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Conventional methods of assessing attitudes towards climate change are limited in capturing authentic opinions, primarily stemming from a lack of context-specific assessment strategies and an overreliance on simplistic surveys. Game-based Assessments (GBA) have demonstrated the ability to overcome these issues by immersing participants in engaging gameplay within carefully crafted, scenario-based environments. Concurrently, advancements in AI and Natural Language Processing (NLP) show promise in enhancing the gamified testing environment, achieving this by generating context-aware, human-like dialogues that contribute to a more natural and effective assessment. Our study introduces a new technique for probing climate change attitudes by actualizing a GPT-driven chatbot system in harmony with a game design depicting a futuristic climate scenario. The correlation analysis reveals an assimilation effect, where players' post-game climate awareness tends to align with their in-game perceptions. Key predictors of pro-climate attitudes are identified as traits like 'Openness' and 'Agreeableness', and a preference for democratic values., Comment: 22 pages, 6 figures, Accepted by CHI Conference on Human Factors in Computing Systems 2024

Extended Reality (XR) allows in-situ previewing of designs to be manufactured through Personal Fabrication (PF). These in-situ interactions exhibit advantages for PF, like incorporating the environment into the design process. However, design-for-fabrication in XR often happens through either highly complex 3D-modeling or is reduced to rudimentary adaptations of crowd-sourced models. We present pARam, a tool combining parametric designs (PDs) and XR, enabling in-situ configuration of artifacts for PF. In contrast to modeling- or search-focused approaches, pARam supports customization through embodied and practical inputs (e.g., gestures, recommendations) and evaluation (e.g., lighting estimation) without demanding complex 3D-modeling skills. We implemented pARam for HoloLens 2 and evaluated it (n=20), comparing XR and desktop conditions. Users succeeded in choosing context-related parameters and took their environment into account for their configuration using pARam. We reflect on the prospects and challenges of PDs in XR to streamline complex design methods for PF while retaining suitable expressivity., Comment: 17 pages, 17 figures. Accepted to ACM CHI 2024 (ACM CHI conference on Human Factors in Computing Systems)

Advances in ubiquitous computing have enabled end-user authoring of context-aware policies (CAPs) that control smart devices based on specific contexts of the user and environment. However, authoring CAPs accurately and avoiding run-time errors is challenging for end-users as it is difficult to foresee CAP behaviors under complex real-world conditions. We propose Fast-Forward Reality, an Extended Reality (XR) based authoring workflow that enables end-users to iteratively author and refine CAPs by validating their behaviors via simulated unit test cases. We develop a computational approach to automatically generate test cases based on the authored CAP and the user's context history. Our system delivers each test case with immersive visualizations in XR, facilitating users to verify the CAP behavior and identify necessary refinements. We evaluated Fast-Forward Reality in a user study (N=12). Our authoring and validation process improved the accuracy of CAPs and the users provided positive feedback on the system usability., Comment: 17 pages, 7 figures, ACM CHI 2024 Full Paper

Communicating design implications is common within the HCI community when publishing academic papers, yet these papers are rarely read and used by designers. One solution is to use design cards as a form of translational resource that communicates valuable insights from papers in a more digestible and accessible format to assist in design processes. However, creating design cards can be time-consuming, and authors may lack the resources/know-how to produce cards. Through an iterative design process, we built a system that helps create design cards from academic papers using an LLM and text-to-image model. Our evaluation with designers (N=21) and authors of selected papers (N=12) revealed that designers perceived the design implications from our design cards as more inspiring and generative, compared to reading original paper texts, and the authors viewed our system as an effective way of communicating their design implications. We also propose future enhancements for AI-generated design cards.

Extended Reality (XR) interfaces offer engaging user experiences, but their effective design requires a nuanced understanding of user behavior and preferences. This knowledge is challenging to obtain without the widespread adoption of XR devices. We introduce MineXR, a design mining workflow and data analysis platform for collecting and analyzing personalized XR user interaction and experience data. MineXR enables elicitation of personalized interfaces from participants of a data collection: for any particular context, participants create interface elements using application screenshots from their own smartphone, place them in the environment, and simultaneously preview the resulting XR layout on a headset. Using MineXR, we contribute a dataset of personalized XR interfaces collected from 31 participants, consisting of 695 XR widgets created from 178 unique applications. We provide insights for XR widget functionalities, categories, clusters, UI element types, and placement. Our open-source tools and data support researchers and designers in developing future XR interfaces., Comment: 17 pages, 18 figures, Proceedings of the 2024 CHI Conference on Human Factors in Computing Systems

This paper explores the integration of causal pathway diagrams (CPD) into human-centered design (HCD), investigating how these diagrams can enhance the early stages of the design process. A dedicated CPD plugin for the online collaborative whiteboard platform Miro was developed to streamline diagram creation and offer real-time AI-driven guidance. Through a user study with designers (N=20), we found that CPD's branching and its emphasis on causal connections supported both divergent and convergent processes during design. CPD can also facilitate communication among stakeholders. Additionally, we found our plugin significantly reduces designers' cognitive workload and increases their creativity during brainstorming, highlighting the implications of AI-assisted tools in supporting creative work and evidence-based designs.

Large language models (LLMs) are widely deployed in various downstream tasks, e.g., auto-completion, aided writing, or chat-based text generation. However, the considered output candidates of the underlying search algorithm are under-explored and under-explained. We tackle this shortcoming by proposing a tree-in-the-loop approach, where a visual representation of the beam search tree is the central component for analyzing, explaining, and adapting the generated outputs. To support these tasks, we present generAItor, a visual analytics technique, augmenting the central beam search tree with various task-specific widgets, providing targeted visualizations and interaction possibilities. Our approach allows interactions on multiple levels and offers an iterative pipeline that encompasses generating, exploring, and comparing output candidates, as well as fine-tuning the model based on adapted data. Our case study shows that our tool generates new insights in gender bias analysis beyond state-of-the-art template-based methods. Additionally, we demonstrate the applicability of our approach in a qualitative user study. Finally, we quantitatively evaluate the adaptability of the model to few samples, as occurring in text-generation use cases., Comment: 24 pages paper, 4 pages references, 3 pages appendix, 8 figures

Tracking kinematic chains has many uses from healthcare to virtual reality. Inertial measurement units, IMUs, are well-recognised for their body tracking capabilities, however, existing solutions rely on gravity and often magnetic fields for drift correction. As humanity's presence in space increases, systems that don't rely on gravity or magnetism are required. We aim to demonstrate the viability of IMU body tracking in a microgravity environment by showing that gravity and magnetism are not necessary for correcting gyroscope-based dead-reckoning drift. We aim to build and evaluate an end-to-end solution accomplishing this. A novel algorithm is developed that compensates for drift using local accelerations alone, without needing gravity or magnetism. Custom PCB sensor, IMU, nodes are created and combined into a body-sensor-network to implement the algorithm and the system is evaluated to determine its strengths and weaknesses. Dead-reckoning alone is accurate to within 1 degree for 30s. The drift correction solution can correct large drifts in yaw within 4 seconds of lateral accelerations to within 3.3 degrees RMSE. Correction accuracy when drift-free and under motion is 1.1 degrees RSME. We demonstrate that gyroscopic drift can be compensated for in a kinematic chain by making use of local acceleration information and often-discarded centripetal and tangential acceleration information, even in the absence of gravitational and magnetic fields. Therefore, IMU body tracking is a viable technology for use in microgravity environments., Comment: 19 pages, 7 figures

This paper presents our work to enhance the background music (BGM) in DareFightingICE by adding adaptive features. The adaptive BGM consists of three different categories of instruments playing the BGM of the winner sound design from the 2022 DareFightingICE Competition. The BGM adapts by changing the volume of each category of instruments. Each category is connected to a different element of the game. We then run experiments to evaluate the adaptive BGM by using a deep reinforcement learning AI agent that only uses audio as input (Blind DL AI). The results show that the performance of the Blind DL AI improves while playing with the adaptive BGM as compared to playing without the adaptive BGM., Comment: This is an updated version of our IEEE CoG 2023 paper (https://ieeexplore.ieee.org/document/10333245). This version has revised the description of the association between the distance between the two players (PD) and the instrument's volume on page 2. arXiv admin note: substantial text overlap with arXiv:2303.15734

Investigating the increasingly popular domain of short video consumption, this study focuses on the impact of Opinion Polarization (OP), a significant factor in the digital landscape influencing public opinions and social interactions. We analyze OP's effect on viewers' perceptions and behaviors, finding that traditional feedback metrics like likes and watch time fail to fully capture and measure OP. Addressing this gap, our research utilizes Electroencephalogram (EEG) signals to introduce a novel, non-invasive approach for evaluating neural responses to OP, affecting perception and cognition. Empirical analysis reveals OP's considerable impact on viewers' emotions, evidenced by changes in brain activity. Our findings also highlight the potential of EEG data in predicting exposure to polarized short video content, offering a new perspective on the dynamics of short video consumption and a unique method for quantifying OP's effects., Comment: 9 pages, 8 figures

This paper presents a new and improved DareFightingICE platform, a fighting game platform with a focus on visually impaired players (VIPs), in the Unity game engine. It also introduces the separation of the DareFightingICE Competition into two standalone competitions called DareFightingICE Sound Design Competition and DareFightingICE AI Competition--at the 2024 IEEE Conference on Games (CoG)--in which a new platform will be used. This new platform is an enhanced version of the old DareFightingICE platform, having a better audio system to convey 3D sound and a better way to send audio data to AI agents. With this enhancement and by utilizing Unity, the new DareFightingICE platform is more accessible in terms of adding new features for VIPs and future audio research. This paper also improves the evaluation method for evaluating sound designs in the Sound Design Competition which will ensure a better sound design for VIPs as this competition continues to run at future CoG. To the best of our knowledge, both of our competitions are first of their kind, and the connection between the competitions to mutually improve the entries' quality with time makes these competitions an important part of representing an often overlooked segment within the broader gaming community, VIPs., Comment: This paper describes a new competition platform using Unity for our competitions at the 2024 IEEE Conference on Games (CoG 2024). It was accepted for presentation at CoG 2024. However, we recently discovered a much more effective way to do this task without using Unity, leading to our decision to withdraw the paper from CoG 2024 and ArXiv

Large language models (LLMs) are shaping a new user interface (UI) paradigm in writing tools by enabling users to generate text through prompts. This paradigm shifts some creative control from the user to the system, thereby diminishing the user's authorship and autonomy in the writing process. To restore autonomy, we introduce Textfocals, a UI prototype designed to investigate a human-centered approach that emphasizes the user's role in writing. Textfocals supports the writing process by providing LLM-generated summaries, questions, and advice (i.e., LLM views) in a sidebar of a text editor, encouraging reflection and self-driven revision in writing without direct text generation. Textfocals' UI affordances, including contextually adaptive views and scaffolding for prompt selection and customization, offer a novel way to interact with LLMs where users maintain full authorship of their writing. A formative user study with Textfocals showed promising evidence that this approach might help users develop underdeveloped ideas, cater to the rhetorical audience, and clarify their writing. However, the study also showed interaction design challenges related to document navigation and scoping, prompt engineering, and context management. Our work highlights the breadth of the design space of writing support interfaces powered by generative AI that maintain authorship integrity., Comment: 15 pages, 2 figures; Accepted to 5th Workshop on Human-AI Co-Creation with Generative Models (HAI-GEN) at ACM IUI 2024

Video games elicit emotions which can be influenced by color stimuli as shown by previous studies. However, little research has been conducted on whether this applies to mobile games played by adolescents. Therefore, we examined the influence of color stimuli hue and saturation on mobile game play. Adolescents (n=21) played a mobile platformer game with varying hue and saturation per level for about 25 minutes. We gathered data on emotional states after each level using the Self-Assessment Manikin questionnaire, recorded time spent in each level, and collected participant self-reports on their video game experience. We performed statistical tests, such as ANOVA, which depict no significant influence of hue and/or saturation on the emotional state of our players. We conclude that it is possible that color alone is not an effective measure for eliciting emotion in mobile games, and further research is needed to consider measures such as time spent in the game and screen size, as these are unique to mobile games. There was a noticeable variance in emotional response between male and female players, with a significant interaction of hue and saturation among male players for valence ratings. This may be an indication that color preference influences perceived pleasantness., Comment: 17 pages, 12 figures, 1 table

The Burrows-Wheeler Transform (BWT) is an efficient invertible text transformation algorithm with the properties of tending to group identical characters together in a run, and enabling search of the text. This transformation has extensive uses particularly in lossless compression algorithms, indexing, and within bioinformatics for sequence alignment tasks. There has been recent interest in minimizing the number of identical character runs ($r$) for a transform and in finding useful alphabet orderings for the sorting step of the matrix associated with the BWT construction. This motivates the inspection of many transforms while developing algorithms. However, the full Burrows-Wheeler matrix is $O(n^2)$ space and therefore very difficult to display and inspect for large input sizes. In this paper we present a graphical user interface (GUI) for working with BWTs, which includes features for searching for matrix row prefixes, skipping over sections in the right-most column (the transform), and displaying BWTs while exploring alphabet orderings with the goal of minimizing the number of runs., Comment: 8 pages, 2 figures

Fidelity describes how closely a replication resembles the original. It can be helpful to analyze how faithful interactions in virtual reality (VR) are to a reference interaction. In prior research, fidelity has been restricted to the simulation of reality - also called realism. Our definition includes other reference interactions, such as superpowers or fiction. Interaction fidelity is a multilayered concept. Unfortunately, different aspects of fidelity have either not been distinguished in scientific discourse or referred to with inconsistent terminology. Therefore, we present the Interaction Fidelity Model (IntFi Model). Based on the human-computer interaction loop, it systematically covers all stages of VR interactions. The conceptual model establishes a clear structure and precise definitions of eight distinct components. It was reviewed through interviews with fourteen VR experts. We provide guidelines, diverse examples, and educational material to universally apply the IntFi Model to any VR experience. We identify common patterns and propose foundational research opportunities., Comment: 34 pages incl. references and appendix

The growing availability of generative AI technologies such as large language models (LLMs) has significant implications for creative work. This paper explores twofold aspects of integrating LLMs into the creative process - the divergence stage of idea generation, and the convergence stage of evaluation and selection of ideas. We devised a collaborative group-AI Brainwriting ideation framework, which incorporated an LLM as an enhancement into the group ideation process, and evaluated the idea generation process and the resulted solution space. To assess the potential of using LLMs in the idea evaluation process, we design an evaluation engine and compared it to idea ratings assigned by three expert and six novice evaluators. Our findings suggest that integrating LLM in Brainwriting could enhance both the ideation process and its outcome. We also provide evidence that LLMs can support idea evaluation. We conclude by discussing implications for HCI education and practice., Comment: Conditionally Accepted to CHI24. 27 pages

Conversational Agents (CAs) have increasingly been integrated into everyday life, sparking significant discussions on social media. While previous research has examined public perceptions of AI in general, there is a notable lack in research focused on CAs, with fewer investigations into cultural variations in CA perceptions. To address this gap, this study used computational methods to analyze about one million social media discussions surrounding CAs and compared people's discourses and perceptions of CAs in the US and China. We find Chinese participants tended to view CAs hedonically, perceived voice-based and physically embodied CAs as warmer and more competent, and generally expressed positive emotions. In contrast, US participants saw CAs more functionally, with an ambivalent attitude. Warm perception was a key driver of positive emotions toward CAs in both countries. We discussed practical implications for designing contextually sensitive and user-centric CAs to resonate with various users' preferences and needs., Comment: 17 pages, 4 figures, 7 tables

Interacting with pedestrians understandably and efficiently is one of the toughest challenges faced by autonomous vehicles (AVs) due to the limitations of current algorithms and external human-machine interfaces (eHMIs). In this paper, we design eHMIs based on gestures inspired by the most popular method of interaction between pedestrians and human drivers. Eight common gestures were selected to convey AVs' yielding or non-yielding intentions at uncontrolled crosswalks from previous literature. Through a VR experiment (N1 = 31) and a following online survey (N2 = 394), we discovered significant differences in the usability of gesture-based eHMIs compared to current eHMIs. Good gesture-based eHMIs increase the efficiency of pedestrian-AV interaction while ensuring safety. Poor gestures, however, cause misinterpretation. The underlying reasons were explored: ambiguity regarding the recipient of the signal and whether the gestures are precise, polite, and familiar to pedestrians. Based on this empirical evidence, we discuss potential opportunities and provide valuable insights into developing comprehensible gesture-based eHMIs in the future to support better interaction between AVs and other road users., Comment: 26 pages,22 figures

Mindfulness practice has many mental and physical well-being benefits. With the increased popularity of live stream technologies and the impact of COVID-19, many people have turned to live stream tools to participate in online meditation sessions. To better understand the practices, challenges, and opportunities in live-stream meditation, we conducted a three-month autoethnographic study, during which two researchers participated in live-stream meditation sessions as the audience. Then we conducted a follow-up semi-structured interview study with 10 experienced live meditation teachers who use different live-stream tools. We found that live meditation, although having a weaker social presence than in-person meditation, facilitates attendees in establishing a practice routine and connecting with other meditators. Teachers use live streams to deliver the meditation practice to the world which also enhances their practice and brand building. We identified the challenges of using live-stream tools for meditation from the perspectives of both audiences and teachers, and provided design recommendations to better utilize live meditation as a resource for mental wellbeing., Comment: Preprint for CSCW24

The rapid development of musical AI technologies has expanded the creative potential of various musical activities, ranging from music style transformation to music generation. However, little research has investigated how musical AIs can support music therapists, who urgently need new technology support. This study used a mixed method, including semi-structured interviews and a participatory design approach. By collaborating with music therapists, we explored design opportunities for musical AIs in music therapy. We presented the co-design outcomes involving the integration of musical AIs into a music therapy process, which was developed from a theoretical framework rooted in emotion-focused therapy. After that, we concluded the benefits and concerns surrounding music AIs from the perspective of music therapists. Based on our findings, we discussed the opportunities and design implications for applying musical AIs to music therapy. Our work offers valuable insights for developing human-AI collaborative music systems in therapy involving complex procedures and specific requirements., Comment: 20 pages, 7 figures

Reading is a challenging task for low vision people. While conventional low vision aids (e.g., magnification) offer certain support, they cannot fully address the difficulties faced by low vision users, such as locating the next line and distinguishing similar words. To fill this gap, we present GazePrompt, a gaze-aware reading aid that provides timely and targeted visual and audio augmentations based on users' gaze behaviors. GazePrompt includes two key features: (1) a Line-Switching support that highlights the line a reader intends to read; and (2) a Difficult-Word support that magnifies or reads aloud a word that the reader hesitates with. Through a study with 13 low vision participants who performed well-controlled reading-aloud tasks with and without GazePrompt, we found that GazePrompt significantly reduced participants' line switching time, reduced word recognition errors, and improved their subjective reading experiences. A follow-up silent-reading study showed that GazePrompt can enhance users' concentration and perceived comprehension of the reading contents. We further derive design considerations for future gaze-based low vision aids.

Recent large language models (LLMs) offer the potential to support public health monitoring by facilitating health disclosure through open-ended conversations but rarely preserve the knowledge gained about individuals across repeated interactions. Augmenting LLMs with long-term memory (LTM) presents an opportunity to improve engagement and self-disclosure, but we lack an understanding of how LTM impacts people's interaction with LLM-driven chatbots in public health interventions. We examine the case of CareCall -- an LLM-driven voice chatbot with LTM -- through the analysis of 1,252 call logs and interviews with nine users. We found that LTM enhanced health disclosure and fostered positive perceptions of the chatbot by offering familiarity. However, we also observed challenges in promoting self-disclosure through LTM, particularly around addressing chronic health conditions and privacy concerns. We discuss considerations for LTM integration in LLM-driven chatbots for public health monitoring, including carefully deciding what topics need to be remembered in light of public health goals., Comment: Accepted to ACM CHI 2024 as a full paper

Hand gesture recognition is becoming a more prevalent mode of human-computer interaction, especially as cameras proliferate across everyday devices. Despite continued progress in this field, gesture customization is often underexplored. Customization is crucial since it enables users to define and demonstrate gestures that are more natural, memorable, and accessible. However, customization requires efficient usage of user-provided data. We introduce a method that enables users to easily design bespoke gestures with a monocular camera from one demonstration. We employ transformers and meta-learning techniques to address few-shot learning challenges. Unlike prior work, our method supports any combination of one-handed, two-handed, static, and dynamic gestures, including different viewpoints. We evaluated our customization method through a user study with 20 gestures collected from 21 participants, achieving up to 97% average recognition accuracy from one demonstration. Our work provides a viable path for vision-based gesture customization, laying the foundation for future advancements in this domain.

Personal devices have adopted diverse authentication methods, including biometric recognition and passcodes. In contrast, headphones have limited input mechanisms, depending solely on the authentication of connected devices. We present Moonwalk, a novel method for passive user recognition utilizing the built-in headphone accelerometer. Our approach centers on gait recognition; enabling users to establish their identity simply by walking for a brief interval, despite the sensor's placement away from the feet. We employ self-supervised metric learning to train a model that yields a highly discriminative representation of a user's 3D acceleration, with no retraining required. We tested our method in a study involving 50 participants, achieving an average F1 score of 92.9% and equal error rate of 2.3%. We extend our evaluation by assessing performance under various conditions (e.g. shoe types and surfaces). We discuss the opportunities and challenges these variations introduce and propose new directions for advancing passive authentication for wearable devices.

While maps provide upfront content, this might not always be the most effective way for users to remember information. With the proliferation of interactive displays for tourists and visitors in public spaces, we can create a more playful user experience with maps than just exploring them. Adding interactions with the map could also help users retain more information as they use them. In this paper, we investigated whether completing a jigsaw puzzle of a map supports users in retaining more information about a specific map. The results of a between-subject study with a sample of n=28 indicate that additional interaction helped improve mean scores of textual and spatial recall but not visual recall. However, the results are not statistically significant, and the topic is subject to further investigation. Our findings contribute to discussions on using interactive touchscreen displays in similar learning scenarios involving memory retention., Comment: HCI SI 2023: Human-Computer Interaction Slovenia 2023, January 26, 2024, Maribor, Slovenia, 2 tables, 4 figures, 10 pages

We ideate a future design workflow that involves AI technology. Drawing from activity and communication theory, we attempt to isolate the new value large AI models can provide design compared to past technologies. We arrive at three affordances -- dynamic grounding, constructive negotiation, and sustainable motivation -- that summarize latent qualities of natural language-enabled foundation models that, if explicitly designed for, can support the process of design. Through design fiction, we then imagine a future interface as a diegetic prototype, the story of Squirrel Game, that demonstrates each of our three affordances in a realistic usage scenario. Our design process, terminology, and diagrams aim to contribute to future discussions about the relative affordances of AI technology with regard to collaborating with human designers., Comment: 12 pages, 4 figures

Early detection of dental disease is crucial to prevent adverse outcomes. Today, dental X-rays are currently the most accurate gold standard for dental disease detection. Unfortunately, regular X-ray exam is still a privilege for billions of people around the world. In this paper, we ask: "Can we develop a low-cost sensing system that enables dental self-examination in the comfort of one's home?" This paper presents ToMoBrush, a dental health sensing system that explores using off-the-shelf sonic toothbrushes for dental condition detection. Our solution leverages the fact that a sonic toothbrush produces rich acoustic signals when in contact with teeth, which contain important information about each tooth's status. ToMoBrush extracts tooth resonance signatures from the acoustic signals to characterize varied dental health conditions of the teeth. We evaluate ToMoBrush on 19 participants and dental-standard models for detecting common dental problems including caries, calculus, and food impaction, achieving a detection ROC-AUC of 0.90, 0.83, and 0.88 respectively. Interviews with dental experts validate ToMoBrush's potential in enhancing at-home dental healthcare.

Landscape renderings are realistic images of landscape sites, allowing stakeholders to perceive better and evaluate design ideas. While recent advances in Generative Artificial Intelligence (GAI) enable automated generation of landscape renderings, the end-to-end methods are not compatible with common design processes, leading to insufficient alignment with design idealizations and limited cohesion of iterative landscape design. Informed by a formative study for comprehending design requirements, we present PlantoGraphy, an iterative design system that allows for interactive configuration of GAI models to accommodate human-centered design practice. A two-stage pipeline is incorporated: first, concretization module transforms conceptual ideas into concrete scene layouts with a domain-oriented large language model; and second, illustration module converts scene layouts into realistic landscape renderings using a fine-tuned low-rank adaptation diffusion model. PlantoGraphy has undergone a series of performance evaluations and user studies, demonstrating its effectiveness in landscape rendering generation and the high recognition of its interactive functionality., Comment: 27 pages, 14 figures, The ACM CHI 2024 conference

Toxicity detection algorithms, originally designed with reactive content moderation in mind, are increasingly being deployed into proactive end-user interventions to moderate content. Through a socio-technical lens and focusing on contexts in which they are applied, we explore the use of these algorithms in proactive moderation systems. Placing a toxicity detection algorithm in an imagined virtual mobile keyboard, we critically explore how such algorithms could be used to proactively reduce the sending of toxic content. We present findings from design workshops conducted with four distinct stakeholder groups and find concerns around how contextual complexities may exasperate inequalities around content moderation processes. Whilst only specific user groups are likely to directly benefit from these interventions, we highlight the potential for other groups to misuse them to circumvent detection, validate and gamify hate, and manipulate algorithmic models to exasperate harm.

With the growing popularity of conversational agents based on large language models (LLMs), we need to ensure their behaviour is ethical and appropriate. Work in this area largely centres around the 'HHH' criteria: making outputs more helpful and honest, and avoiding harmful (biased, toxic, or inaccurate) statements. Whilst this semantic focus is useful when viewing LLM agents as mere mediums or output-generating systems, it fails to account for pragmatic factors that can make the same speech act seem more or less tactless or inconsiderate in different social situations. With the push towards agentic AI, wherein systems become increasingly proactive in chasing goals and performing actions in the world, considering the pragmatics of interaction becomes essential. We propose an interactional approach to ethics that is centred on relational and situational factors. We explore what it means for a system, as a social actor, to treat an individual respectfully in a (series of) interaction(s). Our work anticipates a set of largely unexplored risks at the level of situated social interaction, and offers practical suggestions to help agentic LLM technologies treat people well.

We introduce DrawTalking, an approach to building and controlling interactive worlds by sketching and speaking. It emphasizes user control and flexibility, and gives programming-like capability without requiring code. We built a prototype to demonstrate it. An early open-ended study shows the mechanics resonate and are applicable to many creative-exploratory use cases, with the potential to inspire and inform research in future natural interfaces for creative exploration and authoring.

Mental health is a pressing concern in today's digital age, particularly among youth who are deeply intertwined with technology. Despite the influx of technology solutions addressing mental health issues, youth often remain sidelined during the design process. While co-design methods have been employed to improve participation by youth, many such initiatives are limited to design activities and lack training for youth to research and develop solutions for themselves. In this case study, we detail our 8-week remote, collaborative research initiative called Youth WellTech, designed to facilitate remote co-design sprints aimed at equipping youth with the tools and knowledge to envision and design tech futures for their own communities. We pilot this initiative with 12 student technology evangelists across 8 countries globally to foster the sharing of mental health challenges and diverse perspectives. We highlight insights from our experiences running this global program remotely, its structure, and recommendations for co-research., Comment: Case Study, 13 pages

Researchers are exploring Augmented Reality (AR) interfaces for online robot programming to streamline automation and user interaction in variable manufacturing environments. This study introduces an AR interface for online programming and data visualization that integrates the human in the randomized robot path planning, reducing the inherent randomness of the methods with human intervention. The interface uses holographic items which correspond to physical elements to interact with a redundant manipulator. Utilizing Rapidly Random Tree Star (RRT*) and Spherical Linear Interpolation (SLERP) algorithms, the interface achieves end-effector s progression through collision-free path with smooth rotation. Next, Sequential Quadratic Programming (SQP) achieve robot s configurations for this progression. The platform executes the RRT* algorithm in a loop, with each iteration independently exploring the shortest path through random sampling, leading to variations in the optimized paths produced. These paths are then demonstrated to AR users, who select the most appropriate path based on the environmental context and their intuition. The accuracy and effectiveness of the interface are validated through its implementation and testing with a seven Degree-OF-Freedom (DOF) manipulator, indicating its potential to advance current practices in robot programming. The validation of this paper include two implementations demonstrating the value of human-in-the-loop and context awareness in robotics., Comment: 10 pages, 13 figures

Data exploration is an important aspect of the workflow of mixed-methods researchers, who conduct both qualitative and quantitative analysis. However, there currently exists few tools that adequately support both types of analysis simultaneously, forcing researchers to context-switch between different tools and increasing their mental burden when integrating the results. To address this gap, we propose a unified environment that facilitates mixed-methods analysis in a computational notebook-based settings. We conduct a scenario study with three HCI mixed-methods researchers to gather feedback on our design concept and to understand our users' needs and requirements., Comment: Appeared at 1st ACM CHI Workshop on Human-Notebook Interactions

Three-dimensional immersive sketching for content creation and modeling has been studied for some time. However, research in this domain mainly focused on CAVE-like scenarios. These setups can be expensive and offer a narrow interaction space. Building more affordable setups using head-mounted displays is possible, allowing greater immersion and a larger space for user physical movements. This paper presents a fully immersive environment using bi-manual gestures to sketch and create content freely in the virtual world. This approach can be applied to many scenarios, allowing people to express their ideas or review existing designs. To cope with known motor difficulties and inaccuracy of freehand 3D sketching, we explore proxy geometry and a laser-like metaphor to draw content directly from models and create content surfaces. Our current prototype offers 24 cubic meters for movement, limited by the room size. It features infinite virtual drawing space through pan and scale techniques and is larger than the typical 6-sided cave at a fraction of the cost. In a preliminary study conducted with architects and engineers, our system showed a clear promise as a tool for sketching and 3D content creation in virtual reality with a great emphasis on bi-manual gestures., Comment: 9 pages; 15 Figures

Segmenting dance video into short movements is a popular way to easily understand dance choreography. However, it is currently done manually and requires a significant amount of effort by experts. That is, even if many dance videos are available on social media (e.g., TikTok and YouTube), it remains difficult for people, especially novices, to casually watch short video segments to practice dance choreography. In this paper, we propose a method to automatically segment a dance video into each movement. Given a dance video as input, we first extract visual and audio features: the former is computed from the keypoints of the dancer in the video, and the latter is computed from the Mel spectrogram of the music in the video. Next, these features are passed to a Temporal Convolutional Network (TCN), and segmentation points are estimated by picking peaks of the network output. To build our training dataset, we annotate segmentation points to dance videos in the AIST Dance Video Database, which is a shared database containing original street dance videos with copyright-cleared dance music. The evaluation study shows that the proposed method (i.e., combining the visual and audio features) can estimate segmentation points with high accuracy. In addition, we developed an application to help dancers practice choreography using the proposed method., Comment: 9 pages, 11 figures

Parsons problems are a type of programming activity that present learners with blocks of existing code and requiring them to arrange those blocks to form a program rather than write the code from scratch. Micro Parsons problems extend this concept by having students assemble segments of code to form a single line of code rather than an entire program. Recent investigations into micro Parsons problems have primarily focused on supporting learners leaving open the question of micro Parsons efficacy as an exam item and how students perceive it when preparing for exams. To fill this gap, we included a variety of micro Parsons problems on four exams in an introductory programming course taught in Python. We use Item Response Theory to investigate the difficulty of the micro Parsons problems as well as the ability of the questions to differentiate between high and low ability students. We then compare these results to results for related questions where students are asked to write a single line of code from scratch. Finally, we conduct a thematic analysis of the survey responses to investigate how students' perceptions of micro Parsons both when practicing for exams and as they appear on exams., Comment: This work is to appear in ITiCSE 2024. Both authors contributed equally to this research

This essay proposes and explores the concept of Collage for the design of AI writing tools, transferred from avant-garde literature with four facets: 1) fragmenting text in writing interfaces, 2) juxtaposing voices (content vs command), 3) integrating material from multiple sources (e.g. text suggestions), and 4) shifting from manual writing to editorial and compositional decision-making, such as selecting and arranging snippets. The essay then employs Collage as an analytical lens to analyse the user interface design of recent AI writing tools, and as a constructive lens to inspire new design directions. Finally, a critical perspective relates the concerns that writers historically expressed through literary collage to AI writing tools. In a broad view, this essay explores how literary concepts can help advance design theory around AI writing tools. It encourages creators of future writing tools to engage not only with new technological possibilities, but also with past writing innovations., Comment: 19 pages, 7 figures, 2 tables, ACM DIS 2024

Humans use collaborative robots as tools for accomplishing various tasks. The interaction between humans and robots happens in tight shared workspaces. However, these machines must be safe to operate alongside humans to minimize the risk of accidental collisions. Ensuring safety imposes many constraints, such as reduced torque and velocity limits during operation, thus increasing the time to accomplish many tasks. However, for applications such as using collaborative robots as haptic interfaces with intermittent contacts for virtual reality applications, speed limitations result in poor user experiences. This research aims to improve the efficiency of a collaborative robot while improving the safety of the human user. We used Gaussian process models to predict human hand motion and developed strategies for human intention detection based on hand motion and gaze to improve the time for the robot and human security in a virtual environment. We then studied the effect of prediction. Results from comparisons show that the prediction models improved the robot time by 3\% and safety by 17\%. When used alongside gaze, prediction with Gaussian process models resulted in an improvement of the robot time by 2\% and the safety by 13\%., Comment: 16 pages

The paper presents the second part of a precise description of the prototype that has been developed in the course of the ISAC project over the last two decades. This part describes the "specify-phase", while the first part describing the "solve-phase" is already published. In the specify-phase a student interactively constructs a formal specification. The ISAC prototype implements formal specifications as established in theoretical computer science, however, the input language for the construction avoids requiring users to have knowledge of logic; this makes the system useful for various engineering faculties (and also for high school). The paper discusses not only ISAC's design of the specify-phase in detail, but also gives a brief introduction to implementation with the aim of advertising the re-use of formal frameworks (inclusive respective front-ends) with their generic tools for language definition and their rich pool of software components for formal mathematics., Comment: In Proceedings ThEdu'23, arXiv:2404.03709

Raster Forge is a Python library and graphical user interface for raster data manipulation and analysis. The tool is focused on remote sensing applications, particularly in wildfire management. It allows users to import, visualize, and process raster layers for tasks such as image compositing or topographical analysis. For wildfire management, it generates fuel maps using predefined models. Its impact extends from disaster management to hydrological modeling, agriculture, and environmental monitoring. Raster Forge can be a valuable asset for geoscientists and researchers who rely on raster data analysis, enhancing geospatial data processing and visualization across various disciplines.

We investigate how to use AI-based analytics to support design education. The analytics at hand measure multiscale design, that is, students' use of space and scale to visually and conceptually organize their design work. With the goal of making the analytics intelligible to instructors, we developed a research artifact integrating a design analytics dashboard with design instances, and the design environment that students use to create them. We theorize about how Suchman's notion of mutual intelligibility requires contextualized investigation of AI in order to develop findings about how analytics work for people. We studied the research artifact in 5 situated course contexts, in 3 departments. A total of 236 students used the multiscale design environment. The 9 instructors who taught those students experienced the analytics via the new research artifact. We derive findings from a qualitative analysis of interviews with instructors regarding their experiences. Instructors reflected on how the analytics and their presentation in the dashboard have the potential to affect design education. We develop research implications addressing: (1) how indexing design analytics in the dashboard to actual design work instances helps design instructors reflect on what they mean and, more broadly, is a technique for how AI-based design analytics can support instructors' assessment and feedback experiences in situated course contexts; and (2) how multiscale design analytics, in particular, have the potential to support design education. By indexing, we mean linking which provides context, here connecting the numbers of the analytics with visually annotated design work instances., Comment: 22 pages, 4 figures, Submitted to ACM DIS

Telepresence robots can be used to support users to navigate an environment remotely and share the visiting experience with their social partners. Although such systems allow users to see and hear the remote environment and communicate with their partners via live video feed, this does not provide enough awareness of the environment and their remote partner's activities. In this paper, we introduce an awareness framework for collaborative locomotion in scenarios of onsite and remote users visiting a place together. From an observational study of small groups of people visiting exhibitions, we derived four design goals for enhancing the environmental and social awareness between social partners, and developed a set of awareness-enhancing techniques to add to a standard telepresence robot - named TeleAware robot. Through a controlled experiment simulating a guided exhibition visiting task, TeleAware robot showed the ability to lower the workload, facilitate closer social proximity, and improve mutual awareness and social presence compared with the standard one. We discuss the impact of mobility and roles of local and remote users, and provide insights for the future design of awareness-enhancing telepresence robot systems that facilitate collaborative locomotion., Comment: 33 pages, 12 figures

Large language models (LLMs) are transforming human-computer interaction and conceptions of artificial intelligence (AI) with their impressive capacities for conversing and reasoning in natural language. There is growing interest in whether LLMs have theory of mind (ToM); the ability to reason about the mental and emotional states of others that is core to human social intelligence. As LLMs are integrated into the fabric of our personal, professional and social lives and given greater agency to make decisions with real-world consequences, there is a critical need to understand how they can be aligned with human values. ToM seems to be a promising direction of inquiry in this regard. Following the literature on the role and impacts of human ToM, this paper identifies key areas in which LLM ToM will show up in human:LLM interactions at individual and group levels, and what opportunities and risks for alignment are raised in each. On the individual level, the paper considers how LLM ToM might manifest in goal specification, conversational adaptation, empathy and anthropomorphism. On the group level, it considers how LLM ToM might facilitate collective alignment, cooperation or competition, and moral judgement-making. The paper lays out a broad spectrum of potential implications and suggests the most pressing areas for future research.

In this paper, we explore the generation of one-liner jokes through multi-step reasoning. Our work involved reconstructing the process behind creating humorous one-liners and developing a working prototype for humor generation. We conducted comprehensive experiments with human participants to evaluate our approach, comparing it with human-created jokes, zero-shot GPT-4 generated humor, and other baselines. The evaluation focused on the quality of humor produced, using human labeling as a benchmark. Our findings demonstrate that the multi-step reasoning approach consistently improves the quality of generated humor. We present the results and share the datasets used in our experiments, offering insights into enhancing humor generation with artificial intelligence., Comment: ICCC 2024

Despite demonstrating robust capabilities in performing tasks related to general-domain data-operation tasks, Large Language Models (LLMs) may exhibit shortcomings when applied to domain-specific tasks. We consider the design of domain-specific AI-powered data analysis tools from two dimensions: interaction and user agency. We implemented two design probes that fall on the two ends of the two dimensions: an open-ended high agency (OHA) prototype and a structured low agency (SLA) prototype. We conducted an interview study with nine data scientists to investigate (1) how users perceived the LLM outputs for data analysis assistance, and (2) how the two test design probes, OHA and SLA, affected user behavior, performance, and perceptions. Our study revealed insights regarding participants' interactions with LLMs, how they perceived the results, and their desire for explainability concerning LLM outputs, along with a noted need for collaboration with other users, and how they envisioned the utility of LLMs in their workflow., Comment: CHI'24 Late-Breaking Work

Counterfactual examples are useful for exploring the decision boundaries of machine learning models and determining feature attributions. How can we apply counterfactual-based methods to analyze and explain LLMs? We identify the following key challenges. First, the generated textual counterfactuals should be meaningful and readable to users and thus can be mentally compared to draw conclusions. Second, to make the solution scalable to long-form text, users should be equipped with tools to create batches of counterfactuals from perturbations at various granularity levels and interactively analyze the results. In this paper, we tackle the above challenges and contribute 1) a novel algorithm for generating batches of complete and meaningful textual counterfactuals by removing and replacing text segments in different granularities, and 2) LLM Analyzer, an interactive visualization tool to help users understand an LLM's behaviors by interactively inspecting and aggregating meaningful counterfactuals. We evaluate the proposed algorithm by the grammatical correctness of its generated counterfactuals using 1,000 samples from medical, legal, finance, education, and news datasets. In our experiments, 97.2% of the counterfactuals are grammatically correct. Through a use case, user studies, and feedback from experts, we demonstrate the usefulness and usability of the proposed interactive visualization tool.

HCI is fundamentally occupied with the problem of the future and understanding the acceptability and adoption challenges that future and emerging technologies face from the viewpoint of their being situated in everyday life. This paper explicates an interdisciplinary approach towards addressing the problem and understanding acceptability and adoption challenges that leverages design fiction as breaching experiment. Design fiction is an arts based approach to exploring the future, breaching experiments a social science method for explicating common sense reasoning and surfacing the taken for granted expectations societys members have and hold about situated action and how it should work. Both approaches have previously been employed in HCI, but this the first time they have been combined to enable HCI researchers to provoke through design the acceptability and adoption challenges that confront future and emerging technologies., Comment: 22 pages plus references

This paper explores the changes that pervasive AI is having on the nature of combat. We look beyond the substitution of AI for experts to an approach where complementary human and machine abilities are blended. Using historical and modern examples, we show how autonomous weapons systems can be effectively managed by teams of human "AI Operators" combined with AI/ML "Proxy Operators." By basing our approach on the principles of complementation, we provide for a flexible and dynamic approach to managing lethal autonomous systems. We conclude by presenting a path to achieving an integrated vision of machine-speed combat where the battlefield AI is operated by AI Operators that watch for patterns of behavior within battlefield to assess the performance of lethal autonomous systems. This approach enables the development of combat systems that are likely to be more ethical, operate at machine speed, and are capable of responding to a broader range of dynamic battlefield conditions than any purely autonomous AI system could support., Comment: 15 pages, 2 figures

WhatsApp has become a pivotal communication tool in India, transcending cultural boundaries and deeply integrating into the nation's digital landscape. Meta's introduction of WhatsApp for Business aligns seamlessly with the platform's popularity, offering businesses a crucial tool. However, the monetization plans pose challenges, particularly for smaller businesses, in balancing revenue goals with accessibility. This study, employing discourse analysis, examines Meta's infrastructuring of WhatsApp in India, emphasizing the dynamic interplay of technological, social, and cultural dimensions. Consequently, it highlights potential power differences caused by the deployment of WhatsApp for Business followed by its gradual but significant modifications, encouraging scholars to investigate the implications and ethics of rapid technological changes, particularly for marginalized users., Comment: 7 pages, Accepted to ACM CHI 2024 as an Extended Abstract. In Proceedings of the 2024 CHI Conference on Human Factors in Computing Systems (CHI'24)