SAFELIFT: Safety-Aware Feedback for Ergonomic Lifting & Injury-Free TasksWork-related musculoskeletal disorders, often caused by unsafe lifting techniques, remain a persistent threat to worker health and safety. We present SAFELIFT, a safety-aware recommender system that automatically detects risky lifting behaviors and generates corrective feedback. Using monocular video input, SAFELIFT extracts ergonomic parameters to compute the Lifting Index (LI) from the Revised NIOSH Lifting Equation. When the LI exceeds a safety threshold, the system produces both graphical and textual recommendations to promote safer postural strategies. Unlike prior approaches, SAFELIFT requires no wearable sensors or multi-camera setups, enabling scalable and low-cost deployment in workplace environments. To assess its effectiveness, we conducted a two-phase evaluation: (1) domain experts (ergonomists, occupational safety professionals, medical staff) assessed the accuracy and relevance of the recommendations, and (2) lay users evaluated different presentation formats, judging their clarity, helpfulness, and trustworthiness. By integrating ergonomics with recommender system design, SAFELIFT contributes to a new class of context-aware, safety-oriented recommendation technologies for occupational health.2026GDGaetano Dibenedetto et al.University of Bari Aldo MoroAI-Assisted Decision-Making & AutomationHuman Pose & Activity RecognitionBehavior Change & Reflection TechnologyIUI
Do Children Trust AI, and Should They? Designing and Validating a Child-Centred K-AI Trust Scale for Intelligent SystemsMost trust metrics for intelligent systems are developed for adults, relying on complex reasoning and language that do not align with children’s developmental stages. As intelligent systems increasingly engage with young users, evaluating trust in child-AI interaction has become an urgent concern in HCI. In this paper, we present the iterative refinement and validation of the K-AI Trust Questionnaire, a child-centred instrument that integrates dispositional and situational trust components grounded in child-rights principles. Dispositional trust is captured through a child-adapted Propensity to Trust Technology (PTT), while situational trust is assessed through post-interaction items reflecting children's experience with AI. Starting with a sample of 289 children, we conducted psychometric analyses and exploratory testing, culminating in a confirmatory factor analysis on a subsample of 85 children. Results supported a unidimensional structure consistent with the PTT, and highlighted the limitations of adult-oriented scales, underscoring the need for developmentally appropriate tools for trustworthy child-AI design.2026GRGrazia Ragone et al.University of BariChildren's AI Literacy & Data LiteracyHuman-LLM CollaborationCognitive Impairment & Neurodiversity (Autism, ADHD, Dyslexia)CHI
“React”, “Command”, or “Instruct”? Teachers’ Mental Models on End-User DevelopmentThis paper presents findings from a thinking-aloud protocol exploring mental models in 28 elementary school math teachers during their initial attempt at composing and testing trigger-action rules for a smart tangible educational device. In the study, two sets of event-driven primitives were implemented in an End-User Development platform for guiding teachers with no programming experience in defining new functions of the device: "concrete", based on actual actions performed on the device, and "abstract", based on general definitions of events/actions. With a thematic analysis, we identified three different metaphors that drive participants' interaction with the device. We discuss how the metaphors influenced performance and how the order of exposition to the two primitive sets impacted their grasping of the trigger-action logic. Our findings suggest the importance of guiding teachers in assuming effective metaphors for performing End-User Development tasks, to empower them to adopt an active role toward digital devices in education.2025MAMargherita Andrao et al.University of Trento, Department of Psychology and Cognitive Science; Fondazione Bruno Kessler (FBK)Augmentative & Alternative Communication (AAC)Programming Education & Computational ThinkingIntelligent Tutoring Systems & Learning AnalyticsCHI
Lateralization Effects in Electrodermal Activity Data Collected Using Wearable DevicesAlchieri 等人研究可穿戴设备收集的皮肤电活动数据偏侧化效应，发现不同身体部位的EDA信号存在显著差异，为情绪与压力监测提供理论依据。2024LALeonardo Alchieri et al.Haptic WearablesBiosensors & Physiological MonitoringUbiComp
Soil-Powered Computing: The Engineer's Guide to Practical Soil Microbial Fuel Cell DesignYen 等人提供土壤微生物燃料电池的系统设计指南，涵盖材料选择、结构优化与性能提升，服务于土壤供能计算应用。2024BYBill Yen et al.Ubiquitous ComputingEcological Design & Green ComputingUbiComp
Grand Challenges in SportsHCIThe field of Sports Human-Computer Interaction (SportsHCI) investigates interaction design to support a physically active human being. Despite growing interest and dissemination of SportsHCI literature over the past years, many publications still focus on solving specific problems in a given sport. We believe in the benefit of generating fundamental knowledge for SportsHCI more broadly to advance the field as a whole. To achieve this, we aim to identify the grand challenges in SportsHCI, which can help researchers and practitioners in developing a future research agenda. Hence, this paper presents a set of grand challenges identified in a five-day workshop with 22 experts who have previously researched, designed, and deployed SportsHCI systems. Addressing these challenges will drive transformative advancements in SportsHCI, fostering better athlete performance, athlete-coach relationships, spectator engagement, but also immersive experiences for recreational sports or exercise motivation, and ultimately, improve human well-being.2024DEDon Samitha Elvitigala et al.Monash UniversityGame UX & Player BehaviorSerious & Functional GamesMental Health Apps & Online Support CommunitiesCHI
Eliciting Best Practices for Collaboration with Computational NotebooksDespite the widespread adoption of computational notebooks, little is known about best practices for their usage in collaborative contexts. In this paper, we fill this gap by eliciting a catalog of best practices for collaborative data science with computational notebooks. With this aim, we first look for best practices through a multivocal literature review. Then, we conduct interviews with professional data scientists to assess their awareness of these best practices. Finally, we assess the adoption of best practices through the analysis of 1,380 Jupyter notebooks retrieved from the Kaggle platform. Findings reveal that experts are mostly aware of the best practices and tend to adopt them in their daily work. Nonetheless, they do not consistently follow all the recommendations as, depending on specific contexts, some are deemed unfeasible or counterproductive due to the lack of proper tool support. As such, we envision the design of notebook solutions that allow data scientists not to have to prioritize exploration and rapid prototyping over writing code of quality.2022LQLuigi Quaranta et al.Data WorkCSCW
Eliciting Best Practices for Collaboration with Computational NotebooksDespite the widespread adoption of computational notebooks, little is known about best practices for their usage in collaborative contexts. In this paper, we fill this gap by eliciting a catalog of best practices for collaborative data science with computational notebooks. With this aim, we first look for best practices through a multivocal literature review. Then, we conduct interviews with professional data scientists to assess their awareness of these best practices. Finally, we assess the adoption of best practices through the analysis of 1,380 Jupyter notebooks retrieved from the Kaggle platform. Findings reveal that experts are mostly aware of the best practices and tend to adopt them in their daily work. Nonetheless, they do not consistently follow all the recommendations as, depending on specific contexts, some are deemed unfeasible or counterproductive due to the lack of proper tool support. As such, we envision the design of notebook solutions that allow data scientists not to have to prioritize exploration and rapid prototyping over writing code of quality.2022LQLuigi Quaranta et al.Data WorkCSCW
ArguLens: Anatomy of Community Opinions On Usability Issues Using Argumentation ModelsIn open-source software (OSS), the design of usability is often influenced by the discussions among community members on platforms such as issue tracking systems (ITSs). However, digesting the rich information embedded in issue discussions can be a major challenge due to the vast number and diversity of the comments. We propose and evaluate ArguLens, a conceptual framework and automated technique leveraging an argumentation model to support effective understanding and consolidation of community opinions in ITSs. Through content analysis, we anatomized highly discussed usability issues from a large, active OSS project, into their argumentation components and standpoints. We then experimented with supervised machine learning techniques for automated argument extraction. Finally, through a study with experienced ITS users, we show that the information provided by ArguLens supported the digestion of usability-related opinions and facilitated the review of lengthy issues. ArguLens provides the direction of designing valuable tools for high-level reasoning and effective discussion about usability.2020WWWenting Wang et al.McGill UniversityGenerative AI (Text, Image, Music, Video)Crowdsourcing Task Design & Quality ControlUser Research Methods (Interviews, Surveys, Observation)CHI
Empowering End Users to Customize their Smart Environments: Model, Composition Paradigms, and Domain-Specific ToolsResearch on the Internet of Things (IoT) has devoted many efforts to technological aspects. Little social and practical benefits have emerged so far. IoT devices, so-called smart objects, are becoming even more pervasive and social, leading to the need to provide non-technical users with innovative interaction strategies for controlling their behavior. In other words, the opportunities offered by IoT can be amplified if new approaches are conceived to enable non-technical users to be directly involved in “composing” their smart objects by synchronizing their behavior. To fulfill this goal, this article introduces a model that includes new operators for defining rules combining multiple events and conditions exposed by smart objects, and for defining temporal and spatial constraints on rule activation. The article also presents the results of an elicitation study that was conducted to identify possible visual paradigms for expressing composition rules. Prototypes implementing the resulting visual paradigms were compared during a controlled experiment and the one that resulted most relevant for our goals was used in a study that involved home-automation experts. Finally, the article discusses some design implications that came out from the performed studies and presents the architecture of a platform supporting rule definition and execution.2018GDGiuseppe Desolda et al.University of Bari Aldo MoroContext-Aware ComputingSmart Home Interaction DesignSmart Home Privacy & SecurityCHI
Investigating Crowd Creativity in Online Music CommunitiesCrowd creativity is typically associated with peer-production communities focusing on artistic products like animations, video games, and music, but less frequently to Open Source Software (OSS), despite the fact that also developers must be creative to come up with new solutions to their technical challenges. In this paper, we conduct a study to further the understanding of which factors from prior work in both OSS and art communities are predictive of successful collaboration – defined as reuse of previous songs – in three different songwriting communities, namely Songtree, Splice, and ccMixter. The main findings from this study confirm that the success of collaborations is associated with high community status of recognizable authors and low degree of derivativity of songs.2018FCFabio Calefato et al.Information SharingCSCW