Session B2 - New Media and Research Inspirations

New Media and Research Inspirations

​​Session Host​: Hanfang Lyu

Design Futures in HCI: Design Thinking for Digital Transformation [Guest Talk]

​​Speaker​​: Zhiyong Fu, Tsinghua University

​​Abstract​​: TBD

Participatory Design in Human-Computer Interaction: Cases, Characteristics, and Lessons

​​Speaker​​: Xiang (Nathan) Qi, The Hong Kong Polytechnic University

​​Abstract​​: Participatory Design (PD) has become increasingly prevalent inHuman-Computer Interaction (HCI) research. However, there remains a lack of comprehensive understanding of how PD has beenused by HCI scholars. To bridge this gap, we sampled PD applicationcases (𝑁 = 185) from the SIGCHI conferences over the past decadeand examined these cases through the dimensions of applicationfeatures (e.g., contexts and functions of PD) and PD principles (e.g.,its political commitment and mutual learning principle). Our analysis reveals the various ways PD has been applied in HCI and howits core features have been or have not been manifested in thesecases. Based on these findings, we reflect on the conceptual understanding of PD within the HCI community and discuss potentialmisconceptions. Ultimately, we hope this work can serve as a usefulreference for HCI researchers and beyond who are interested inincorporating PD into their design and research practices.

SeQR: A User-Friendly and Secure-by-Design Configurator for Enterprise Wi-Fi

​​Speaker​​: Sze Yiu Chau, The Chinese University of Hong Kong

​​Abstract​​: A classic problem in enterprise Wi-Fi is client-side misconfiguration, which enables credential theft via “Evil Twin” (ET) attacks. To mitigate this, we design, develop, and evaluate a new configurator, SeQR, which allows users to effortlessly and securely set up an enterprise Wi-Fi connection. Utilizing existing authenticated channels, SeQR fully automates the client-side enterprise Wi-Fi configuration process with a simple scan, leaving no room for misconfigurations. Specifically, SeQR thwarts ET by making it impossible for users to opt-out from the security-critical certificate validation. We evaluate the efficacy of SeQR on two fronts. First, we implement a prototype of SeQR in Android, and test its functionality and runtime performance. Next, we compare the usability of SeQR against two existing Wi-Fi configuration interfaces of Android in an in-person user study (n=41) with real devices. Our evaluation shows that SeQR achieves noticeable usability improvements over existing designs, and prevents users from misconfiguring.

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

​​Speaker​​: Hanfang Lyu, The Hong Kong University of Science and Technology

​​Abstract​​: As service robots become commonplace, it is essential for them to effectively interpret human signals, such as verbal, gesture, and eye gaze, in social contexts, where people need to focus on their primary tasks to minimize interruptions and distractions. Towards such a socially acceptable Human-Robot Interaction, we conducted a study (N=24) in an AR-simulated context of a coffee chat. Participants elicited social cues for signaling intentions to an anthropomorphic, zoomorphic, grounded technical, or aerial technical robot waiter when they were speakers or listeners. Our findings reveal the 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. We offer insights into understanding social cues concerning perceptions of robots, cognitive load, and social context. Additionally, we discuss design considerations on approaching, social cue recognition, and response strategies for future service robots.

Exploring the Evolvement of User Engagement in Online Creative Community under the Surge of Generative AI: A Case Study of DeviantArt

​​Speaker​​: Kangyu Yuan, The Hong Kong University of Science and Technology

​​Abstract​​: The rise of AI-generated content (AIGC) is transforming online creative communities (OCCs) and posing challenges to their regulation. The interacting behaviors, such as sharing artworks with descriptions, commenting on creations, and creators’ subsequent replying are the essential components of user engagement in these communities. Understanding the influence of AIGC on the evolving user engagement could be helpful for community regulation. In this work, we collect 235K posts and their associated 255K comments from DeviantArt, a large creative community allowing uploading AIGC. Through open coding, we identify five categories of practices in describing and commenting on artworks, respectively. A set of deep learning models are applied to classify the posts and comments. We then combine time series regression analysis, causal inference analysis, and logistic regression analysis, to examine the impact of the surge of AIGC on user engagement. Results suggest that AI-generated artworks show a decreasing emphasis on the content of creations but an increasing trend toward commercial and promotion purposes. AI-generated artworks emphasize less on IP issues than human-created ones, while the awareness of IP issues drops for human-created artworks with the growth of AIGC as well. Although comments with high sentiment valence, for peer bonding or for requesting usage positively predict the reply behavior for human-created artworks, community members are less likely to maintain these interactions as AIGC rises. Finally, we discuss insights and design implications for OCCs.

Designing Highly Accessible XR Interfaces [REP]

​​Speaker​​: Yang Tian, Guangxi University

​​Abstract​​: TBD