huashen218/convxai-cia-dataset huggingface dataset:
Title: “It Would Be Cool to Get Stampeded by Dinosaurs”: Analyzing Children's Conceptual Model of AR Headsets Through Co-Design
Authors: family: Woodward
given: Julia; family: Alemu
given: Feben; family: E. López Adames
given: Natalia; family: Anthony
given: Lisa; family: C. Yip
given: Jason; family: Ruiz
given: Jaime
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: participatory design, co-design, conceptual model, Augmented reality, children

Abstract: Children are being presented with augmented reality (AR) in different contexts, such as education and gaming. However, little is known about how children conceptualize AR, especially AR headsets. Prior work has shown that children's interaction behaviors and expectations of technological devices can be quite different from adults’. It is important to understand children's mental models of AR headsets to design more effective experiences for them. To elicit children's perceptions, we conducted four participatory design sessions with ten children on designing content for imaginary AR headsets. We found that children expect AR systems to be highly intelligent and to recognize and virtually transform surroundings to create immersive environments. Also, children are in favor of using these devices for difficult tasks but prefer to work on their own for easy tasks. Our work contributes new understanding on how children comprehend AR headsets and provides recommendations for designing future headsets for children.
Title: “I Didn’t Catch That, But I’ll Try My Best”: Anticipatory Error Handling in a Voice Controlled Game
Authors: family: Zargham
given: Nima; family: Pfau
given: Johannes; family: Schnackenberg
given: Tobias; family: Malaka
given: Rainer
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: Voice User Interfaces, Voice Interaction, Game Design, Speech-Based Systems, Voice-Controlled Game, Error Handling

Abstract: Advances in speech recognition, language processing and natural interaction have led to an increased industrial and academic interest. While the robustness and usability of such systems are steadily increasing, speech-based systems are still susceptible to recognition errors. This makes intelligent error handling of utmost importance for the success of those systems. In this work, we integrated anticipatory error handling for a voice-controlled video game where the game would perform a locally optimized action in respect to goal completion and obstacle avoidance, when a command is not recognized. We evaluated the user experience of our approach versus traditional, repetition-based error handling (). Our results indicate that implementing anticipatory error handling can improve the usability of a system, if it follows the intention of the user. Otherwise, it impairs the user experience, even when deciding for technically optimal decisions.
Title: The Impact of Latency on Navigation in a First-Person Perspective Game
Authors: family: Liu
given: Shengmei; family: Claypool
given: Mark
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: navigation, skill, user study, gamer, FPS, lag

Abstract: Competitive first-person shooter games are played over a network, where latency can degrade player performance. To better understand latency’s impact, a promising approach is to study how latency affects individual game actions, such as moving and shooting. While target selection (aiming and shooting at an opponent) is fairly well studied, navigation (moving an avatar into position) is not. This paper presents results from a 30-person user study that evaluates the impact of latency on first-person navigation using a custom “hide and seek” game that isolates avatar movement in a manner intended to be similar to movement in a first-person shooter game. Analysis of the results shows latency has pronounced effects on player performance (score and seek positioning), with subjective opinions on Quality of Experience following suit.
Title: Press A to Jump: Design Strategies for Video Game Learnability
Authors: family: Poretski
given: Lev; family: Tang
given: Anthony
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: visual cues, design strategies, learnability, Video games

Abstract: Learnability is a core aspect of software usability. Video games are not an exception, as game designers need to teach players how to play their creations. We analyzed 40 contemporary video games to identify how video games approach learning experiences. We found that games have advanced far beyond using simple tutorials or demonstration screens and adopt a range of repeatable and reusable design strategies using visual cues to facilitate learning. We provide a detailed descriptive framework of these design strategies, elucidating how and when they can be used, and describing how the visual cues are used to build them. Our research can be useful for both general HCI researchers and practitioners seeking to tap into the rich ideas from video game learnability design looking for practical solutions for their work.
Title: Trading Accuracy for Enjoyment? Data Quality and Player Experience in Data Collection Games
Authors: family: Gundry
given: David; family: Deterding
given: Sebastian
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: Applied Games, Human Computation Games, Games with a Purpose, Validity, Crowdsourcing Games, Human Subject Data

Abstract: Games have become a popular way of collecting human subject data, based on the premise that they are more engaging than surveys or experiments, but generate equally valid data. However, this premise has not been empirically tested. In response, we designed a game for eliciting linguistic data following Intrinsic Elicitation – a design approach aiming to minimise validity threats in data collection games – and compared it to an equivalent linguistics experiment as control. In a preregistered study and replication (n=96 and n=136), using two different ways of operationalising accuracy, the game generated substantially more enjoyment (d=.70,.73) and substantially less accurate data (d=-.68, -.40) – though still more accurate than random responding. We conclude that for certain data types data collection games may present a serious trade-off between participant enjoyment and data quality, identify possible causes of lower data quality for future research, reflect on our design approach, and urge games HCI researchers to use careful controls where appropriate.
Title: The Reward for Luck: Understanding the Effect of Random Reward Mechanisms in Video Games on Player Experience
Authors: family: Yin
given: Michael; family: Xiao
given: Robert
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: player experience, video games, reward systems

Abstract: Random Reward Mechanisms (RRMs) in video games are systems in which rewards are issued probabilistically upon certain trigger conditions, such as completing gameplay tasks, exceeding a playtime quota, or making in-game purchases. We investigated the relationship between RRM implementations and user experience. Video analysis of 35 RRM systems allowed for the creation of a classification system based on contrasting observed dimensions. Interviews with 14 video game players provided insights into how factors such as the affordances of non-optimal rewards and the trade-off between random luck and skill impact player perception and interaction with RRMs. We additionally investigated the relationship between auditory, visual, and gameplay design decisions and player expectations for RRM reward presentations, finding that the resources required to obtain the reward and the relative value of the reward impact its expected presentation. Finally, we applied our findings to propose design methodologies for creating engaging and significant RRM systems.
Title: ”Because AI is 100% right and safe”: User Attitudes and Sources of AI Authority in India
Authors: family: Kapania
given: Shivani; family: Siy
given: Oliver; family: Clapper
given: Gabe; family: SP
given: Azhagu Meena; family: Sambasivan
given: Nithya
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: algorithmic decision-making, artificial intelligence, perceptions of AI, India

Abstract: Most prior work on human-AI interaction is set in communities that indicate skepticism towards AI, but we know less about contexts where AI is viewed as aspirational. We investigated the perceptions around AI systems by drawing upon 32 interviews and 459 survey respondents in India. Not only do Indian users accept AI decisions (79.2% respondents indicate acceptance), we find a case of AI authority—AI has a legitimized power to influence human actions, without requiring adequate evidence about the capabilities of the system. AI authority manifested into four user attitudes of vulnerability: faith, forgiveness, self-blame, and gratitude, pointing to higher tolerance for system misfires, and introducing potential for irreversible individual and societal harm. We urgently call for calibrating AI authority, reconsidering success metrics and responsible AI approaches and present methodological suggestions for research and deployments in India.
Title: Capable but Amoral? Comparing AI and Human Expert Collaboration in Ethical Decision Making
Authors: family: Tolmeijer
given: Suzanne; family: Christen
given: Markus; family: Kandul
given: Serhiy; family: Kneer
given: Markus; family: Bernstein
given: Abraham
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: Trust, Human-AI Collaboration, Ethical AI, Responsibility

Abstract: While artificial intelligence (AI) is increasingly applied for decision-making processes, ethical decisions pose challenges for AI applications. Given that humans cannot always agree on the right thing to do, how would ethical decision-making by AI systems be perceived and how would responsibility be ascribed in human-AI collaboration? In this study, we investigate how the expert type (human vs. AI) and level of expert autonomy (adviser vs. decider) influence trust, perceived responsibility, and reliance. We find that participants consider humans to be more morally trustworthy but less capable than their AI equivalent. This shows in participants’ reliance on AI: AI recommendations and decisions are accepted more often than the human expert’s. However, AI team experts are perceived to be less responsible than humans, while programmers and sellers of AI systems are deemed partially responsible instead.
Title: AI-Moderated Decision-Making: Capturing and Balancing Anchoring Bias in Sequential Decision Tasks
Authors: family: Echterhoff
given: Jessica Maria; family: Yarmand
given: Matin; family: McAuley
given: Julian
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: Neural Networks, Bias, Decision-Making, Anchoring, Human-AI Interaction

Abstract: Decision-making involves biases from past experiences, which are difficult to perceive and eliminate. We investigate a specific type of anchoring bias, in which decision-makers are anchored by their own recent decisions, e.g. a college admission officer sequentially reviewing students. We propose an algorithm that identifies existing anchored decisions, reduces sequential dependencies to previous decisions, and mitigates decision inaccuracies post-hoc with 2% increased agreement to ground-truth on a large-scale college admission decision data set. A crowd-sourced study validates this algorithm on product preferences (5% increased agreement). To avoid biased decisions ex-ante, we propose a procedure that presents instances in an order that reduces anchoring bias in real-time. Tested in another crowd-sourced study, it reduces bias and increases agreement to ground-truth by 7%. Our work reinforces individuals with similar characteristics to be treated similarly, independent of when they were reviewed in the decision-making process.
Title: How Child Welfare Workers Reduce Racial Disparities in Algorithmic Decisions
Authors: family: Cheng
given: Hao-Fei; family: Stapleton
given: Logan; family: Kawakami
given: Anna; family: Sivaraman
given: Venkatesh; family: Cheng
given: Yanghuidi; family: Qing
given: Diana; family: Perer
given: Adam; family: Holstein
given: Kenneth; family: Wu
given: Zhiwei Steven; family: Zhu
given: Haiyi
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: machine learning, algorithmic biases, human-centered AI, algorithm-assisted decision-making, child welfare

Abstract: Machine learning tools have been deployed in various contexts to support human decision-making, in the hope that human-algorithm collaboration can improve decision quality. However, the question of whether such collaborations reduce or exacerbate biases in decision-making remains underexplored. In this work, we conducted a mixed-methods study, analyzing child welfare call screen workers’ decision-making over a span of four years, and interviewing them on how they incorporate algorithmic predictions into their decision-making process. Our data analysis shows that, compared to the algorithm alone, workers reduced the disparity in screen-in rate between Black and white children from 20% to 9%. Our qualitative data show that workers achieved this by making holistic risk assessments and adjusting for the algorithm’s limitations. Our analyses also show more nuanced results about how human-algorithm collaboration affects prediction accuracy, and how to measure these effects. These results shed light on potential mechanisms for improving human-algorithm collaboration in high-risk decision-making contexts.
Title: Expressive Auditory Gestures in a Voice-Based Pedagogical Agent
Authors: family: Ceha
given: Jessy; family: Law
given: Edith
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: rapport, music, agent, voice, interjections, education, motivation

Abstract: In this paper, we explore how expressive auditory gestures added to the speech of a pedagogical agent influence the human-agent relationship and learning outcomes. In a between-subjects experiment, 41 participants assumed the role of a tutor to teach a voice-based agent. The agent used either: expressive interjections (e.g.,“yay”, “hmm”, “oh”), brief expressive musical executions, or no auditory gestures at all (control condition), throughout the interaction. Overall, the results indicate that both gestures can positively affect the interaction, but in particular, interjections can significantly increase feelings of emotional rapport with the agent and enhance motivation in learners. The implications of our findings are discussed as our work adds to the understanding of conversational agent design and can be useful for education as well as other domains in which dialogue systems are used.
Title: To type or to speak? The effect of input modality on text understanding during note-taking
Authors: family: Khan
given: Anam Ahmad; family: Nawaz
given: Sadia; family: Newn
given: Joshua; family: Kelly
given: Ryan M.; family: Lodge
given: Jason M.; family: Bailey
given: James; family: Velloso
given: Eduardo
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: Voice notes, Input Modality, Digital learning, Note-taking tools

Abstract: Though recent technological advances have enabled note-taking through different modalities (e.g., keyboard, digital ink, voice), there is still a lack of understanding of the effect of the modality choice on learning. In this paper, we compared two note-taking input modalities—keyboard and voice—to study their effects on participants’ understanding of learning content. We conducted a study with 60 participants in which they were asked to take notes using voice or keyboard on two independent digital text passages while also making a judgment about their performance on an upcoming test. We built mixed-effects models to examine the effect of the note-taking modality on learners’ text comprehension, the content of notes and their meta-comprehension judgement. Our findings suggest that taking notes using voice leads to a higher conceptual understanding of the text when compared to typing the notes. We also found that using voice triggers generative processes that result in learners taking more elaborate and comprehensive notes. The findings of the study imply that note-taking tools designed for digital learning environments could incorporate voice as an input modality to promote effective note-taking and higher conceptual understanding of the text.
Title: Audio Matters Too: How Audial Avatar Customization Enhances Visual Avatar Customization
Authors: family: Kao
given: Dominic; family: Ratan
given: Rabindra; family: Mousas
given: Christos; family: Joshi
given: Amogh; family: Melcer
given: Edward F.
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: Customization, Games, Player Experience, Identification, Voice, Audio, Avatar

Abstract: Avatar customization is known to positively affect crucial outcomes in numerous domains. However, it is unknown whether audial customization can confer the same benefits as visual customization. We conducted a preregistered 2 x 2 (visual choice vs. visual assignment x audial choice vs. audial assignment) study in a Java programming game. Participants with visual choice experienced higher avatar identification and autonomy. Participants with audial choice experienced higher avatar identification and autonomy, but only within the group of participants who had visual choice available. Visual choice led to an increase in time spent, and indirectly led to increases in intrinsic motivation, immersion, time spent, future play motivation, and likelihood of game recommendation. Audial choice moderated the majority of these effects. Our results suggest that audial customization plays an important enhancing role vis-à-vis visual customization. However, audial customization appears to have a weaker effect compared to visual customization. We discuss the implications for avatar customization more generally across digital applications.
Title: “Elinor’s Talking to Me!”:Integrating Conversational AI into Children’s Narrative Science Programming
Authors: family: Xu
given: Ying; family: Vigil
given: Valery; family: Bustamante
given: Andres S.; family: Warschauer
given: Mark
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: conversational agents, science learning, children, Conversational AI, educational media

Abstract: Video programs are important, accessible educational resources for young children, especially those from an under-resourced backgrounds. These programs’ potential can be amplified if children are allowed to socially interact with media characters during their video watching. This paper presents the design and empirical investigation of interactive science-focused videos in which the main character, powered by a conversational agent, engaged in contingent conversation with children by asking children questions and providing responsive feedback. We found that children actively interacted with the media character in the conversational videos and their parents spontaneously provided support in the process. We also found that the children who watched the conversational video performed better in the immediate, episode-specific science assessment compared to their peers who watched the broadcast, non-interactive version of the same episode. Several design implications are discussed for using conversational technologies to better support child active learning and parent involvement in video watching.
Title: Supporting the Design of Smart Speakers to Foster a Sense of Ownership in Asian Indian Families
Authors: family: Garg
given: Radhika
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: sense of ownership, autonomy, self-identity, accountability, parents, self-efficacy, Google Home, territoriality, responsibility, Alexa, teenagers, smart speakers

Abstract: Smart speakers are increasingly being adopted by families in the U.S. In many cases, a smart speaker is shared by family members, which might make the sense of ownership uncertain. Through a diary and interview-based study with 20 Asian Indian parents and teenagers living in the U.S., this study through thematic analysis highlights various aspects of smart speakers that support or hinder the fulfilment of the psychological needs – self-efficacy, self-identity, territoriality, autonomy, and accountability and responsibility – that foster a sense of ownership. The paper also discusses the experiences of a cultural group that is not well represented in prior HCI work, and thus, it will add useful nuances and knowledge about inclusivity to the study of smart-speaker technology in HCI. Finally, it contributes six actionable design guidelines, including guidelines that relate to maintaining conversational context across different interactions and fostering a sense of ownership among users who share smart speakers, through the use of territorial markers.
Title: FAR: End-to-End Vibrotactile Distributed System Designed to Facilitate Affect Regulation in Children Diagnosed with Autism Spectrum Disorder Through Slow Breathing
Authors: family: Miri
given: Pardis; family: Arora
given: Mehul; family: Malhotra
given: Aman; family: Flory
given: Robert; family: Hu
given: Stephanie; family: Lowber
given: Ashley; family: Goyal
given: Ishan; family: Nguyen
given: Jacqueline; family: Hegarty
given: John P; family: Kohn
given: Marlo D; family: Schneider
given: David; family: Culbertson
given: Heather; family: Yamins
given: Daniel L. K.; family: Fung
given: Lawrence; family: Hardan
given: Antonio; family: Gross
given: James J.; family: Marzullo
given: Keith
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: Affect Regulation, Vibrotactile, Expert evaluation, Autism Spectrum Disorder, Heuristic evaluation, Haptic, Pacer, Affect, Slow-paced breathing, Anxiety, Wearable, Respiration

Abstract: To address difficulties with affect dysregulation in youth diagnosed with autism spectrum disorder (ASD), we designed and developed an end-to-end vibrotactile breathing pacer system and evaluated its usability. In this paper we describe the system architecture and the features we deployed for this system based on expert advice and reviews. Through piloting this system with one child diagnosed with ASD, we learned that our system was used in ways we did and did not anticipate. For example, the paced-breathing personalization procedure did not meet the attention span of the pilot participant but two instead of one pacer devices encouraged caregiver’s involvement. This paper details our learnings and concludes with a list of system design guidelines at the system architecture level. To the best of our knowledge, this is the first fully functional vibrotactile system designed for ASD children that withstood usability testing in vitro for two weeks.