Understanding the Challenges Students Face in Non-English Programming Environments Due to the Programming Language Transition: A Case Study of Keywords in the Chinese Version of ScratchAs the importance of computer science (CS) education gains global recognition, the learner population is expanding to include all manner of backgrounds. However, students from non-English backgrounds face challenges in understanding instructional material, technical communication, and reading and writing code, which further impacts their learning outcomes. These issues have attracted attention in the fields of Human-Computer Interaction (HCI), programming languages, and computer education, which have demonstrated that using programming tools in mother tongues or local languages enhances learners' ability to grasp computing concepts. Consequently, extensive efforts have been dedicated to translating English technical terms across various languages and even developing non-English-based programming languages.2025SWSiyu Wang et al.Wuhan University, School of Computer ScienceMultilingual & Cross-Cultural Voice InteractionProgramming Education & Computational ThinkingK-12 Digital Education ToolsCHI
LitLinker: Supporting the Ideation of Interdisciplinary Contexts with Large Language Models for Teaching Literature in Elementary SchoolsTeaching literature under interdisciplinary contexts (e.g., science, art) that connect reading materials has become popular in elementary schools. However, constructing such contexts is challenging as it requires teachers to explore substantial amounts of interdisciplinary content and link it to the reading materials. In this paper, we develop LitLinker via an iterative design process involving 13 teachers to facilitate the ideation of interdisciplinary contexts for teaching literature. Powered by a large language model (LLM), LitLinker can recommend interdisciplinary topics and contextualize them with the literary elements (e.g., paragraphs, viewpoints) in the reading materials. A within-subjects study (N=16) shows that compared to an LLM chatbot, LitLinker can improve the integration depth of different subjects and reduce workload in this ideation task. Expert interviews (N=9) also demonstrate LitLinker’s usefulness for supporting the ideation of interdisciplinary contexts for teaching literature. We conclude with concerns and design considerations for supporting interdisciplinary teaching with LLMs.2025HFHaoxiang Fan et al.Sun Yat-sen UniversityHuman-LLM CollaborationK-12 Digital Education ToolsCHI
Using Affordance to Understand Usability of Web3 Social MediaWeb3 social media refers to a new generation of platforms built on decentralized technologies, particularly blockchain. Although academia has investigated the newly emerging Web3 social media, it is not clear how users perceive the usability of such platforms and how these perceptions are influenced by the inherent characteristics of Web3. To address this gap, we utilize affordance theory to explore the unique usability of Web3 social media compared with Web2 social media. We conducted interviews with 32 participants who are experienced with Web3 social media and examined the affordances of Web3 social media from the perspectives of content creation, content consumption, and community interaction. We further discuss the correlation between the usability of Web3 social media and the underlying decentralized technology, and provide design implications for enhancing the usability of this new type of social interaction platform.2025MGMaggie Yongqi Guan et al.University of MacauAlgorithmic Transparency & AuditabilityPrivacy by Design & User ControlSocial Platform Design & User BehaviorCHI
" It Felt Like Having a Second Mind": Investigating Human-AI Co-creativity in Prewriting with Large Language ModelsPrewriting is the process of discovering and developing ideas before writing a first draft, which requires divergent thinking and often implies unstructured strategies such as diagramming, outlining, free-writing, etc. Although large language models (LLMs) have been demonstrated to be useful for a variety of tasks including creative writing, little is known about how users would collaborate with LLMs to support prewriting. The preferred collaborative role and initiative of LLMs during such a creative process is also unclear. To investigate human-LLM collaboration patterns and dynamics during prewriting, we conducted a three-session qualitative study with 15 participants in two creative tasks: story writing and slogan writing. The findings indicated that during collaborative prewriting, there appears to be a three-stage iterative Human-AI Co-creativity process that includes Ideation, Illumination, and Implementation stages. This collaborative process champions the human in a dominant role, in addition to mixed and shifting levels of initiative that exist between humans and LLMs. This research also reports on collaboration breakdowns that occur during this process, user perceptions of using existing LLMs during Human-AI Co-creativity, and discusses design implications to support this co-creativity process.2024QWKin Chung Kwan et al.Session 3a: AI in Creative Workflows: Opportunities and ChallengesCSCW
“There is a Job Prepared for Me Here”: Understanding How Short Video and Live-streaming Platforms Empower Ageing Job Seekers in ChinaIn recent years, the global unemployment rate has remained persistently high. Compounding this issue, the ageing population in China often encounters additional challenges in finding employment due to prevalent age discrimination in daily life. However, with the advent of social media, there has been a rise in the popularity of short videos and live-streams for recruiting ageing workers. To better understand the motivations of ageing job seekers to engage with these video-based recruitment methods and to explore the extent to which such platforms can empower them, we conducted an interview-based study with ageing job seekers who have had exposure to these short recruitment videos and live-streaming channels. Our findings reveal that these platforms can provide a job-seeking choice that is particularly friendly to ageing job seekers, effectively improving their disadvantaged situation.2024PWPiaoHong Wang et al.City University Of HongKongSocial Platform Design & User BehaviorImpact of Automation on WorkSmart Cities & Urban SensingCHI
Understanding User-Perceived Security Risks and Mitigation Strategies in the Web3 EcosystemThe advent of Web3 technologies promises unprecedented levels of user control and autonomy. However, this decentralization shifts the burden of security onto the users, making it crucial to understand their security behaviors and perceptions. To address this, our study introduces a comprehensive framework that identifies four core components of user interaction within the Web3 ecosystem: blockchain infrastructures, Web3-based Decentralized Applications (DApps), online communities, and off-chain cryptocurrency platforms. We delve into the security concerns perceived by users in each of these components and analyze the mitigation strategies they employ, ranging from risk assessment and aversion to diversification and acceptance. We further discuss the landscape of both technical and human-induced security risks in the Web3 ecosystem, identify the unique security differences between Web2 and Web3, and highlight key challenges that render users vulnerable, to provide implications for security design in Web3.2024JSJanice Jianing SI et al.University of MacauPrivacy by Design & User ControlPrivacy Perception & Decision-MakingIoT Device PrivacyCHI
CrowdQ: Predicting the Queue State of Hospital Emergency Department Using Crowdsensing Mobility Data-Driven Models"Hospital Emergency Departments (EDs) are essential for providing emergency medical services, yet often overwhelmed due to increasing healthcare demand. Current methods for monitoring ED queue states, such as manual monitoring, video surveillance, and front-desk registration are inefficient, invasive, and delayed to provide real-time updates. To address these challenges, this paper proposes a novel framework, CrowdQ, which harnesses spatiotemporal crowdsensing data for real-time ED demand sensing, queue state modeling, and prediction. By utilizing vehicle trajectory and urban geographic environment data, CrowdQ can accurately estimate emergency visits from noisy traffic flows. Furthermore, it employs queueing theory to model the complex emergency service process with medical service data, effectively considering spatiotemporal dependencies and event context impact on ED queue states. Experiments conducted on large-scale crowdsensing urban traffic datasets and hospital information system datasets from Xiamen City demonstrate the framework's effectiveness. It achieves an F1 score of 0.93 in ED demand identification, effectively models the ED queue state of key hospitals, and reduces the error in queue state prediction by 18.5%-71.3% compared to baseline methods. CrowdQ, therefore, offers valuable alternatives for public emergency treatment information disclosure and maximized medical resource allocation." https://doi.org/10.1145/36108752023TSTieqi Shou et al.Content Moderation & Platform GovernancePublic Transit & Trip PlanningUbiComp