AI tools are increasingly used for UI evaluation, yet most treat evaluation as a single-pass, black-box process that limits both effective model reasoning and human involvement. Grounded in Situation Awareness (SA) theory, we reframe single-screen heuristic evaluation of mobile UIs as stagewise human–AI co-critique, structuring evaluation into three editable stages: Perception (what is on the screen), Comprehension (what elements mean and do), and Projection (what problems and fixes follow). We instantiate this framing in Criticmate, an interactive system that exposes intermediate reasoning artifacts for intervention. Across offline benchmarks and a controlled user study, we show that stagewise co-critique yields more expert-like and better balanced critiques than single-pass approaches, while supporting higher trust and engagement without reducing perceived autonomy.

Eye blink detection serves as a crucial biomarker for evaluating both physical and mental states, garnering considerable attention in biometric and video-based studies. Among various methods, video-based eye blink detection has been particularly favored due to its non-invasive nature, enabling broader applications. However, capturing eye blinks from different camera angles poses significant challenges, primarily because the eye region is relatively small and eye blinks occur rapidly, necessitating a robust detection algorithm. To address these challenges, we introduce Dual Embedding Video Vision Transformer (DEViViT), a novel approach for eye blink detection that employs two different embedding strategies: (i) tubelet embedding and (ii) residual embedding. Each embedding can capture large and subtle changes within the eye movement sequence respectively. We rigorously evaluate our proposed method using HUST-LEBW, a publicly available dataset, as well as our newly collected multi-angle eye blink dataset (MAEB). The results indicate that the proposed model consistently outperforms existing methods across both datasets, with notably minor performance variations depending on the camera angles.

Mandala coloring is popular among many people, from children to adults, and many studies have revealed its benefits in mental well-being. However, our preliminary study results reveal difficulties in mandala coloring tasks, such as selecting harmonious colors/areas and envisioning how each selection affects the final output. This paper presents Colorbo, an interactive system based on human-AI collaboration to envision mandala coloring. Colorbo and its user colorize a mandala individually by watching each other work. The user shows a colored mandala in progress, and Colorbo fills in the remaining areas by analyzing the patterns and color combinations of the user’s image. Colorbo then projects the complete mandala onto the paper the user is colorizing, and the user continues coloring by envisioning the outcome based on images from Colorbo. We conducted a within-subject study to investigate the effectiveness of Colorbo. Our quantitative and qualitative analysis results show the positive experiences of the participants, concerns regarding the coloring behavior with Colorbo, and their preferred projection method for envisioning a mandala. Finally, based on the findings, we discuss the design implications for human-AI collaboration in the area of art.

Techno-spiritual practices refer to the use of digital technologies to support various spiritual activities, such as scripture reading. While prior human-computer interaction studies largely focus on understanding techno-spiritual practices in both personal and ministry environments, there is a lack of design research that explores novel design opportunities, based on longitudinal field deployment. As an important techno-spiritual practice, this work focuses on scripture reading and investigates the design space of "social scripture reading," as it is often organized into small groups for successful behavior maintenance. We designed and evaluated BibleCell, a social scripture reading tool that supports personalized reading plans, scripture reading, and social sharing. After the third year of deployment, we performed a two-month user study in Korean Protestant churches to deepen our understanding of techno-spiritual practices in social contexts via in-depth interviews (n = 27). We report the major themes of social techno-spiritual practices, such as social motivators, social interaction patterns, and leadership roles. We discuss our findings using a novel design concept of social-spiritual awareness that considers both the social and spiritual aspects of interactions in social computing systems.

Instant access and gratification make it difficult for us to self-limit the use of smartphone apps. We hypothesize that a slight increase in the interaction cost of accessing an app could successfully discourage app use. We propose a proactive intervention that requests users to perform a simple lockout task (e.g., typing a fixed length number) whenever a target app is launched. We investigate how a lockout task with varying workloads (i.e., pause only without number input, 10-digit input, and 30-digit input) influence a user's decision making, by a 3-week, in-situ experiment with 40 participants. Our findings show that even the pause-only task that requires a user to press a button to proceed discouraged an average of 13.1% of app use, and the 30-digit-input task discouraged 47.5%. We derived determinants of app use and non-use decision making for a given lockout task. We further provide implications for persuasive technology design for discouraging undesired behaviors.

Prior studies have addressed many negative aspects of mobile distractions in group activities. In this paper, we present Lock n' LoL. This is an application designed to help users focus on their group activities by allowing group members to limit their smartphone usage together. In particular, it provides synchronous social awareness of each other's limiting behavior. This synchronous social awareness can arouse feelings of connectedness among group members and can mitigate social vulnerability due to smartphone distraction (e.g., social exclusion) that often results in poor social experiences. After following an iterative prototyping process, we conducted a large-scale user study (n = 976) via real field deployment. The study results revealed how the participants used Lock n' LoL in their diverse contexts and how Lock n' LoL helped them to mitigate smartphone distractions.

This article aims to deepen understanding of these mass interactions in online sports viewing through studying Naver Sports, the largest online sports viewing service in Korea. We examined the diverse aspects of mass interactions, including interactive experiences, usage motives, and relationships between usage patterns and motives, through analysis of almost 6 million chats from Naver Sports and from self-reporting survey data from 1,123 users. First, we found that online sports viewing provides unique interactive experiences when compared to other settings such as offline sports viewing and social TV viewing with friends. Second, we found the key motives inspiring online sports viewing include the following: sharing feelings/thoughts, wanting to be entertained, sharing information, and wanting to feel membership in a group. Third, these motives were significantly related to specific usage patterns. Finally, we explored how the study’s key findings can offer practical design implications to enhance online sports viewing services, and to show system designers how to support particular usage patterns to better accommodate specific user motives.

Our preliminary study reveals that individuals use various management strategies for limiting smartphone use, ranging from keeping smartphones out of reach to removing apps. However, we also found that users often had difficulties in maintaining their chosen management strategies due to lack of self-regulation. In this paper, we present NUGU, a group-based intervention app for improving self-regulation of limiting smartphone use through leveraging social support: groups of people limit their use together by sharing their limiting information. NUGU is designed based on social cognitive theory, and it has been developed iteratively through two pilot tests. Our three-week user study (n = 62) demonstrated that compared with its non-social counterpart, the NUGU users' usage amount significantly decreased and their perceived level of managing disturbances improved. Furthermore, our exit interview confirmed that NUGU's design elements are effective for achieving limiting goals.

We consider participatory parental mediation in which children engage with their parents in activities that encourage both parents and children to participate in co-learning of digital media use. To this end, we developed FamiLync, a mobile service that treats use-limiting as a family activity and provides the family with a virtual public space to foster social awareness and improve self-regulation. A three-week user study conducted with twelve families in Korea (17 parents and 18 teenagers) showed that FamiLync improves mutual understanding of usage behavior, thereby providing common grounds for parental mediation. Further, parents actively participated in use-limiting with their children, which significantly increased the children's desire to participate. As a consequence, parental mediation methods and parent-child interaction in relation to smartphone usage changed appreciably, and the participants smartphone usage amount significantly decreased.

The negative aspects of smartphone overuse on young adults, such as sleep deprivation and attention deficits, are being increasingly recognized recently. This emerging issue motivated us to analyze the usage patterns related to smartphone overuse. We investigate smartphone usage for 95 college students using surveys, logged data, and interviews. We first divide the participants into risk and non-risk groups based on self-reported rating scale for smartphone overuse. We then analyze the usage data to identify between-group usage differences, which ranged from the overall usage patterns to app-specific usage patterns. Compared with the non-risk group, our results show that the risk group has longer usage time per day and different diurnal usage patterns. Also, the risk group users are more susceptible to push notifications, and tend to consume more online content. We characterize the overall relationship between usage features and smartphone overuse using analytic modeling and provide detailed illustrations of problematic usage behaviors based on interview data.

The social network represents various relationships between users, and community discovery is one of the most popular tasks analyzing these relationships. The relationships are either explicit (e.g., friends) or implicit, and we focus on community discovery with implicit relationships. Here, the key issue is how to extract the relationships between users. A user is typically represented by his/her profile, and the similarity between user profiles is measured. In most algorithms, a user has a single profile aggregating all the information about the user. For example, a profile for a researcher is a list of papers he/she wrote. This setting, however, oversimplifies the multiple characteristics of a man since individual characteristics are mixed up. In this paper, we propose the notion and method of profile decomposition, which divides a profile into a set of sub-profiles so that they represent individual characteristics precisely. Then, we develop a community discovery algorithm, which we call DecompClus, based on profile decomposition. Using a real data set of CiteULike, we show that our proposed algorithm can precisely distinguish multiple research interests of a user and discover communities corresponding to each interest, whereas previous algorithms cannot. Overall, profile decomposition enables us to find fine-granularity user communities, thus improving the accuracy of community discovery.

Collaborative filtering relies on numerical ratings for recommendations. While users consider various aspects of content as a basis of their evaluation, a numeric rating provides only an aggregated report of final assessment. The performance of a collaborative recommender system could be enhanced if

Political views frequently conflict in the coverage of contentious political issues, potentially causing serious social problems. We present a novel social annotation analysis approach for identification of news articles' political orientation. The approach focuses on the behavior of individual commenters. It uncovers commenters' sentiment patterns towards political news articles, and predicts the political orientation from the sentiments expressed in the comments. It takes advantage of commenters' participation as well as their knowledge and intelligence condensed in the sentiment of comments, thereby greatly reduces the high complexity of political view identification. We conduct extensive study on commenters' behaviors, and discover predictive commenters showing a high degree of regularity in their sentiment patterns. We develop and evaluate sentiment pattern-based methods for political view identification.