Dr. Xiaojuan Ma Received the ACM CHI Conference on Human Factors in Computing Systems 2025 Best Paper Honorable Mention

The Computer Science and Engineering Department proudly congratulates Dr. Xiaojuan Ma, our Associate Professor, for her co-authored papers titled "Signaling Human Intentions to Service Robots: Understanding the Use of Social Cues during In-Person Conversations", "Towards Human-AI Deliberation: Design and Evaluation of LLM-Empowered Deliberative AI for AI-Assisted Decision-Making", and "'AI Afterlives' as Digital Legacy: Perceptions, Expectations, and Concerns." Dr. Ma's exceptional research has earned her The ACM CHI Conference on Human Factors in Computing Systems 2025 Best Paper Honorable Mention.

Details: CHI '25 Awards

"Signaling Human Intentions to Service Robots: Understanding the Use of Social Cues during In-Person Conversations"

As social service robots become commonplace, it is essential for them to effectively interpret human signals, such as verbal, gesture, and eye gaze, when people need to focus on their primary tasks to minimize interruptions and distractions. Toward such a socially acceptable Human-Robot Interaction, Dr. Ma and co-authors conducted a study (N=24) in an AR-simulated context of a coffee chat. Participants elicited social cues to signal intentions to an anthropomorphic, zoomorphic, grounded technical, or aerial technical robot waiter when they were speakers or listeners. Their findings reveal common patterns of social cues over intentions, the effects of robot morphology on social cue position and conversational role on social cue complexity, and users' rationale in choosing social cues. They offer insights into understanding social cues concerning perceptions of robots, cognitive load, and social context. Additionally, Dr. Ma and co-authors discuss design considerations on approaching, social cue recognition, and response strategies for future service robots.

Details: Signaling Human Intentions to Service Robots: Understanding the Use of Social Cues during In-Person Conversations - CHI '25

"Towards Human-AI Deliberation: Design and Evaluation of LLM-Empowered Deliberative AI for AI-Assisted Decision-Making"

Traditional AI-assisted decision-making systems often provide fixed recommendations that users must either accept or reject entirely, limiting meaningful interaction—especially in cases of disagreement. To address this, Dr. Ma and co-authors introduce Human-AI Deliberation, an approach inspired by human deliberation theories that enables dimension-level opinion elicitation, iterative decision updates, and structured discussions between humans and AI. At the core of this approach is Deliberative AI, an assistant powered by large language models (LLMs) that facilitates flexible, conversational interactions and precise information exchange with domain-specific models. Through a mixed-methods user study, Dr. Ma and co-authors found that Deliberative AI outperforms traditional explainable AI (XAI) systems by fostering appropriate human reliance and improving task performance. By analyzing participant perceptions, user experience, and open-ended feedback, Dr. Ma and co-authors highlight key findings, discuss potential concerns, and explore the broader applicability of this approach for future AI-assisted decision-making systems.

Details: Towards Human-AI Deliberation: Design and Evaluation of LLM-Empowered Deliberative AI for AI-Assisted Decision-Making - CHI '25

"'AI Afterlives' as Digital Legacy: Perceptions, Expectations, and Concerns."

The rise of generative AI technology has sparked interest in using digital information to create AI-generated agents as digital legacy. These agents, often referred to as "AI Afterlives", present unique challenges compared to traditional digital legacy. Yet, there is limited human-centered research on "AI Afterlife" as digital legacy, especially from the perspectives of the individuals being represented by these agents. This paper presents a qualitative study examining users' perceptions, expectations, and concerns regarding AI-generated agents as digital legacy. Dr. Ma and co-authors identify factors shaping people's attitudes, their perceived differences compared with the traditional digital legacy and concerns they might have in real practices. They also examine the design aspects throughout the life cycle and interaction process. Based on these findings, they situate "AI Afterlife" in digital legacy and delve into design implications for maintaining identity consistency and balancing intrusiveness and support in "AI Afterlife" as digital legacy.

Details: "AI Afterlives" as Digital Legacy: Perceptions, Expectations, and Concerns - CHI '25

(From left) Dr. Shuai Ma, Ms. Hanfang Lyu, Dr. Xiaojuan Ma receiving the ACM CHI Conference on Human Factors in Computing Systems 2025 Best Paper Honorable Mention.

Congratulations to Dr. Ma on this remarkable accomplishment!