While conversational agents’ (CAs) semantic and syntactic capabilities have advanced, their pragmatic skills, using language appropriately in context, have emerged as a critical focus in practical applications. Hence, scholars integrate conversational skills derived from human-human interaction into CA designs. However, existing research mainly adopts an empirical approach and focuses on specific CA deployment, making it challenging to identify overarching patterns or develop a comprehensive methodology for transferring human pragmatic skills to CA design. Thus, we conducted a systematic review of 85 studies from primary databases (e.g., ACM, IEEE, etc.), focusing on designing CAs with human-derived conversational skills. We identified skill categories (verbal, paralinguistic, nonverbal), transfer strategies (from dialog data, theories, and via co-design), implementations, and evaluation metrics. We consolidated these insights into a four-stage design process: human skill exploration, definition, transfer, and iterative evaluation. Future research can leverage this to design CAs that achieve conversational goals through contextually appropriate language use.

Asynchronous online discussions enable diverse participants to co-construct knowledge beyond individual contributions. This process ideally evolves through sequential phases, from superficial information exchange to deeper synthesis. However, many discussions stagnate in the early stages. Existing AI interventions typically target isolated phases, lacking mechanisms to progressively advance knowledge co-construction, and the impacts of different intervention styles in this context remain unclear and warrant investigation. To address these gaps, we conducted a design workshop to explore AI intervention strategies (task-oriented and/or relationship-oriented) throughout the knowledge co-construction process, and implemented them in an LLM-powered agent capable of facilitating progression while consolidating foundations at each phase. A within-subject study (N=60) involving five consecutive asynchronous discussions showed that the agent consistently promoted deeper knowledge progression, with different styles exerting distinct effects on both content and experience. These findings provide actionable guidance for designing adaptive AI agents that sustain more constructive online discussions.

Existing research has examined how artificial teammates influence collaboration within teams, but far less is known about their role in shaping interactions between teams. In particular, it remains unclear how transparent integration of AI teammates influences intergroup biases in competitive contexts. To investigate this, we designed StarHarvest, an online game where two hybrid teams (each consisting of one human and one bot, either concealed or revealed) competed for resources while bots elicited prosocial or antisocial behaviors. Drawing on data from 240 participants, we analyzed behavioral choices, evaluations, and resource allocations toward ingroup and outgroup members. Our findings show that hidden bots fostered stronger within-team coordination but also allowed asymmetric retribution toward weaker opponents. By contrast, revealed bots were treated as secondary teammates, reducing cohesion and shifting responsibility onto human partners. We conclude with design implications for socially responsible integration of artificial teammates, highlighting tensions between group-level and agent-level identities.

Decomposition is a fundamental skill in algorithmic programming, requiring learners to break down complex problems into smaller, manageable parts. However, current self-study methods, such as browsing reference solutions or using LLM assistants, often provide excessive or generic assistance that misaligns with learners' decomposition strategies, hindering independent problem-solving and critical thinking. To address this, we introduce Decomposition Box (DBox), an interactive LLM-based system that scaffolds and adapts to learners' personalized construction of a step tree through a "learner-LLM co-decomposition"approach, providing tailored support at an appropriate level. A within-subjects study (N=24) found that compared to the baseline, DBox significantly improved learning gains, cognitive engagement, and critical thinking. Learners also reported a stronger sense of achievement and found the assistance appropriate and helpful for learning. Additionally, we examined DBox's impact on cognitive load, identified usage patterns, and analyzed learners' strategies for managing system errors. We conclude with design implications for future AI-powered tools to better support algorithmic programming education.

Danmaku, a system of scene-aligned, time-synced, floating comments, can augment video content to create g'collective knowledge'. However, its chaotic nature often hinders viewers from effectively assimilating the collective knowledge, especially in knowledge-intensive science videos. With a formative study, we examined viewers' practices for processing collective knowledge and the specific barriers they encountered. Building on these insights, we designed a processing pipeline to filter, classify, and cluster danmaku, leading to the development of CoKnowledge - a tool incorporating a video abstract, knowledge graphs, and supplementary danmaku features to support viewers' assimilation of collective knowledge in science videos. A within-subject study (N=24) showed that CoKnowledge significantly enhanced participants' comprehension and recall of collective knowledge compared to a baseline with unprocessed live comments. Based on our analysis of user interaction patterns and feedback on design features, we presented design considerations for developing similar support tools.

On online video platforms, viewers often lack a channel to sense others’ and express their affective state on the fly compared to co-located group-viewing. This study explored the design of complementary affective communication specifically for effortless, spontaneous sharing of frissons during video watching. Also known as aesthetic chills, frissons are instant psycho-physiological reactions like goosebumps and shivers to arousing stimuli. We proposed an approach that unobtrusively detects viewers’ frissons using skin electrodermal activity sensors and presents the aggregated data alongside online videos. Following a design process of brainstorming, focus group interview (N=7), and design iterations, we proposed three different designs to encode viewers’ frisson experiences, namely, ambient light, icon, and vibration. A mixed-methods within-subject study (N=48) suggested that our approach offers a non-intrusive and efficient way to share viewers’ frisson moments, increases the social presence of others as if watching together, and can create affective contagion among viewers.

Interdisciplinary studies often require researchers to explore literature in diverse branches of knowledge. Yet, navigating through the highly scattered knowledge from unfamiliar disciplines poses a significant challenge. In this paper, we introduce DiscipLink, a novel interactive system that facilitates collaboration between researchers and large language models (LLMs) in interdisciplinary information seeking (IIS). Based on users’ topic of interest, DiscipLink initiates exploratory questions from the perspectives of possible relevant fields of study, and users can further tailor these questions. DiscipLink then supports users in searching and screening papers under selected questions by automatically expanding queries with disciplinary-specific terminologies, extracting themes from retrieved papers, and highlighting the connections between papers and questions. Our evaluation, comprising a within-subject comparative experiment and an open-ended exploratory study, reveals that DiscipLink can effectively support researchers in breaking down disciplinary boundaries and integrating scattered knowledge in diverse fields. The findings underscore the potential of LLM-powered tools in fostering information-seeking practices and bolstering interdisciplinary research.

Fraud detection aims to identify fraudsters from normal users. In graph environments, both fraudsters and normal users are modeled as nodes, while edges represent the connections between them. However, fraudulent nodes in the real world often camouflage themselves by establishing numerous fake connections with normal nodes, making them challenging to be identified. Existing fraud detection methods struggle to address this issue, they utilize graph neural networks to aggregate normal informations from normal neighbors, which leads to the smoothing of the fraudulent information. Furthermore, these methods exhibit poor generalization performance as they are unable to detect new fraudsters which not present in the training process. To overcome these limitations, this paper proposes GFAN, a novel model based on Graph Feature enhAncement Network. Specifically, GFAN introduces a specific semantic extraction module to screen and delete fake connections by evaluating the confidence level of edge presence. Additionally, GFAN provides a representation enhanced co-training module that highlights camouflaged fraudulent representations by training the small sphere and large margin support vector data description. Experimental results show that GFAN outperforms other competitive graph-based fraud detectors on public datasets. The GFAN code is available at: https://github.com/scu-kdde/OAM-GFAN-2023.

An effective task-oriented chatbot should be able to exert a certain level of Social Intelligence (SI), the ability to emulate human social behaviors to reduce user frustration and dissatisfaction. However, few studies explored using humor, a common rhetorical device in human-human interactions, to improve chatbots’ overall SI. To fill this gap, we proposed to apply self-mockery humor to a customer service chatbot in different interaction stages with users. We proposed a pipeline to create situated self-mockery for the chatbot and conducted a within-subject experiment (N=28) to compare it with a chatbot without self-mockery utterance. Results showed that the self-mockery chatbot was perceived as significantly funnier, more satisfactory, and delivering higher performance in two out of the five measured characteristics of SI with comparable performance in the rest. We further discussed how participants’ individual factors might affect the perceived helpfulness of self-mockery on SI and concluded with design considerations.

More and more learning apps like Duolingo are using some form of gamification (e.g., badges, points, and leaderboards) to enhance user learning. However, they are not always successful. Gamification misuse is a phenomenon that occurs when users become too fixated on gamification and get distracted from learning. This undesirable phenomenon wastes users' precious time and negatively impacts their learning performance. However, there has been little research in the literature to understand gamification misuse and inform future gamification designs. Therefore, this paper aims to fill this knowledge gap by conducting the first extensive qualitative research on gamification misuse in a popular learning app called Duolingo. Duolingo is currently the world's most downloaded learning app used to learn languages. This study consists of two phases: (I)a content analysis of data from Duolingo forums (from the past nine years) and (II)semi-structured interviews with 15 international Duolingo users. Our research contributes to the Human-Computer Interaction (HCI) and Learning at Scale ([email protected]) research communities in three ways: (1) elaborating the ramifications of gamification misuse on user learning, well-being, and ethics, (2) identifying the most common reasons for gamification misuse (e.g., competitiveness, overindulgence in playfulness, and herding), and (3) providing designers with practical suggestions to prevent (or mitigate) the occurrence of gamification misuse in their future designs of gamified learning apps.

In recent years, studies on the user experience have emerged as an indispensable part of any gamification research. The study of user experience enables gamification designers and practitioners to design or adapt their gamification schemes in a more knowledgeable and efficacious manner. However, one popular gamification scheme that has largely remained under-researched in terms of user experience is promotional gamification, which refers to an optional and time-limited gamification program that usually mounts an already gamified platform to increase user incentive and engagement for a short span of time (e.g., during the holiday season). The current study undertakes the first steps necessary to explore users' experiences of working with a promotional gamification scheme in a large-scale online community. To this end, we conduct an extensive qualitative case study of users' experiences with a promotional gamification scheme on the Community Question Answering Website (CQA) of Stack Exchange, called Winter Bash (WB). Notably, the purpose of WB is to operate as a makeshift solution that prevents the decline in user contributions during the holiday season. However, like many other gamification schemes, WB is not devoid of issues, and our research helps identify those issues without overlooking the WB's strengths. Our study denotes not only the first (empirical) typology of users' affective responses to promotional gamification schemes but also the first classification of (de)motivational factors involved in user engagement. At its core, this study comprises two salient parts: (1) a content analysis of user-generated data regarding WB (from the past eight years), and (2) a series of semi-structured interviews with 17 international users who are familiar with WB. We triangulate our findings from (1) and (2) by performing a similar content analysis for two other promotional gamification schemes, namely "Answerathon" (from Travel Meta) and "Discussion Tournament" (from Reddit). Based on the findings of this study, we present certain guidelines for gamification designers and practitioners, enabling them to deploy or adapt their promotional gamification schemes in a more knowledgeable and effective manner. Finally, our work is concluded by highlighting a few novel research opportunities for researchers invested in the fields of Human-Computer Interaction (HCI) and Computer-Supported Cooperative Work (CSCW).

BACKGROUND: High throughput parallel sequencing, RNA-Seq, has recently emerged as an appealing alternative to microarray in identifying differentially expressed genes (DEG) between biological groups. However, there still exists considerable discrepancy on gene expression measurements and DEG results between the two platforms. The objective of this study was to compare parallel paired-end RNA-Seq and microarray data generated on 5-azadeoxy-cytidine (5-Aza) treated HT-29 colon cancer cells with an additional simulation study. METHODS: We first performed general correlation analysis comparing gene expression profiles on both platforms. An Errors-In-Variables (EIV) regression model was subsequently applied to assess proportional and fixed biases between the two technologies. Then several existing algorithms, designed for DEG identification in RNA-Seq and microarray data, were applied to compare the cross-platform overlaps with respect to DEG lists, which were further validated using qRT-PCR assays on selected genes. Functional analyses were subsequently conducted using Ingenuity Pathway Analysis (IPA). RESULTS: Pearson and Spearman correlation coefficients between the RNA-Seq and microarray data each exceeded 0.80, with 66%~68% overlap of genes on both platforms. The EIV regression model indicated the existence of both fixed and proportional biases between the two platforms. The DESeq and baySeq algorithms (RNA-Seq) and the SAM and eBayes algorithms (microarray) achieved the highest cross-platform overlap rate in DEG results from both experimental and simulated datasets. DESeq method exhibited a better control on the false discovery rate than baySeq on the simulated dataset although it performed slightly inferior to baySeq in the sensitivity test. RNA-Seq and qRT-PCR, but not microarray data, confirmed the expected reversal of SPARC gene suppression after treating HT-29 cells with 5-Aza. Thirty-three IPA canonical pathways were identified by both microarray and RNA-Seq data, 152 pathways by RNA-Seq data only, and none by microarray data only. CONCLUSIONS: These results suggest that RNA-Seq has advantages over microarray in identification of DEGs with the most consistent results generated from DESeq and SAM methods. The EIV regression model reveals both fixed and proportional biases between RNA-Seq and microarray. This may explain in part the lower cross-platform overlap in DEG lists compared to those in detectable genes.