code. Key functionalities of A11yShape include accessible descriptions of 3-D models, version control to track changes in models and code, and a hierarchical representation of model components. Most importantly, A11yShape employs a cross-representation highlighting mechanism to synchronize semantic selections across all model representations -- code, semantic hierarchy, AI description, and 3-D rendering. We conducted a multi-session user study with four BLV programmers, where, after an initial tutorial session, participants independently completed 12 distinct models across two testing sessions, achieving results that aligned with their own satisfaction. The result demonstrates that participants were able to comprehend provided 3-D models, as well as independently create and modify 3-D models -- tasks that were previously impossible without assistance from sighted individuals.

cs.HC↗

SlideAudit: A Dataset and Taxonomy for Automated Evaluation of Presentation Slides

Automated evaluation of specific graphic designs like presentation slides is an open problem. We present SlideAudit, a dataset for automated slide evaluation. We collaborated with design experts to develop a thorough taxonomy of slide design flaws. Our dataset comprises 2400 slides collected and synthesized from multiple sources, including a subset intentionally modified with specific design problems. We then fully annotated them using our taxonomy through strictly trained crowdsourcing from Prolific. To evaluate whether AI is capable of identifying design flaws, we compared multiple large language models under different prompting strategies, and with an existing design critique pipeline. We show that AI models struggle to accurately identify slide design flaws, with F1 scores ranging from 0.331 to 0.655. Notably, prompting techniques leveraging our taxonomy achieved the highest performance. We further conducted a remediation study to assess AI's potential for improving slides. Among 82.0% of slides that showed significant improvement, 87.8% of them were improved more with our taxonomy, further demonstrating its utility.

cs.HC↗

From Interaction to Impact: Towards Safer AI Agents Through Understanding and Evaluating Mobile UI Operation Impacts

With advances in generative AI, there is increasing work towards creating autonomous agents that can manage daily tasks by operating user interfaces (UIs). While prior research has studied the mechanics of how AI agents might navigate UIs and understand UI structure, the effects of agents and their autonomous actions-particularly those that may be risky or irreversible-remain under-explored. In this work, we investigate the real-world impacts and consequences of mobile UI actions taken by AI agents. We began by developing a taxonomy of the impacts of mobile UI actions through a series of workshops with domain experts. Following this, we conducted a data synthesis study to gather realistic mobile UI screen traces and action data that users perceive as impactful. We then used our impact categories to annotate our collected data and data repurposed from existing mobile UI navigation datasets. Our quantitative evaluations of different large language models (LLMs) and variants demonstrate how well different LLMs can understand the impacts of mobile UI actions that might be taken by an agent. We show that our taxonomy enhances the reasoning capabilities of these LLMs for understanding the impacts of mobile UI actions, but our findings also reveal significant gaps in their ability to reliably classify more nuanced or complex categories of impact.

cs.HC↗

OmniQuery: Contextually Augmenting Captured Multimodal Memory to Enable Personal Question Answering

People often capture memories through photos, screenshots, and videos. While existing AI-based tools enable querying this data using natural language, they only support retrieving individual pieces of information like certain objects in photos, and struggle with answering more complex queries that involve interpreting interconnected memories like sequential events. We conducted a one-month diary study to collect realistic user queries and generated a taxonomy of necessary contextual information for integrating with captured memories. We then introduce OmniQuery, a novel system that is able to answer complex personal memory-related questions that require extracting and inferring contextual information. OmniQuery augments individual captured memories through integrating scattered contextual information from multiple interconnected memories. Given a question, OmniQuery retrieves relevant augmented memories and uses a large language model (LLM) to generate answers with references. In human evaluations, we show the effectiveness of OmniQuery with an accuracy of 71.5%, outperforming a conventional RAG system by winning or tying for 74.5% of the time.

cs.HC↗

ChartA11y: Designing Accessible Touch Experiences of Visualizations with Blind Smartphone Users

We introduce ChartA11y, an app developed to enable accessible 2-D visualizations on smartphones for blind users through a participatory and iterative design process involving 13 sessions with two blind partners. We also present a design journey for making accessible touch experiences that go beyond simple auditory feedback, incorporating multimodal interactions and multisensory data representations. Together, ChartA11y aimed at providing direct chart accessing and comprehensive chart understanding by applying a two-mode setting: a semantic navigation framework mode and a direct touch mapping mode. By re-designing traditional touch-to-audio interactions, ChartA11y also extends to accessible scatter plots, addressing the under-explored challenges posed by their non-linear data distribution. Our main contributions encompass the detailed participatory design process and the resulting system, ChartA11y, offering a novel approach for blind users to access visualizations on their smartphones.

cs.HC↗