CHCI participation at IUI 2025

The ACM Conference on Intelligent User Interfaces (ACM IUI) 2025 is the 30th annual premiere venue, where researchers and practitioners meet and discuss state-of-the-art advances at the intersection of Artificial Intelligence (AI) and Human-Computer Interaction (HCI). It will be held in Cagliari, Italy, from March 24-27, 2025.

CHCI members have four full papers at the conference. Here are the details:

1. KHAIT: K-9 Handler Artificial Intelligence Teaming for Collaborative Sensemaking

2. Enhancing Immersive Sensemaking with Gaze-Driven Recommendation Cues

3. Users’ Mental Models of Generative AI Chatbot Ecosystems

4. CLEAR: Towards Contextual LLM-Empowered Privacy Policy Analysis and Risk Generation for Large Language Model Applications

KHAIT: K-9 Handler Artificial Intelligence Teaming for Collaborative Sensemaking

Matthew Wilchek, Linhan Wang, Sally Dickinson, Erica Feuerbacher, Kurt Luther, Feras A. Batarseh

In urban search and rescue (USAR) operations, communication between handlers and specially trained canines is crucial but often complicated by challenging environments and the specific behaviors canines are trained to exhibit when detecting a person. Since a USAR canine often works out of sight of the handler, the handler lacks awareness of the canine's location and situation, known as the 'sensemaking gap.' In this paper, we propose KHAIT, a novel approach to close the sensemaking gap and enhance USAR effectiveness by integrating object detection-based Artificial Intelligence (AI) and Augmented Reality (AR). Equipped with AI-powered cameras, edge computing, and AR headsets, KHAIT enables precise and rapid object detection from a canine's perspective, improving survivor localization. We evaluate this approach in a real-world USAR environment, demonstrating an average survival allocation time decrease of 22%, enhancing the speed and accuracy of operations.

Enhancing Immersive Sensemaking with Gaze-Driven Recommendation Cues

Ibrahim Tahmid, Chris North, Kylie Davidson, Kirsten Whitley, Doug Bowman

Sensemaking is a complex task that places a heavy cognitive demand on individuals. With the recent surge in data availability, making sense of vast amounts of information has become a significant challenge for many professionals, such as intelligence analysts. Immersive technologies such as mixed reality offer a potential solution by providing virtually unlimited space to organize data. However, processing, filtering relevant information, and synthesizing insights remain difficult.

We proposed using eye-tracking data from mixed reality head-worn displays to derive the analyst's perceived interest in documents and words and convey that part of the mental model to the analyst. The global interest of the documents is reflected in their color and order on the list. In contrast, the local interest of the documents is used to generate focused recommendations for a document. To evaluate these recommendation cues, we conducted a user study with two conditions: a gaze-aware system, EyeST, and a ``Freestyle'' system without gaze-based visual cues. Our findings reveal that the EyeST helped analysts stay on track by reading more essential information while avoiding distractions. However, this came at the cost of reduced focused attention and perceived system performance.

The results of our study highlight the need for explainable AI in human-AI collaborative sensemaking to build user trust and encourage the integration of AI outputs into the immersive sensemaking process. Based on our findings, we offer a set of guidelines for designing gaze-driven recommendation cues in an immersive environment.

Users’ Mental Models of Generative AI Chatbot Ecosystems

Xingyi Wang, Xiaozheng Wang, Sunyup Park, Yaxing Yao

The capability of GenAI-based chatbots, such as ChatGPT and Gemini, has expanded quickly in recent years, turning them into GenAI Chatbot Ecosystems. Yet, users' understanding of how such ecosystems work remains unknown. In this paper, we investigate users' mental models of how GenAI Chatbot Ecosystems work. This is an important question because users' mental models guide their behaviors, including making decisions that impact their privacy. Through 21 semi-structured interviews, we uncovered users' four mental models towards first-party (e.g., Google Gemini) and third-party (e.g., ChatGPT) GenAI Chatbot Ecosystems. These mental models centered around the role of the chatbot in the entire ecosystem. We further found that participants held a more consistent and simpler mental model towards third-party ecosystems than the first-party ones, resulting in higher trust and fewer concerns towards the third-party ecosystems. We discuss the design and policy implications based on our results.

CLEAR: Towards Contextual LLM-Empowered Privacy Policy Analysis and Risk Generation for Large Language Model Applications

Chaoran Chen, Daodao Zhou, Yanfang Ye, Toby Jia-Jun Li, Yaxing Yao

The rise of end-user applications powered by large language models (LLMs), including both conversational interfaces and add-ons to existing graphical user interfaces (GUIs), introduces new privacy challenges. However, many users remain unaware of the risks. This paper explores methods to increase user awareness of privacy risks associated with LLMs in end-user applications. We conducted five co-design workshops to uncover user privacy concerns and their demand for contextual privacy information within LLMs. Based on these insights, we developed CLEAR (Contextual LLM-Empowered Privacy Policy Analysis and Risk Generation), a just-in-time contextual assistant designed to help users identify sensitive information, summarize relevant privacy policies, and highlight potential risks when sharing information with LLMs. We evaluated the usability and usefulness of CLEAR across two example domains: ChatGPT and the Gemini plugin in Gmail. Our findings demonstrated that CLEAR is easy to use and improves users' understanding of data practices and privacy risks. We also discussed LLM's duality in posing and mitigating privacy risks, offering design and policy implications.