Designing Effective Training Dataset Explanations: The Impact of Information Depth and Progressive DisclosureTransparency in AI is crucial for fostering user trust and acceptance, yet achieving it through explanations presents significant design challenges, particularly regarding how much detail to provide. For example, in-depth explanations can convey accurate and comprehensive information, but they also risk overwhelming users. This paper considers this important design tradeoff in the context of training dataset explanations, which describe the data used to train AI systems and differ from most model-centric explanations in terms of what and how much information they communicate. Specifically, we investigate how information depth in training dataset explanations and the use of Progressive Disclosure impact users’ understanding of an AI system (assessed via their critiques of the system), their system assessments, and their cognitive load. Findings from a study with 32 participants show advantages to providing users with comprehensive information on training datasets. Detailed explanations not only enhanced perceived trust, fairness, and understanding, but were also preferred by participants despite the increased cognitive load. While Progressive Disclosure did not effectively mitigate cognitive load, it improved users’ perception of learning. These findings suggest that effective transparency does not come from minimizing detail, but from embracing it, as participants consistently valued clarity and completeness over brevity, even at the cost of higher cognitive load.2026AAAriful Islam Anik et al.University of ManitobaExplainable AI (XAI)Algorithmic Transparency & AuditabilityPrivacy by Design & User ControlIUI
The Landscape of Digital Tech Disengagement Solutions for Early Adolescents: Insights from a Systematic Review and App AnalysisThe widespread use of digital devices among children and teenagers has raised concerns about overuse, particularly for early adolescents, who have unique developmental needs and engage with technology more frequently than other age groups. A challenge for designers and researchers interested in contributing solutions is a lack of synthesized design guidelines and characterization of the current state-of-the-art. In this paper, we present a systematic scoping review of academic literature and an analysis of 47 apps, providing a comprehensive characterization of existing tech-mediated solutions for early adolescents. Our review covers literature from two major databases (ACM DL and IEEE Xplore) spanning the past 10 years (2014-May 2024), following the scope of prior similar reviews. The app analysis includes Google Play and Apple App Store apps with features targeting tech overuse, excluding general-purpose apps (e.g., social media, games) and apps without a free trial version. Our findings highlight researchers' design recommendations for promoting tech disengagement in this demographic (e.g., supporting collaborative rule-setting and self-monitoring, maintaining privacy, addressing diverse user needs), while revealing that existing apps tend to prioritize restrictive measures, overlooking self-regulation and active parental engagement. Our findings also identify areas of agreement and potential misalignments between current digital interventions and prior research on target users’ preferences.2025ACAnanta Chowdhury et al.Supporting YouthCSCW
Older Adults' Collaborative Learning Dynamics When Exploring Feature-Rich SoftwareCollaborative learning has been suggested as a promising approach to help older adults learn new technology, however, its effectiveness has been understudied in the context of feature-rich applications. We conducted an observational study with the aim of identifying aspects of collaborative learning, including characteristics of collaborative partners, that impact older adults’ exploratory learning behaviour in feature-rich software. We recruited 22 participants (6 younger adults and 16 older adults) who formed 5 same-age and 6 mixed-age dyads. These dyads worked together remotely to explore a feature-rich application, which was new to them. We classified dyadic interactions into four different collaboration dynamics characterized by distinct attributes. We discovered that effective communication and the ability to navigate the software independently enabled a successful collaboration dynamic that empowered learners. We showed that trust between partners enabled effective communication and we observed that the existing relationship between partners strongly impacted their communication patterns. The more complicated study tasks required participants to validate the correctness of their work and this validation was particularly difficult for some novice older adults who did not benefit from transfer learning and struggled with navigation issues.2024ABAfsane Baghestani et al.Session 1a: Aging and TechnologyCSCW
Co-Designing with Early Adolescents: Understanding Perceptions of and Design Considerations for Tech-Based Mediation Strategies that Promote Technology Disengagement Children’s excessive use of technology is a growing concern, and despite taking various measures, parents often find it difficult to limit their children’s device use. Limiting tech usage can be especially challenging with early adolescents as they start to develop a sense of autonomy. While numerous tech-based mediation solutions exist, in this paper, we aim to learn from early adolescents directly by having them contribute to co-design activities. Through a multi-session, group-based, online co-design study with 21 early adolescents (ages 11-14), we explore their perceptions towards tech overuse and what types of solutions they propose to help with disengagement. Findings from these co-design sessions contribute insights into how the participants conceptualized the problem of tech overuse, how they envisioned appropriate mediation strategies, and important design considerations. We also reflect on our study methods, which encouraged active participation from our participants and facilitated valuable contributions during the online co-design sessions.2023ACAnanta Chowdhury et al.University of ManitobaSpecial Education TechnologyParticipatory DesignCHI
Towards More Gender-Inclusive Q&As: Investigating Perceptions of Additional Community Presence InformationOnline question and answer communities (Q&As) are popular spaces for learning and sharing knowledge. However, prior research suggests that Q&As may not be appealing to and inclusive of both men and women, with absent social considerations listed as a potential contributing factor. We investigate how additional community presence information can affect users’ perceptions of and engagement with a Q&A for graphic design software. Through a 10-day task-based field study with 30 participants (14 women, 14 men, 2 non-binary), we uncover how community presence information can humanize the Q&A and play a role in promoting an inclusive environment. On the other hand, some participants question if community presence information belongs on a Q&A and describe some privacy implications. The women in our sample also talked about the importance of diverse community demographics, while we did not observe this sentiment expressed by the men. Our findings contribute an understanding of how users perceive the role of community presence information within a Q&A. We also discuss how this information might impact women’s future participation and engagement.2022PDPatrick Marcel Joseph Dubois et al.Online Communities & Inclusivity; Online Communities & InclusivityCSCW
Data-Centric Explanations: Explaining Training Data of Machine Learning Systems to Promote TransparencyTraining datasets fundamentally impact the performance of machine learning (ML) systems. Any biases introduced during training (implicit or explicit) are often reflected in the system’s behaviors leading to questions about fairness and loss of trust in the system. Yet, information on training data is rarely communicated to stakeholders. In this work, we explore the concept of data-centric explanations for ML systems that describe the training data to end-users. Through a formative study, we investigate the potential utility of such an approach, including the information about training data that participants find most compelling. In a second study, we investigate reactions to our explanations across four different system scenarios. Our results suggest that data-centric explanations have the potential to impact how users judge the trustworthiness of a system and to assist users in assessing fairness. We discuss the implications of our findings for designing explanations to support users’ perceptions of ML systems.2021AAAriful Islam Anik et al.University of ManitobaExplainable AI (XAI)Privacy by Design & User ControlPrivacy Perception & Decision-MakingCHI
Gender Differences in Graphic Design Q&As: How Community and Site Characteristics Contribute to Gender Gaps in Answering QuestionsQuestion and answer (Q&A) sites can capture a range of user perspectives on using complex, feature-rich software. Little is known, however, on who is contributing to the sites. We look at contribution diversity from the perspective of gender in a domain with near gender parity: graphic design. Through content analysis of 330 answers from two popular Q&A sites and semi-structured interviews with 24 graphic designers, we examine who is contributing, what content, how the community shows appreciation towards their answers, and perceived motivations and barriers to participation. We find that despite gender balance in the field, women contribute far less frequently than men. We also see gender differences in contribution styles and user appreciation. Our interviews shed further light on how Q&A community cultures might be impacting men and women differently and how design choices made by the sites’ developers might be exacerbating these differences. We suggest implications for design for improving gender inclusivity.2020PDPatrick Marcel Joseph Dubois et al.Gender, Sexuality, and RaceCSCW
Where is that Feature? Designing for Cross-Device Software LearnabilityPeople increasingly access cross-device applications from their smartphones while on the go. Yet, they do not fully use the mobile versions for complex tasks, preferring the desktop version of the same application. We conducted a survey (N=77) to identify challenges when switching back and forth between devices. We discovered significant cross-device learnability issues, including that users often find exploring the mobile version frustrating, which leads to prematurely giving up on using the mobile version. Based on the findings, we created four design concepts as video prototypes to explore how to support cross-device learnability. The concepts vary in four key dimensions: the device involved, automation, temporality, and learning approach. Interviews (N=20) probing the design concepts identified individual differences affecting cross-device learning preferences, and that users are more motivated to use cross-device applications when offered the right cross-device learnability support. We conclude with future design directions for supporting seamless cross-device learnability.2020JAJessalyn Alvina et al.Knowledge Worker Tools & WorkflowsPrototyping & User TestingDIS
Creating Augmented and Virtual Reality Applications: Current Practices, Challenges, and OpportunitiesAugmented Reality (AR) and Virtual Reality (VR) devices are becoming easier to access and use, but the barrier to entry for creating AR/VR applications remains high. Although the recent spike in HCI research on novel AR/VR tools is promising, we lack insights into how AR/VR creators use today's state-of-the-art authoring tools as well as the types of challenges that they face. We interviewed 21 AR/VR creators, which we grouped into hobbyists, domain experts, and professional designers. Despite having a variety of motivations and skillsets, they described similar challenges in designing and building AR/VR applications. We synthesize 8 key barriers that AR/VR creators face nowadays, starting from prototyping the initial experiences to dealing with "the many unknowns" during implementation, to facing difficulties in testing applications. Based on our analysis, we discuss the importance of considering end-user developers as a growing population of AR/VR creators, how we can build learning opportunities into AR/VR tools, and the need for building AR/VR toolchains that integrate debugging and testing.2020NANarges Ashtari et al.Simon Fraser UniversityMixed Reality WorkspacesPrototyping & User TestingCHI
Learning Through Exploration: How Children, Adults, and Older Adults Interact with a New Feature-Rich ApplicationFeature-rich applications such as word processors and spreadsheets are not only being used by adults but increasingly by children and older adults as well. Learning these applications is challenging as they offer hundreds of commands throughout the interface. We investigate how newcomers from different age groups explore the user interface of a feature-rich application to determine, locate, and use relevant features. We conducted an in-lab observational study with 10 children (10-12), 10 adults (20-35) and 10 older adults (60-75) who were first-time users of Microsoft OneNote. Our results illustrate key exploration differences across age groups, including that children were careful and performed as efficiently as the adults, whereas older adults spent a longer time and repeated sequences of failed selections. Further, their exploration style was negatively influenced by their past knowledge of similar applications. We discuss design interventions to accommodate these exploration differences and to improve software onboarding for newcomers.2020SMShareen Mahmud et al.University of British ColumbiaAging-Friendly Technology DesignUniversal & Inclusive DesignUser Research Methods (Interviews, Surveys, Observation)CHI
Beyond "One-Size-Fits-All": Understanding the Diversity in How Software Newcomers Discover and Make Use of Help ResourcesFor most modern feature-rich software, considerable external help and learning resources are available on the web (e.g., documentation, tutorials, videos, Q&A forums). But, how do users new to an application discover and make use of such resources? We conducted in-lab and diary studies with 26 software newcomers from a variety of different backgrounds who were all using Fusion 360, a 3D modeling application, for the first time. Our results illustrate newcomers' diverse needs, perceptions, and help-seeking behaviors. We found a number of distinctions in how technical and non-technical users approached help-seeking, including: when and how they initiated the help-seeking process, their struggles in recognizing relevant help, the degree to which they made coordinated use of the application and different resources, and in how they perceived the utility of different help formats. We discuss implications for moving beyond "one-size-fits-all" help resources towards more structured, personalized, and curated help and learning materials.2019KKKimia Kiani et al.Simon Fraser UniversityUser Research Methods (Interviews, Surveys, Observation)Prototyping & User TestingCHI
Maestro: Designing a System for Real-Time Orchestration of 3D Modeling WorkshopsInstructors of 3D design workshops for children face many challenges, including maintaining awareness of students’ progress, helping students who need additional attention, and creating a fun experience while still achieving learning goals. To help address these challenges, we developed Maestro, a workshop orchestration system that visualizes students’ progress, automatically detects and draws attention to common challenges faced by students, and provides mechanisms to address common student challenges as they occur. We present the design of Maestro, and the results of a case-study evaluation with an experienced facilitator and 13 children. The facilitator appreciated Maestro’s real-time indications of which students were successfully following her tutorial demonstration, and recognized the system’s potential to “extend her reach” while helping struggling students. Participant interaction data from the study provided support for our follow-along detection algorithm, and the capability to remind students to use 3D navigation.2018VDVolodymyr Dziubak et al.Programming Education & Computational ThinkingPrototyping & User TestingUIST
Crowdsourcing vs Laboratory-Style Social Acceptability Studies? Examining the Social Acceptability of Spatial User Interactions for Head-Worn DisplaysThe use of crowdsourcing platforms for data collection in HCI research is attractive in their ability to provide rapid access to large and diverse participant samples. As a result, several researchers have conducted studies investigating the similarities and differences between data collected through crowdsourcing and more traditional, laboratory-style data collection. We add to this body of research by examining the feasibility of conducting social acceptability studies via crowdsourcing. Social acceptability can be a key determinant for the early adoption of emerging technologies, and as such, we focus our investigation on social acceptability for Head-Worn Display (HWD) input modalities. Our results indicate that data collected via a crowdsourced experiment and a laboratory-style setting did not differ at a statistically significant level. These results provide initial support for crowdsourcing platforms as viable options for conducting social acceptability research.2018FAFouad Alallah et al.University of ManitobaUser Research Methods (Interviews, Surveys, Observation)Prototyping & User TestingCHI