huashen218/convxai-cia-dataset huggingface dataset:
Title: Engaging Teachers to Co-Design Integrated AI Curriculum for K-12 Classrooms
Authors: family: Lin
given: Phoebe; family: Van Brummelen
given: Jessica
Venue: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Keywords: K-12 Education, Co-Design, Artificial Intelligence

Abstract: Artificial Intelligence (AI) education is an increasingly popular topic area for K-12 teachers. However, little research has investigated how AI curriculum and tools can be designed to be more accessible to all teachers and learners. In this study, we take a Value-Sensitive Design approach to understanding the role of teacher values in the design of AI curriculum and tools, and identifying opportunities to integrate AI into core curriculum to leverage learners’ interests. We organized co-design workshops with 15 K-12 teachers, where teachers and researchers co-created lesson plans using AI tools and embedding AI concepts into various core subjects. We found that K-12 teachers need additional scaffolding in AI tools and curriculum to facilitate ethics and data discussions, and value supports for learner evaluation and engagement, peer-to-peer collaboration, and critical reflection. We present an exemplar lesson plan that shows entry points for teaching AI in non-computing subjects and reflect on co-designing with K-12 teachers in a remote setting.
Title: Towards Understanding Perceptual Differences between Genuine and Face-Swapped Videos
Authors: family: Wöhler
given: Leslie; family: Zembaty
given: Martin; family: Castillo
given: Susana; family: Magnor
given: Marcus
Venue: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Keywords: video manipulation, human perception, face swapping, eye tracking

Abstract: In this paper, we report on perceptual experiments indicating that there are distinct and quantitatively measurable differences in the way we visually perceive genuine versus face-swapped videos. Recent progress in deep learning has made face-swapping techniques a powerful tool for creative purposes, but also a means for unethical forgeries. Currently, it remains unclear why people are misled, and which indicators they use to recognize potential manipulations. Here, we conduct three perceptual experiments focusing on a wide range of aspects: the conspicuousness of artifacts, the viewing behavior using eye tracking, the recognition accuracy for different video lengths, and the assessment of emotions. Our experiments show that responses differ distinctly when watching manipulated as opposed to original faces, from which we derive perceptual cues to recognize face swaps. By investigating physiologically measurable signals, our findings yield valuable insights that may also be useful for advanced algorithmic detection.
Title: Domestic Robots and the Dream of Automation: Understanding Human Interaction and Intervention
Authors: family: Schneiders
given: Eike; family: Kanstrup
given: Anne Marie; family: Kjeldskov
given: Jesper; family: Skov
given: Mikael B.
Venue: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Keywords: human-robot interaction, domestic robot automation, interventions to domestic robot breakdown, domestic robots

Abstract: Domestic robots such as vacuum cleaners or lawnmowers are becoming popular consumer products in private homes, but while current HCI research on domestic robots has highlighted for example personalisation, long-term effects, or design guidelines, little attention has been paid to automation. To address this, we conducted a qualitative study with 24 participants in private households using interviews, contextual technology tours, and robot deployment. Through thematic analysis we identified three themes related to 1) work routines and automation, 2) domestic robot automation and the physical environment, as well as 3) interaction and breakdown intervention. We present an empirical understanding of how task automation using domestic robots can be implemented in the home. Lastly, we discuss our findings in relation to existing literature and highlight three opportunities for improved task automation using domestic robots for future research.
Title: Designers Characterize Naturalness in Voice User Interfaces: Their Goals, Practices, and Challenges
Authors: family: Kim
given: Yelim; family: Reza
given: Mohi; family: McGrenere
given: Joanna; family: Yoon
given: Dongwook
Venue: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Keywords: challenges, characterize, designers, naturalness, voice user interfaces (VUI), practices

Abstract: This work investigates the practices and challenges of voice user interface (VUI) designers. Existing VUI design guidelines recommend that designers strive for natural human-agent conversation. However, the literature leaves a critical gap regarding how designers pursue naturalness in VUIs and what their struggles are in doing so. Bridging this gap is necessary for identifying designers’ needs and supporting them. Our interviews with 20 VUI designers identified 12 ways that designers characterize and approach naturalness in VUIs. We categorized these characteristics into three groupings based on the types of conversational context that each characteristic contributes to: Social, Transactional, and Core. Our results contribute new findings on designers’ challenges, such as a design dilemma in augmenting task-oriented VUIs with social conversations, difficulties in writing for spoken language, lack of proper tool support for imbuing synthesized voice with expressivity, and implications for developing design tools and guidelines.
Title: Trade-offs for Substituting a Human with an Agent in a Pair Programming Context: The Good, the Bad, and the Ugly
Authors: family: Kuttal
given: Sandeep Kaur; family: Ong
given: Bali; family: Kwasny
given: Kate; family: Robe
given: Peter
Venue: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Keywords: empirical evaluation, Pair programming, conversational agents, avatars

Abstract: Pair programming has a documented history of benefits, such as increased code quality, productivity, self-efficacy, knowledge transfer, and reduced gender gap. Research uncovered problems with pair programming related to scheduling, collocating, role imbalance, and power dynamics. We investigated the trade-offs of substituting a human with an agent to simultaneously provide benefits and alleviate obstacles in pair programming. We conducted gender-balanced studies with human-human pairs in a remote lab with 18 programmers and Wizard-of-Oz studies with 14 programmers, then analyzed results quantitatively and qualitatively. Our comparative analysis of the two studies showed no significant differences in productivity, code quality, and self-efficacy. Further, agents facilitated knowledge transfer; however, unlike humans, agents were unable to provide logical explanations or discussions. Human partners trusted and showed humility towards agents. Our results demonstrate that agents can act as effective pair programming partners and open the way towards new research on conversational agents for programming.
Title: What Do We See in Them? Identifying Dimensions of Partner Models for Speech Interfaces Using a Psycholexical Approach
Authors: family: Doyle
given: Philip R; family: Clark
given: Leigh; family: Cowan
given: Benjamin R.
Venue: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Keywords: psycholexical, speech interfaces, partner models, mental models, human-machine dialogue, psychometrics

Abstract: Perceptions of system competence and communicative ability, termed partner models, play a significant role in speech interface interaction. Yet we do not know what the core dimensions of this concept are. Taking a psycholexical approach, our paper is the first to identify the key dimensions that define partner models in speech agent interaction. Through a repertory grid study (N=21), a review of key subjective questionnaires, an expert review of resulting word pairs and an online study of 356 users of speech interfaces, we identify three key dimensions that make up a users’ partner model: 1) perceptions towards partner competence and dependability; 2) assessment of human-likeness; and 3) a system’s perceived cognitive flexibility. We discuss the implications for partner modelling as a concept, emphasising the importance of salience and the dynamic nature of these perceptions.
Title: The Technology-Mediated Reflection Model: Barriers and Assistance in Data-Driven Reflection
Authors: family: Bentvelzen
given: Marit; family: Niess
given: Jasmin; family: Woźniak
given: Paweł W.
Venue: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Keywords: tracking, fitness trackers, construal, reflection, personal informatics

Abstract: Current personal informatics models consider reflection as an important stage in users’ journeys with trackers. However, these models describe reflection from a meta perspective and it remains unclear what this stage entails. To design interactive technologies that support reflection, we need a more thorough understanding of how people reflect on their personal data in practice. To that end, we conducted semi-structured interviews with users of fitness trackers and an online survey to study practices in reflecting on fitness data. Our results show that users reported reflecting on data despite lacking reflection support from their tracking technology. Based on our results, we introduce the Technology-Mediated Reflection Model, which describes conditions and barriers for reflection on personal data. Our model consists of the temporal and conceptual cycles of reflection and helps designers identify the possible barriers a user might face when using a system for reflection.
Title: AffectiveSpotlight: Facilitating the Communication of Affective Responses from Audience Members during Online Presentations
Authors: family: Murali
given: Prasanth; family: Hernandez
given: Javier; family: McDuff
given: Daniel; family: Rowan
given: Kael; family: Suh
given: Jina; family: Czerwinski
given: Mary
Venue: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Keywords: Affective Computing, Intelligent User Interfaces, Videoconferencing, Public Speaking

Abstract: The ability to monitor audience reactions is critical when delivering presentations. However, current videoconferencing platforms offer limited solutions to support this. This work leverages recent advances in affect sensing to capture and facilitate communication of relevant audience signals. Using an exploratory survey (N=175), we assessed the most relevant audience responses such as confusion, engagement, and head-nods. We then implemented AffectiveSpotlight, a Microsoft Teams bot that analyzes facial responses and head gestures of audience members and dynamically spotlights the most expressive ones. In a within-subjects study with 14 groups (N=117), we observed that the system made presenters significantly more aware of their audience, speak for a longer period of time, and self-assess the quality of their talk more similarly to the audience members, compared to two control conditions (randomly-selected spotlight and default platform UI). We provide design recommendations for future affective interfaces for online presentations based on feedback from the study.
Title: Hidden Interaction Techniques: Concealed Information Acquisition and Texting on Smartphones and Wearables
Authors: family: Mäkelä
given: Ville; family: Kleine
given: Johannes; family: Hood
given: Maxine; family: Alt
given: Florian; family: Schmidt
given: Albrecht
Venue: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Keywords: smartwatches, smartphones, ubiquitous computing, subtle interaction, hidden interaction, interaction design, mobile computing, interaction techniques

Abstract: There are many situations where using personal devices is not socially acceptable, or where nearby people present a privacy risk. For these situations, we explore the concept of hidden interaction techniques through two prototype applications. HiddenHaptics allows users to receive information through vibrotactile cues on a smartphone, and HideWrite allows users to write text messages by drawing on a dimmed smartwatch screen. We conducted three user studies to investigate whether, and how, these techniques can be used without being exposed. Our primary findings are (1) users can effectively hide their interactions while attending to a social situation, (2) users seek to interact when another person is speaking, and they also tend to hide the interaction using their body or furniture, and (3) users can sufficiently focus on the social situation despite their interaction, whereas non-users feel that observing the user hinders their ability to focus on the social activity.
Title: Tea, Earl Grey, Hot: Designing Speech Interactions from the Imagined Ideal of Star Trek
Authors: family: Axtell
given: Benett; family: Munteanu
given: Cosmin
Venue: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Keywords: Star Trek, Voice user interfaces, Conversational analysis, Speech interaction design

Abstract: Speech is now common in daily interactions with our devices, thanks to voice user interfaces (VUIs) like Alexa. Despite their seeming ubiquity, designs often do not match users’ expectations. Science fiction, which is known to influence design of new technologies, has included VUIs for decades. Star Trek: The Next Generation is a prime example of how people envisioned ideal VUIs. Understanding how current VUIs live up to Star Trek’s utopian technologies reveals mismatches between current designs and user expectations, as informed by popular fiction. Combining conversational analysis and VUI user analysis, we study voice interactions with the Enterprise’s computer and compare them to current interactions. Independent of futuristic computing power, we find key design-based differences: Star Trek interactions are brief and functional, not conversational, they are highly multimodal and context-driven, and there is often no spoken computer response. From this, we suggest paths to better align VUIs with user expectations.
Title: Firefox Voice: An Open and Extensible Voice Assistant Built Upon the Web
Authors: family: Cambre
given: Julia; family: Williams
given: Alex C; family: Razi
given: Afsaneh; family: Bicking
given: Ian; family: Wallin
given: Abraham; family: Tsai
given: Janice; family: Kulkarni
given: Chinmay; family: Kaye
given: Jofish
Venue: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Keywords: CUI, browser extension, conversational user interface, open source, voice assistant

Abstract: Voice assistants are fundamentally changing the way we access information. However, voice assistants still leverage little about the web beyond simple search results. We introduce Firefox Voice, a novel voice assistant built on the open web ecosystem with an aim to expand access to information available via voice. Firefox Voice is a browser extension that enables users to use their voice to perform actions such as setting timers, navigating the web, and reading a webpage’s content aloud. Through an iterative development process and use by over 12,000 active users, we find that users see voice as a way to accomplish certain browsing tasks efficiently, but struggle with discovering functionality and frequently discontinue use. We conclude by describing how Firefox Voice enables the development of novel, open web-powered voice-driven experiences.
Title: Acceptability of Speech and Silent Speech Input Methods in Private and Public
Authors: family: Pandey
given: Laxmi; family: Hasan
given: Khalad; family: Arif
given: Ahmed Sabbir
Venue: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Keywords: voice assistant, contactless interaction, silent speech, input and interaction, speech, social acceptance

Abstract: Silent speech input converts non-acoustic features like tongue and lip movements into text. It has been demonstrated as a promising input method on mobile devices and has been explored for a variety of audiences and contexts where the acoustic signal is unavailable (e.g., people with speech disorders) or unreliable (e.g., noisy environment). Though the method shows promise, very little is known about peoples’ perceptions regarding using it. In this work, first, we conduct two user studies to explore users’ attitudes towards the method with a particular focus on social acceptance and error tolerance. Results show that people perceive silent speech as more socially acceptable than speech input and are willing to tolerate more errors with it to uphold privacy and security. We then conduct a third study to identify a suitable method for providing real-time feedback on silent speech input. Results show users find an abstract feedback method effective and significantly more private and secure than a commonly used video feedback method.
Title: MeetingCoach: An Intelligent Dashboard for Supporting Effective & Inclusive Meetings
Authors: family: Samrose
given: Samiha; family: McDuff
given: Daniel; family: Sim
given: Robert; family: Suh
given: Jina; family: Rowan
given: Kael; family: Hernandez
given: Javier; family: Rintel
given: Sean; family: Moynihan
given: Kevin; family: Czerwinski
given: Mary
Venue: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Keywords: feedback, meeting, sensing, video-conferencing, group

Abstract: Video-conferencing is essential for many companies, but its limitations in conveying social cues can lead to ineffective meetings. We present MeetingCoach, an intelligent post-meeting feedback dashboard that summarizes contextual and behavioral meeting information. Through an exploratory survey (N=120), we identified important signals (e.g., turn taking, sentiment) and used these insights to create a wireframe dashboard. The design was evaluated with in situ participants (N=16) who helped identify the components they would prefer in a post-meeting dashboard. After recording video-conferencing meetings of eight teams over four weeks, we developed an AI system to quantify the meeting features and created personalized dashboards for each participant. Through interviews and surveys (N=23), we found that reviewing the dashboard helped improve attendees’ awareness of meeting dynamics, with implications for improved effectiveness and inclusivity. Based on our findings, we provide suggestions for future feedback system designs of video-conferencing meetings.
Title: Proxemics and Social Interactions in an Instrumented Virtual Reality Workshop
Authors: family: Williamson
given: Julie; family: Li
given: Jie; family: Vinayagamoorthy
given: Vinoba; family: Shamma
given: David A.; family: Cesar
given: Pablo
Venue: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Keywords: Virtual Meetings, Social Signal Processing, Virtual Environments, Interviews.

Abstract: Virtual environments (VEs) can create collaborative and social spaces, which are increasingly important in the face of remote work and travel reduction. Recent advances, such as more open and widely available platforms, create new possibilities to observe and analyse interaction in VEs. Using a custom instrumented build of Mozilla Hubs to measure position and orientation, we conducted an academic workshop to facilitate a range of typical workshop activities. We analysed social interactions during a keynote, small group breakouts, and informal networking/hallway conversations. Our mixed-methods approach combined environment logging, observations, and semi-structured interviews. The results demonstrate how small and large spaces influenced group formation, shared attention, and personal space, where smaller rooms facilitated more cohesive groups while larger rooms made small group formation challenging but personal space more flexible. Beyond our findings, we show how the combination of data and insights can fuel collaborative spaces’ design and deliver more effective virtual workshops.
Title: Eliciting and Analysing Users’ Envisioned Dialogues with Perfect Voice Assistants
Authors: family: Völkel
given: Sarah Theres; family: Buschek
given: Daniel; family: Eiband
given: Malin; family: Cowan
given: Benjamin R.; family: Hussmann
given: Heinrich
Venue: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Keywords: personality, Adaptation, conversational agent, voice assistant, dialogue

Abstract: We present a dialogue elicitation study to assess how users envision conversations with a perfect voice assistant (VA). In an online survey, N=205 participants were prompted with everyday scenarios, and wrote the lines of both user and VA in dialogues that they imagined as perfect. We analysed the dialogues with text analytics and qualitative analysis, including number of words and turns, social aspects of conversation, implied VA capabilities, and the influence of user personality. The majority envisioned dialogues with a VA that is interactive and not purely functional; it is smart, proactive, and has knowledge about the user. Attitudes diverged regarding the assistant’s role as well as it expressing humour and opinions. An exploratory analysis suggested a relationship with personality for these aspects, but correlations were low overall. We discuss implications for research and design of future VAs, underlining the vision of enabling conversational UIs, rather than single command “Q&As”.
Title: Experiencing Simulated Confrontations in Virtual Reality
Authors: family: Dickinson
given: Patrick; family: Jones
given: Arthur; family: Christian
given: Wayne; family: Westerside
given: Andrew; family: Mulloy
given: Francis; family: Gerling
given: Kathrin; family: Hicks
given: Kieran; family: Wilson
given: Liam; family: Parke
given: Adrian
Venue: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Keywords: confrontational behaviour, Virtual reality, virtual character

Abstract: The use of virtual reality (VR) to simulate confrontational human behaviour has significant potential for use in training, where the recreation of uncomfortable feelings may help users to prepare for challenging real-life situations. In this paper we present a user study (n=68) in which participants experienced simulated confrontational behaviour performed by a virtual character either in immersive VR, or on a 2D display. Participants reported a higher elevation in anxiety in VR, which correlated positively with a perceived sense of physical space. Character believability was influenced negatively by visual elements of the simulation, and positively by behavioural elements, which complements findings from previous work. We recommend the use of VR for simulations of confrontational behaviour, where a realistic emotional response is part of the intended experience. We also discuss incorporation of domain knowledge of human behaviours, and carefully crafted motion-captured sequences, to increase users’ sense of believability.