Cognitive Forcing for Better Decision-Making: Reducing Overreliance on AI Systems Through Partial ExplanationsIn AI-assisted decision-making, explanations aim to enhance transparency and user trust but can also lead to negligence. In two separate studies, we explore the use of partial explanations to activate cognitive forcing and increase user engagement. In Study~I ($N = 264$), we present participants with weighted graphs and ask them to identify the shortest paths. In Study~II ($N = 210$), participants correct spelling and grammar mistakes in short text segments. In both studies, we provide a solution suggestion accompanied by either no explanation, a full explanation, or a partial explanation. Our results show that partial explanations reduce overreliance on incorrect AI suggestions, performing significantly better than the baseline but not as well as full explanations. Individuals with a high need for cognition benefit more from AI explanations and consequently perform better. Our work suggests that partial explanations can be valuable in domains where reducing overreliance on AI is critical, like medical diagnosis. It also underscores the need to consider explanation effectiveness across different task difficulties, a factor often overlooked in contemporary human-AI studies.2025SJSander de Jong et al.Humans vs. AI for Decision MakingCSCW
"Nuisance is Better Than Nothing?": Exploring How Pedestrians and Cyclists Perceive Automated E-Scooter Alerts in Shared SpacesElectric scooters (e-scooters) offer flexible urban mobility but raise safety concerns in shared spaces. This study investigates how e-scooters can better communicate their presence to pedestrians and cyclists in shared active mobility environments. A focus group with e-scooter riders identified notifying others of arrival as a key challenge. To address this, we co-designed audio and visual alerts in a participatory workshop and evaluated them in a real-world Wizard of Oz (WoZ) study involving live encounters. WoZ self-report data showed that voice and bell alerts were rated significantly higher in visibility, safety, communication, and acceptance than continuous sounds and flashing lights. These findings were supported by video analysis, which captured clear spatial responses such as turning or moving aside. Cyclists rated alerts as more distracting than pedestrians. Eye-tracking data revealed increased pedestrian attention during overtaking. By combining self-reports, video, and gaze data, we provide in-situ evidence and design recommendations to improve e-scooter signalling and reduce conflict. The dataset, including anonymized ratings, fixation data, alert designs, and analysis scripts, is available at https://github.com/HiruniNuwanthika/User-Perception-Evaluation-Escooter-Alerts.git.2025HKHiruni Nuwanthika Kegalle et al.External HMI (eHMI) — Communication with Pedestrians & CyclistsMicromobility (E-bike, E-scooter) InteractionMobileHCI
Assessing Susceptibility Factors of Confirmation Bias in News Feed ReadingIndividuals tend to apply preferences and beliefs as heuristics to effectively sift through the sheer amount of information available online. Such tendencies, however, often result in cognitive biases, which can skew judgment and open doors for manipulation. In this work, we investigate how individual and contextual factors lead to instances of confirmation bias when seeking, evaluating, and recalling polarising information. We conducted a lab study, in which we exposed participants to opinions on controversial issues through a Twitter-like news feed. We found that low-effortful thinking, strong political beliefs, and content conveying a strong issue amplify the occurrences of confirmation bias, leading to skewed information processing and recall. We discuss how the adverse effects of confirmation bias can be mitigated by taking bias-susceptibility into account. Specifically, social media platforms could aim to reduce strong expressions and integrate media literacy-building mechanisms, as low-effortful thinking styles and strong political beliefs render individuals especially susceptible to cognitive biases.2025NBNattapat Boonprakong et al.University of Melbourne, School of Computing and Information SystemsPrivacy Perception & Decision-MakingMisinformation & Fact-CheckingCHI
How Do HCI Researchers Study Cognitive Biases? A Scoping ReviewComputing systems are increasingly designed to adapt to users' cognitive states and mental models. Yet, cognitive biases affect how humans form such models and, therefore, they can impact their interactions with computers. To better understand this interplay, we conducted a scoping review to chart how Human-Computer Interaction (HCI) researchers study cognitive biases. Our findings show that computing systems not only have the potential to induce and amplify cognitive biases but also can be designed to steer users' behaviour and decision-making by capitalising on biases. We describe how HCI researchers develop algorithms and sensing methods to detect and quantify the effects of cognitive biases and discuss how we can use their understanding to inform system design. In this paper, we outline a research agenda for more theory-grounded research and highlight ethical issues when researching and designing computing systems with cognitive biases in mind as they affect real-world behaviour.2025NBNattapat Boonprakong et al.University of Melbourne, School of Computing and Information SystemsExplainable AI (XAI)Chronic Disease Self-Management (Diabetes, Hypertension, etc.)Privacy Perception & Decision-MakingCHI
Actual Achieved Gain and Optimal Perceived Gain: Modeling Human Take-over Decisions Towards Automated Vehicles' SuggestionsDriver decision quality in take-overs is critical for effective human-Autonomous Driving System (ADS) collaboration. However, current research lacks detailed analysis of its variations. This paper introduces two metrics--Actual Achieved Gain (AAG) and Optimal Perceived Gain (OPG)--to assess decision quality, with OPG representing optimal decisions and AAG reflecting actual outcomes. Both are calculated as weighted averages of perceived gains and losses, influenced by ADS accuracy. Study 1 (N=315) used a 21-point Thurstone scale to measure perceived gains and losses—key components of AAG and OPG—across typical tasks: route selection, overtaking, and collision avoidance. Studies 2 (N=54) and 3 (N=54) modeled decision quality under varying ADS accuracy and decision time. Results show with sufficient time (>3.5s), AAG converges towards OPG, indicating rational decision-making, while limited time leads to intuitive and deterministic choices. Study 3 also linked AAG-OPG deviations to irrational behaviors. An intervention study (N=8) and a pilot (N=4) employing voice alarms and multi-modal alarms based on these deviations demonstrated AAG's potential to improve decision quality.2025SZHaihua Zhang et al.Tsinghua University, Institute for Network Sciences and CyberspaceAutomated Driving Interface & Takeover DesignHead-Up Display (HUD) & Advanced Driver Assistance Systems (ADAS)AI-Assisted Decision-Making & AutomationCHI
Vision-Based Multimodal Interfaces: A Survey and Taxonomy for Enhanced Context-Aware System DesignThe recent surge in artificial intelligence, particularly in multimodal processing technology, has advanced human-computer interaction, by altering how intelligent systems perceive, understand, and respond to contextual information (i.e., context awareness). Despite such advancements, there is a significant gap in comprehensive reviews examining these advances, especially from a multimodal data perspective, which is crucial for refining system design. This paper addresses a key aspect of this gap by conducting a systematic survey of data modality-driven Vision-based Multimodal Interfaces (VMIs). VMIs are essential for integrating multimodal data, enabling more precise interpretation of user intentions and complex interactions across physical and digital environments. Unlike previous task- or scenario-driven surveys, this study highlights the critical role of the visual modality in processing contextual information and facilitating multimodal interaction. Adopting a design framework moving from the whole to the details and back, it classifies VMIs across dimensions, providing insights for developing effective, context-aware systems.2025YHYongquan 'Owen' Hu et al.University of New South WalesContext-Aware ComputingUbiquitous ComputingCHI
AlphaPIG: The Nicest Way to Prolong Interactive Gestures in Extended RealityMid-air gestures serve as a common interaction modality across Extended Reality (XR) applications, enhancing engagement and ownership through intuitive body movements. However, prolonged arm movements induce shoulder fatigue—known as "Gorilla Arm Syndrome"—degrading user experience and reducing interaction duration. Although existing ergonomic techniques derived from Fitts' law (such as reducing target distance, increasing target width, and modifying control-display gain) provide some fatigue mitigation, their implementation in XR applications remains challenging due to the complex balance between user engagement and physical exertion. We present \textit{AlphaPIG}, a meta-technique designed to \textbf{P}rolong \textbf{I}nteractive \textbf{G}estures by leveraging real-time fatigue predictions. AlphaPIG assists designers in extending and improving XR interactions by enabling automated fatigue-based interventions. Through adjustment of intervention timing and intensity decay rate, designers can explore and control the trade-off between fatigue reduction and potential effects such as decreased body ownership. We validated AlphaPIG's effectiveness through a study (N=22) implementing the widely-used Go-Go technique. Results demonstrated that AlphaPIG significantly reduces shoulder fatigue compared to non-adaptive Go-Go, while maintaining comparable perceived body ownership and agency. Based on these findings, we discuss positive and negative perceptions of the intervention. By integrating real-time fatigue prediction with adaptive intervention mechanisms, AlphaPIG constitutes a critical first step towards creating fatigue-aware applications in XR.2025YLZhuying Li et al.Monash UniversityFull-Body Interaction & Embodied InputImmersion & Presence ResearchCHI
MAPLE: Mobile App Prediction Leveraging Large Language Model EmbeddingsKhaokaew等人提出MAPLE，利用大型语言模型嵌入预测用户移动应用使用，提升推荐准确性。2024YKYonchanok Khaokaew et al.Human-LLM CollaborationUbiComp
Understanding Physiological Responses of Students Over Different CoursesStudent engagement plays a vital role in academic success with high engagement often linked to positive educational outcomes. Traditionally, student engagement is measured through self-reports, which are both labour-intensive and not real-time. An emerging alternative is monitoring physiological signals such as Electrodermal Activity (EDA) and Inter-Beat Interval (IBI), which reflect students' emotional and cognitive states. In this research, we analyzed these signals from 23 students wearing Empatica E4 devices in real-world scenarios. Diverging from previous studies focused on lab settings or specific subjects, we examined physiological synchrony at the intra-student level across various courses. We also assessed how different courses influence physiological responses and identified consistent temporal patterns. Our findings show unique physiological response patterns among students, enhancing our understanding of student engagement dynamics. This opens up possibilities for tailoring educational strategies based on unobtrusive sensing data to optimize learning outcomes.2024SASoundariya Ananthan et al.Mental Health Apps & Online Support CommunitiesBiosensors & Physiological MonitoringUbiComp
WorkR: Occupation Inference for Intelligent Task AssistanceOccupation information can be utilized by digital assistants to provide occupation-specific personalized task support, including interruption management, task planning, and recommendations. Prior research in the digital workplace assistant domain requires users to input their occupation information for effective support. However, as many individuals switch between multiple occupations daily, current solutions falter without continuous user input. To address this, this study introduces WorkR, a framework that leverages passive sensing to capture pervasive signals from various task activities, addressing three challenges: the lack of a passive sensing architecture, personalization of occupation characteristics, and discovering latent relationships among occupation variables. We argue that signals from application usage, movements, social interactions, and the environment can inform a user's occupation. WorkR uses a Variational Autoencoder (VAE) to derive latent features for training models to infer occupations. Our experiments with an anonymized, context-rich activity and task log dataset demonstrate that our models can accurately infer occupations with more than 91% accuracy across six ISO occupation categories.2024YKYonchanok Khaokaew et al.Head-Up Display (HUD) & Advanced Driver Assistance Systems (ADAS)Human-LLM CollaborationPrivacy by Design & User ControlUbiComp
GustosonicSense: Towards understanding the design of playful gustosonic eating experiencesThe pleasure that often comes with eating can be further enhanced with intelligent technology, as the field of human-food interaction suggests. However, knowledge on how to design such pleasure-supporting eating systems is limited. To begin filling this knowledge gap, we designed “GustosonicSense”, a novel gustosonic eating system that utilizes wireless earbuds for sensing different eating and drinking actions with a machine learning algorithm and trigger playful sounds as a way to facilitate pleasurable eating experiences. We present the findings from our design and a study that revealed how we can support the "stimulation", "hedonism", and "reflexivity" for playful human-food interactions. Ultimately, with our work, we aim to support interaction designers in facilitating playful experiences with food.2024YWYan Wang et al.Monash University, Monash UniversityFood Culture & Food InteractionCHI
Reading Between the Lines: Identifying the Linguistic Markers of Anhedonia for the Stratification of DepressionStratifying depressed individuals may help to improve recovery rates by identifying the subgroups who would benefit from targeted treatments. Detecting depressed individuals with prominent anhedonia (i.e. lack of pleasure) may be one effective approach, given these individuals experience poorer treatment outcomes. This paper explores the linguistic features associated with anhedonia among depressed adults. Over 9 weeks, 218 individuals with depressive symptoms completed a fortnightly psychometric measure of depression (PHQ-9) and provided text data (SMS, social media posts, expressive essays, emotion diaries, personal letters). Linguistic features were examined using LIWC-22. Greater use of discrepancy words was significantly associated with higher anhedonia, but in SMS data only. Machine learning showed some utility for predicting increased anhedonia, with discrepancy words the most important linguistic feature in the model. Discrepancy words were not found to be associated with overall depression scores. These results suggest that this linguistic feature may show some promise for the stratification of anhedonic depression.2024BOBridianne O'Dea et al.University of New South WalesCognitive Impairment & Neurodiversity (Autism, ADHD, Dyslexia)CHI
MicroCam: Leveraging Smartphone Microscope Camera for Context-Aware Contact Surface Sensing"The primary focus of this research is the discreet and subtle everyday contact interactions between mobile phones and their surrounding surfaces. Such interactions are anticipated to facilitate mobile context awareness, encompassing aspects such as dispensing medication updates, intelligently switching modes (e.g., silent mode), or initiating commands (e.g., deactivating an alarm). We introduce MicroCam, a contact-based sensing system that employs smartphone IMU data to detect the routine state of phone placement and utilizes a built-in microscope camera to capture intricate surface details. In particular, a natural dataset is collected to acquire authentic surface textures in situ for training and testing. Moreover, we optimize the deep neural network component of the algorithm, based on continual learning, to accurately discriminate between object categories (e.g., tables) and material constituents (e.g., wood). Experimental results highlight the superior accuracy, robustness and generalization of the proposed method. Lastly, we conducted a comprehensive discussion centered on our prototype, encompassing topics such as system performance and potential applications and scenarios." https://doi.org/10.1145/36109212023YHYongquan Hu et al.Context-Aware ComputingUbiquitous ComputingUbiComp
Iris: Passive Visible Light Positioning Using Light Spectral Information"We propose a novel Visible Light Positioning (VLP) method, called Iris, that leverages light spectral information (LSI) to localize individuals in a completely passive manner. This means that the user does not need to carry any device, and the existing lighting infrastructure remains unchanged. Our method uses a background subtraction approach to accurately detect changes in ambient LSI caused by human movement. Furthermore, we design a Convolutional Neural Network (CNN) capable of learning and predicting user locations from the LSI change data. To validate our approach, we implemented a prototype of Iris using a commercial-off-the-shelf light spectral sensor and conducted experiments in two typical real-world indoor environments: a 25 m2 one-bedroom apartment and a 13.3m × 8.4m office space. Our results demonstrate that Iris performs effectively in both artificial lighting at night and in highly dynamic natural lighting conditions during the day. Moreover, Iris outperforms the state-of-the-art passive VLP techniques significantly in terms of localization accuracy and the required density of light sensors. To reduce the overhead associated with multi-channel spectral sensing, we develop and validate an algorithm that can minimize the required number of spectral channels for a given environment. Finally, we propose a conditional Generative Adversarial Network (cGAN) that can artificially generate LSI and reduce data collection effort by 50% without sacrificing localization accuracy."https://doi.org/10.1145/36109132023JHYongquan 'Owen' Hu et al.Context-Aware ComputingUbiquitous ComputingUbiComp
Spectral-Loc: Indoor Localization Using Light Spectral Information"For indoor settings, we investigate the impact of location on the spectral distribution of the received light, i.e., the intensity of light for different wavelengths. Our investigations confirm that even under the same light source, different locations exhibit slightly different spectral distribution due to reflections from their localised environment containing different materials or colours. By exploiting this observation, we propose Spectral-Loc, a novel indoor localization system that uses light spectral information to identify the location of the device. With spectral sensors finding their way into the latest products and applications, such as white balancing in smartphone photography, Spectral-Loc can be readily deployed without requiring any additional hardware or infrastructure. We prototype Spectral-Loc using a commercial-off-the-shelf light spectral sensor, AS7265x, which can measure light intensity over 18 different wavelength sub-bands. We benchmark the localization accuracy of Spectral-Loc against the conventional light intensity sensors that provide only a single intensity value. Our evaluations over two different indoor spaces, a meeting room, and a large office space, demonstrate that the use of light spectral information significantly reduces the localization error for the different percentiles. https://dl.acm.org/doi/10.1145/3580878"2023YWYanxiang Wang et al.Geospatial & Map VisualizationContext-Aware ComputingUbiComp
RadarFoot: Fine-grain Ground Surface Context Awareness for Smart ShoesEveryday, billions of people use footwear for walking, running, or exercise. Of emerging interest are ``smart footwear'', which help users track gait, count steps or even analyse performance. However, such nascent footwear lack fine-grain ground surface context awareness, which could allow them to adapt to the conditions and create usable functions and experiences. Hence, this research aims to recognize the walking surface using a radar sensor embedded in a shoe, enabling ground context-awareness. Using data collected from 23 participants from an in-the-wild setting, we developed several classification models. We show that our model can detect five common terrain types with an accuracy of 80.0\% and further ten terrain types with an accuracy of 66.3\%, while moving. Importantly, it can detect the gait motion types such as `walking', `stepping up', `stepping down', `still', with an accuracy of 90\%. Finally, we present potential use cases and insights for future work based on such ground-aware smart shoes.2023DEDon Samitha Elvitigala et al.Biosensors & Physiological MonitoringContext-Aware ComputingUIST
Spatially Distributed Robot Sound: A Case StudyThe potential of spatial sound to immerse, guide, and affect humans is well-known, but has so far received little attention in the field of human-robot interaction. In this paper, we therefore explore how a robot emitting spatial sound affects a human’s impressions and behavior. Following a Research-through-Design (RtD) approach, we created two immersive robot sound designs which are emitted across the robot’s body and through speakers in the environment. In an evaluation study, participants interacted with the robot and shared their impressions through semi-structured interviews. Reactions showed that spatial robot sound had a notable effect on participant behavior and impressions, influencing, among others, how much attention they paid to the physical robot and how much animacy and agency they attributed to it. We report on our insights into how spatial sound may provide social robots with new ways to inform and engage the humans around them.2023FRFrederic Anthony Robinson et al.Social Robot InteractionDIS
A Study of Creative Development with an IoT-based Audiovisual System: Creative Strategies and Impacts for System DesignIn this paper we describe a qualitative study investigating how artists work with a scalable and distributed audio-visual installation system that utilises IoT technology. With no prior experience of the system invited artists incorporated the new technology according to a creative brief for a public performance. We examined how they (i) built an understanding of the technology's affordances, (ii) refined their creative goals, and (iii) deployed collaborative strategies to achieve creative outcomes. We examine how the artists worked from the examples we provided to integrate our audio-visual system and develop their creative work. We identify three distinct creative strategies and use these to suggest ways that the design of examples, presets and readymade configurations can be successfully integrated into interfaces for new creative technologies.2023KMKurt Mikolajczyk et al.Context-Aware ComputingDigital Art Installations & Interactive PerformanceC&C
Assessing Mapper Conflict in OpenStreetMap Using the Delphi Survey MethodStudies of mapper conflict in OpenStreetMap (OSM) have focused exclusively on cartographic vandalism and its effect on data quality. This paper takes a broader view on mapper conflict in OSM. Using a Delphi survey method, we collect qualitative data on perceived conflict from long-time OSM mappers. We ask mappers about four aspects of conflict in OSM: (1) topic of conflict, (2) factors leading to conflict, (3) effects of conflict, and (4) potential conflict management methods. Our results show that conflict in OSM can be explained by clashing values and opinions within and across different mapper subgroups and can be exacerbated by negative mapper behaviors. The boundaries of these subgroups, while implicit, are often defined by gender, mappers' geographic location, level of expertise, and mappers' professional affiliation. Based on these results, we discuss design options for OSM's existing public communication channels that often become foci of mapper conflict management.2023YCYoujin Choe et al.University of Melbourne, University of MelbourneCommunity Collaboration & WikipediaUser Research Methods (Interviews, Surveys, Observation)CHI
OmniSense: Exploring Novel Input Sensing and Interaction Techniques on Mobile Device with an Omni-Directional CameraAn omni-directional (360°) camera captures the entire viewing sphere surrounding its optical center. Such cameras are growing in use to create highly immersive content and viewing experiences. When such a camera is held by a user, the view includes the user's hand grip, finger, body pose, face, and the surrounding environment, providing a complete understanding of the visual world and context around it. This capability opens up numerous possibilities for rich mobile input sensing. In OmniSense, we explore the broad input design space for mobile devices with a built-in omni-directional camera and broadly categorize them into three sensing pillars: i) near device ii) around device and iii) surrounding device. In addition we explore potential use cases and applications that leverage these sensing capabilities to solve user needs. Following this, we develop a working system to put these concepts into action, by leveraging these sensing capabilities to enable potential use cases and applications. We studied the system in a technical evaluation and a preliminary user study to gain initial feedback and insights. Collectively these techniques illustrate how a single, omni-purpose sensor on a mobile device affords many compelling ways to enable expressive input, while also affording a broad range of novel applications that improve user experience during mobile interaction.2023HYHui-Shyong Yeo et al.HuaweiEye Tracking & Gaze InteractionImmersion & Presence Research360° Video & Panoramic ContentCHI