As people increasingly turn to AI for personal deliberation beyond task-oriented assistance, concerns about sycophancy in these value-laden contexts have grown. Unlike human flattery, which is intentional and self-interested, AI sycophancy emerges as a byproduct of RLHF’s reward structure for user-preference alignment. Yet the observable behavior is similar: both produce responses that preserve what users want to hear. Focusing on this phenomenon through Goffman’s face-work framework, we operationalize AI sycophancy as excessive face-saving, either active (preserving positive face through agreement) or passive (preserving negative face by withholding challenge). In a mixed-methods study (N=31), participants engaged with AI across three moral dilemmas under these conditions and a non-sycophantic neutral baseline. Sycophantic responses increased decision confidence but reduced open-minded thinking; participants felt supported yet found the conversations unproductive. Neutral responses, though initially uncomfortable, promoted cognitive flexibility and meaningful deliberation. These findings reveal a confidence-competence trade-off in AI-mediated moral reasoning and suggest that effective AI for personal deliberation requires calibrated friction, not unconditional agreement.

While generative AI is rapidly advancing in creative industries, its adoption in webtoons—a mobile-first digital comics format—remains contentious. In this exploratory study, we conducted interviews with nine readers, four creators, and six platform stakeholders to examine the sociotechnical dynamics of AI integration. Findings reveal a complex tension: readers value the parasocial authenticity of human creators and reject AI as soulless, compelling creators to adopt strategic silence regarding their use of AI for efficiency. Platforms mediate this conflict by redefining authorship from manual labor to directing and leveraging strategic invisibility to reconcile industrial efficiency with the illusion of human touch. We propose a Tripartite Mediation Model, which maps the structural tensions between creative agency (Production), authenticity (Reception), and market stratification (Distribution). Our study contributes design implications for labor-aware disclosure, scaffolded agency, and personalized training frameworks to preserve artistic integrity while addressing the sequential and emotional demands of webtoon storytelling.

Generative information retrieval (GenIR) enables users to obtain synthesized information through iterative interaction with LLMs, fundamentally reshaping how AI-generated content is produced and consumed. Within this shift, users may encounter AI-generated informational content through two primary pathways: actively creating it themselves or consuming content generated by others. We examine whether authorship biases evaluation---whether users judge AI output from their own interactions more favorably than equivalent output from others. In a mixed-methods experiment (N=28, 2×2 within-subjects), participants interacted with an AI system to retrieve and craft information, then evaluated both their own result and equivalent output generated through the same process but framed as someone else's. Results reveal a selective authorship bias: participants rated self-obtained information significantly higher in quality, but showed no corresponding difference in trust. This pattern suggests that hallucination-aware skepticism constrained trust judgments, but could not prevent quality-driven selection behavior, even in the presence of information conflicts. Given that iterative interactions are inherent to GenIR, diverse interventions seem needed to support users' critical evaluation.

With the advancement of AI, even people without professional experience can create artworks using AI-based image generation systems like DALL-E 2. However, little is known about how users interact with these new AI algorithms, much less how AI-infused systems can be designed. We explore the user experience of these new technologies and their potential to foster creativity. A user study was carried out where 13 participants executed tasks of creating artworks using DALL-E 2 alongside in-depth interviews related to their experience. The results showed that users had ambivalent opinions regarding the algorithm’s performance. When users were informed of the system’s capabilities, they subsequently utilized more specific prompts to generate the intended output. Users also optimized their prompts (the queries they entered to create artworks) based on how algorithms worked to achieve their desired outcome. The users wanted a two-way interaction where AI explained the outcome and accepted feedback rather than simply accepting unilateral instructions. We discuss the implications for designing interfaces that maximize creativity while providing comfort for the users.

Large language models (LLMs) enhance productivity and creativity, but many users struggle to formulate appropriate prompts, discouraging consistent usage. We introduce PromptPilot that assists users by recommending context-appropriate prompts based on task types and the user input. We evaluated PromptPilot through an online experiment using a 3 × 3 mixed factorial design. The study involved 273 participants and examined three initiative conditions (AI-initiative, mixed-initiative, user-initiative) as a between-subjects variable, across three distinct task types (browsing, daily ideation, brainstorming) as a within-subjects variable. We found that the AI-initiative and mixed-initiative systems yielded superior performance results compared to the user-initiative system. Notably, participants in the mixed initiative generated prompts using fewer words compared to those in the AI and user-initiative. The proportion of AI-generated prompts in the AI-initiative was 2.3 times that of the mixed-initiative. We discuss implications for user interaction where AI can support users’ prompting process.

Social media platforms increasingly employ proactive moderation techniques, such as detecting and curbing toxic and uncivil comments, to prevent the spread of harmful content. Despite these efforts, such approaches are often criticized for creating a climate of censorship and failing to address the underlying causes of uncivil behavior. Our work makes both theoretical and practical contributions by proposing and evaluating two types of emotion monitoring dashboards to enhance users' emotional awareness and mitigate hate speech. In a study involving 211 participants, we evaluate the effects of the two mechanisms on user commenting behavior and emotional experiences. The results reveal that these interventions effectively increase users' awareness of their emotional states and reduce hate speech. However, our findings also indicate potential unintended effects, including increased expression of negative emotions (Angry, Fear, and Sad) when discussing sensitive issues. These insights provide a basis for further research on integrating proactive emotion regulation tools into social media platforms to foster healthier digital interactions.

Many people gather online and form teams with strangers to collaborate on tasks. However, while intrateam trust and cohesion are critical for team performance, such characteristics take time to establish and are harder to build up through computer-mediated communication. Building on prior research that has shown that enhancing familiarity between members can help, we hypothesized that the use of a chatbot to support the familiarization of ad hoc teammates can help their collaboration. As such, we designed IntroBot, a chatbot that builds on an online discussion facilitator framework and leverages the social media data of users to assist their familiarization process. Through a between-subjects study (N=60), we found that participants who used IntroBot reported higher levels of trust, cohesion, and interaction quality, as well as generated more ideas in a collaborative brainstorming task. We discuss insights gained from our study, and present opportunities for the future of chatbot-assisted collaboration.

One of the key issues in the synthesis of clock gating is how the flip-flops with similar activity patterns in the target design are identified and grouped, so that all flip-flops in each group should be clock-gated in a way to make a full effectiveness in power saving. As yet, due to the excessive runtime and explosive memory usage demand, the conventional grouping methods have relied on flip-flops‘ toggling probability or toggling pattern of ‘short’ length, which clearly results in the power saving far off that of the optimal grouping. In this work, we overcome this limitation by proposing a machine learning (ML) based flip-flop grouping for clock gating. Precisely, we devise (1) a convolutional autoencoder (CAE) model to produce a ‘short’ embedding vector corresponding to the ‘very long’ input activity pattern of every flip-flop, (2) a convolutional neural network (CNN) based ranker model to predict the degree of flip-flop activity similarity between two input embedding vectors, and (3) a CNN-based model to produce an embedding vector that combines two input embedding vectors. Then, we propose an ML based clock gating synthesis algorithm, which is able to reduce the total dynamic power on circuits by6.3% further on average over that by the conventional state-of-the-art clock gating with no timing violation by the gated logic delay as well as the satisfaction of physical proximity constraint on flip-flops for clock gating.

The benefit of multi-bit flip-flop (MBFF) as opposed to single-bit flip-flop is sharing in-cell clock inverters among the master and slave latches in the internal flip-flops of MBFF. Theoretically, the more flip-flops an MBFF has, the more power saving it can achieve. However, in practice, physically increasing the size of MBFF to accommodate many flip-flops imposes two new challenging problems in physical design: (1) non-flexible MBFF cell flipping for multiple D-to-Q signals and (2) unbalanced or wasted use of MBFF footprint space. In this work, we solve the two problems in a way to enhance routability and timing at the placement and routing stages. Precisely, for problem 1, we make the non-flexible MBFF cell flipping to be fully flexible by generating MBFF layouts supporting diverse D-to-Q flow directions in the detailed placement to improve routability and for problem 2, we enhance the setup and clock-to-Q delay on timing critical flip-flops in MBFF through gate upsizing (i.e., transistor folding) by using the unused space in MBFF to improve timing slack at the post-routing stage. Through experiments with benchmark circuits, it is shown that our proposed design and technology co-optimization (DTCO) flow using MBFFs that solves problems 1 and 2 is very promising.

Since the width of flip-flop standard cells is relatively much longer than that of the cells of primitive gates, the impact of flipping flip-flop cells horizontally in the placement on routing complexity and timing is significant. However, as yet, no work has addressed the issue of how we can effectively exploit the well-known cell flipping technique to multi-bit flip-flop cells in placement. To this end, in this work, we introduce a concept of D-to-Q signal flipping for cell instances of multi-bit flip-flop where the directions of D-t-O signal flow of the individual flip-flop instances can be controlled separately and independently. Then, we propose an effective multi-bit cell flipping methodology based on the D-to-O signal flipping concept with the objective of enhancing routing complexity as well as timing slack in the placement optimization stage.

Online chat functions as a discussion channel for diverse social issues. However, deliberative discussion and consensus-reaching can be difficult in online chats in part because of the lack of structure. To explore the feasibility of a conversational agent that enables deliberative discussion, we designed and developed DebateBot, a chatbot that structures discussion and encourages reticent participants to contribute. We conducted a 2 (discussion structure: unstructured vs. structured) × 2 (discussant facilitation: unfacilitated vs. facilitated) between-subjects experiment (N = 64, 12 groups). Our findings are as follows: (1) Structured discussion positively affects discussion quality by generating diverse opinions within a group and resulting in a high level of perceived deliberative quality. (2) Facilitation drives a high level of opinion alignment between group consensus and independent individual opinions, resulting in authentic consensus reaching. Facilitation also drives more even contribution and a higher level of task cohesion and communication fairness. Our results suggest that a chatbot agent could partially substitute for a human moderator in deliberative discussions.

Individuals interact with algorithms in various ways. Users even game and circumvent algorithms so as to achieve favorable outcomes. This study aims to come to an understanding of how various stakeholders interact with each other in tricking algorithms, with a focus towards online review communities. We employed a mixed-method approach in order to explore how and why users write machine non-translatable reviews as well as how those encrypted messages are perceived by those receiving them. We found that users are able to find tactics to trick the algorithms in order to avoid censoring, to mitigate interpersonal burden, to protect privacy, and to provide authentic information for enabling the formation of informative review communities. They apply several linguistic and social strategies in this regard. Furthermore, users perceive encrypted messages as both more trustworthy and authentic. Based on these findings, we discuss implications for online review community and content moderation algorithms.

Artificial intelligence (AI) algorithms are making remarkable achievements even in creative fields such as aesthetics. However, whether those outside the machine learning (ML) community can sufficiently interpret or agree with their results, especially in such highly subjective domains, is being questioned. In this paper, we try to understand how different user communities reason about AI algorithm results in subjective domains. We designed AI Mirror, a research probe that tells users the algorithmically predicted aesthetic scores of photographs. We conducted a user study of the system with 18 participants from three different groups: AI/ML experts, domain experts (photographers), and general public members. They performed tasks consisting of taking photos and reasoning about AI Mirror's prediction algorithm with think-aloud sessions, surveys, and interviews. The results showed the following: (1) Users understood the AI using their own group-specific expertise; (2) Users employed various strategies to close the gap between their judgments and AI predictions overtime; (3) The difference between users' thoughts and AI pre-dictions was negatively related with users' perceptions of the AI's interpretability and reasonability. We also discuss design considerations for AI-infused systems in subjective domains.

Although group chat discussions are prevalent in daily life, they have a number of limitations. When discussing in a group chat, reaching a consensus often takes time, members contribute unevenly to the discussion, and messages are unorganized. Hence, we aimed to explore the feasibility of a facilitator chatbot agent to improve group chat discussions. We conducted a needfinding survey to identify key features for a facilitator chatbot. We then implemented GroupfeedBot, a chatbot agent that could facilitate group discussions by managing the discussion time, encouraging members to participate evenly, and organizing members' opinions. To evaluate GroupfeedBot, we performed preliminary user studies that varied for diverse tasks and different group sizes. We found that the group with GroupfeedBot appeared to exhibit more diversity in opinions even though there were no differences in output quality and message quantity. On the other hand, GroupfeedBot promoted members' even participation and effective communication for the medium-sized group.

This study aims to explore the feasibility of a text-based virtual agent as a new survey method to overcome the web survey's common response quality problems, which are caused by respondents' inattention. To this end, we conducted a 2 (platform: web vs. chatbot) × 2 (conversational style: formal vs. casual) experiment. We used satisficing theory to compare the responses' data quality. We found that the participants in the chatbot survey, as compared to those in the web survey, were more likely to produce differentiated responses and were less likely to satisfice; the chatbot survey thus resulted in higher-quality data. Moreover, when a casual conversational style is used, the participants were less likely to satisfice-although such effects were only found in the chatbot condition. These results imply that conversational interactivity occurs when a chat interface is accompanied by messages with effective tone. Based on an analysis of the qualitative responses, we also showed that a chatbot could perform part of a human interviewer's role by applying effective communication strategies.

In this article, we aim to offer audiences opportunities to have an immersive experience with the statistical figures in the news. We go beyond the current numerical information representation method to develop a new system for improving the numerical experience. We implemented three different conditions for representing numerical information: 1) text, 2) infographic, 3) VR. We will observe user responses to these methods by measuring engagement, immersion and flow status to detect narrative experience. It is assumed that the VR narrative will provide a more immersive user experience. The main purpose of journalism is to deliver information that is necessary for citizens, communities, and societies to make better decisions. Statistical data is one of the essential elements to comprehend the information about themselves, the community, and society. Since the number is the core element of the statistics, our research will propose the new method to achieve the purpose of the journalism by transforming numbers into life-sized materials.