Understanding the Effects of AI-based Credibility Indicators When People Are Influenced By Both Peers and ExpertsIn an era marked by rampant online misinformation, artificial intelligence (AI) technologies have emerged as tools to combat this issue. This paper examines the effects of AI-based credibility indicators in people’s online information processing under the social influence from both peers and "experts''. Via three pre-registered, randomized experiments, we confirm the effectiveness of accurate AI-based credibility indicators to enhance people's capability in judging information veracity and reduce their propensity to share false information, even under the influence from both laypeople peers and experts. Notably, these effects remain consistent regardless of whether experts' expertise is verified, with particularly significant impacts when AI predictions disagree with experts. However, the competence of AI moderates the effects, as incorrect predictions can mislead people. Furthermore, exploratory analyses suggest that under our experimental settings, the impact of the AI-based credibility indicator is larger than that of the expert's. Additionally, AI's influence on people is partially mediated through peer influence, although people automatically discount the opinions of their laypeople peers when seeing an agreement between AI and peers' opinions. We conclude by discussing the implications of utilizing AI to combat misinformation.2025ZLZhuoran Lu et al.Autodesk Research; Purdue University, Computer ScienceAI Ethics, Fairness & AccountabilityMisinformation & Fact-CheckingCHI
SonoHaptics: An Audio-Haptic Cursor for Gaze-Based Object Selection in XRWe introduce SonoHaptics, an audio-haptic cursor for gaze-based 3D object selection. SonoHaptics addresses challenges around providing accurate visual feedback during gaze-based selection in Extended Reality (XR), e.g., lack of world-locked displays in no- or limited-display smart glasses and visual inconsistencies. To enable users to distinguish objects without visual feedback, SonoHaptics employs the concept of cross-modal correspondence in human perception to map visual features of objects (color, size, position, material) to audio-haptic properties (pitch, amplitude, direction, timbre). We contribute data-driven models for determining cross-modal mappings of visual features to audio and haptic features, and a computational approach to automatically generate audio-haptic feedback for objects in the user's environment. SonoHaptics provides global feedback that is unique to each object in the scene, and local feedback to amplify differences between nearby objects. Our comparative evaluation shows that SonoHaptics enables accurate object identification and selection in a cluttered scene without visual feedback.2024HCHyunsung Cho et al.Mid-Air Haptics (Ultrasonic)Eye Tracking & Gaze InteractionSocial & Collaborative VRUIST
InstruMentAR: Auto-Generation of Augmented Reality Tutorials for Operating Digital Instruments Through Recording Embodied DemonstrationAugmented Reality tutorials, which provide necessary context by directly superimposing visual guidance on the physical referent, represent an effective way of scaffolding complex instrument operations. However, current AR tutorial authoring processes are not seamless as they require users to continuously alternate between operating instruments and interacting with virtual elements. We present InstruMentAR, a system that automatically generates AR tutorials through recording user demonstrations. We design a multimodal approach that fuses gestural information and hand-worn pressure sensor data to detect and register the user's step-by-step manipulations on the control panel. With this information, the system autonomously generates virtual cues with designated scales to respective locations for each step. Voice recognition and background capture are employed to automate the creation of text and images as AR content. For novice users receiving the authored AR tutorials, we facilitate immediate feedback through haptic modules. We compared InstruMentAR with traditional systems in the user study.2023ZLZiyi Liu et al.Purdue UniversityIn-Vehicle Haptic, Audio & Multimodal FeedbackHand Gesture RecognitionAR Navigation & Context AwarenessCHI
Ubi Edge: Authoring Edge-Based Opportunistic Tangible User Interfaces in Augmented RealityEdges are one of the most ubiquitous geometric features of physical objects. They provide accurate haptic feedback and easy-to-track features for camera systems, making them an ideal basis for Tangible User Interfaces (TUI) in Augmented Reality (AR). We introduce Ubi Edge, an AR authoring tool that allows end-users to customize edges on daily objects as TUI inputs to control varied digital functions. We develop an integrated AR device and an integrated vision-based detection pipeline that can track 3D edges and detect the touch interaction between fingers and edges. Leveraging the spatial awareness of AR, users can simply select an edge by sliding fingers along it and then make the edge interactive by connecting it to various digital functions. We demonstrate four use cases including multi-function controllers, smart homes, games, and TUI-based tutorials. We also evaluated and proved our system’s usability through a two-session user study, where qualitative and quantitative results are positive.2023FHFengming He et al.Purdue UniversityShape-Changing Interfaces & Soft Robotic MaterialsAR Navigation & Context AwarenessCHI
ARnnotate: An Augmented Reality Interface for Collecting Custom Dataset of 3D Hand-Object Interaction Pose EstimationVision-based 3D pose estimation has substantial potential in hand-object interaction applications and requires user-specified datasets to achieve robust performance. We propose ARnnotate, an Augmented Reality (AR) interface enabling end-users to create custom data using a hand-tracking-capable AR device. Unlike other dataset collection strategies, ARnnotate first guides a user to manipulate a virtual bounding box and records its poses and the user's hand joint positions as the labels. By leveraging the spatial awareness of AR, the user manipulates the corresponding physical object while following the in-situ AR animation of the bounding box and hand model, while ARnnotate captures the user's first-person view as the images of the dataset. A 12-participant user study was conducted, and the results proved the system's usability in terms of the spatial accuracy of the labels, the satisfactory performance of the deep neural networks trained with the data collected by ARnnotate, and the users' subjective feedback.2022XQXun Qian et al.Hand Gesture RecognitionEye Tracking & Gaze InteractionHuman Pose & Activity RecognitionUIST
MechARspace: An Authoring System Enabling Bidirectional Binding of AR with Toys in Real-timeAugmented Reality (AR), which blends physical and virtual worlds, presents the possibility of enhancing traditional toy design. By leveraging bidirectional virtual-physical interactions between humans and the designed artifact, such AR-enhanced toys can provide more playful and interactive experiences for traditional toys. However, designers are constrained by the complexity and technical difficulties of the current AR content creation processes. We propose MechARspace, an immersive authoring system that supports users to create toy-AR interactions through direct manipulation and visual programming. Based on the elicitation study, we propose a bidirectional interaction model which maps both ways: from the toy inputs to reactions of AR content, and also from the AR content to the toy reactions. This model guides the design of our system which includes a plug-and-play hardware toolkit and an in-situ authoring interface. We present multiple use cases enabled by MechARspace to validate this interaction model. Finally, we evaluate our system with a two-session user study where users first recreated a set of predefined toy-AR interactions and then implemented their own AR-enhanced toy designs.2022ZZZhengzhe Zhu et al.Mixed Reality WorkspacesUIST
ScalAR: Authoring Semantically Adaptive Augmented Reality Experiences in Virtual RealityAugmented Reality (AR) experiences tightly associate virtual contents with environmental entities. However, the dissimilarity of different environments limits the adaptive AR content behaviors under large-scale deployment. We propose ScalAR, an integrated workflow enabling designers to author semantically adaptive AR experiences in Virtual Reality (VR). First, potential AR consumers collect local scenes with a semantic understanding technique. ScalAR then synthesizes numerous similar scenes. In VR, a designer authors the AR contents' semantic associations and validates the design while being immersed in the provided scenes. We adopt a decision-tree-based algorithm to fit the designer’s demonstrations as a semantic adaptation model to deploy the authored AR experience in a physical scene. We further showcase two application scenarios authored by ScalAR and conduct a two-session user study where the quantitative results prove the accuracy of the AR content rendering and the qualitative results show the usability of ScalAR.2022XQXun Qian et al.Purdue UniversityAR Navigation & Context AwarenessMixed Reality WorkspacesCHI
GesturAR: An Authoring System for Creating Freehand Interactive Augmented Reality ApplicationsThe freehand gesture is an essential input modality for modern Augmented Reality (AR) user experiences. However, developing AR applications with customized hand interactions remains a challenge for end-users. Therefore, we propose GesturAR, an end-to-end authoring tool that supports users to create in-situ freehand AR applications through embodied demonstration and visual programming. During authoring, users can intuitively demonstrate the customized gesture inputs while referring to the spatial and temporal context. Based on the taxonomy of gestures in AR, we proposed a hand interaction model which maps the gesture inputs to the reactions of the AR contents. Thus, users can author comprehensive freehand applications using trigger-action visual programming and instantly experience the results in AR. Further, we demonstrate multiple application scenarios enabled by GesturAR, such as interactive virtual objects, robots, and avatars, room-level interactive AR spaces, embodied AR presentations, etc. Finally, we evaluate the performance and usability of GesturAR through a user study.2021TWTianyi Wang et al.Hand Gesture RecognitionAR Navigation & Context AwarenessUIST
AdapTutAR: an Adaptive Tutoring System for Machine Tasks using Augmented RealityModern manufacturing processes are in a state of flux, as they adapt to increasing demand for flexible and self-configuring production. This poses challenges for training workers to rapidly master new machine operations and processes, i.e. machine tasks. Conventional in-person training is effective but requires time and effort of experts for each worker trained and not scalable. Recorded tutorials, such as video-based or augmented reality (AR), permit more efficient scaling. However, unlike in-person tutoring, existing recorded tutorials lack the ability to adapt to workers' diverse experiences and learning behaviors. We present AdapTutAR, an adaptive task tutoring system that enables experts to record machine task tutorials via embodied demonstration and train learners with different AR tutoring contents adapting to each user's characteristics. The adaptation is achieved by continually monitoring learners' tutorial-following status and adjusting the tutoring content on-the-fly and in-situ. The results of our user study evaluation have demonstrated that our adaptive system is more effective and preferable than the non-adaptive one.2021GHGaoping Huang et al.Purdue UniversityAR Navigation & Context AwarenessIntelligent Tutoring Systems & Learning AnalyticsCHI
CAPturAR: An Augmented Reality Tool for Authoring Human-Involved Context-Aware ApplicationsRecognition of human behavior plays an important role in context-aware applications. However, it is still a challenge for end-users to build personalized applications that accurately recognize their own activities. Therefore, we present CAPturAR, an in-situ programming tool that supports users to rapidly author context-aware applications by referring to their previous activities. We customize an AR head-mounted device with multiple camera systems that allow for non-intrusive capturing of user’s daily activities. During authoring, we reconstruct the captured data in AR with an animated avatar and use virtual icons to represent the surrounding environment. With our visual programming interface, users create human-centered rules for the applications and experience them instantly in AR. We further demonstrate four use cases enabled by CAPturAR. Also, we verify the effectiveness of the AR-HMD and the authoring workflow with a system evaluation using our prototype. Moreover, we conduct a remote user study in an AR simulator to evaluate the usability.2020TWTianyi Wang et al.Human Pose & Activity RecognitionAR Navigation & Context AwarenessMixed Reality WorkspacesUIST
An Exploratory Study of Augmented Reality Presence for Tutoring Machine Tasks[object Object]2020YCYuanzhi Cao et al.Purdue UniversityMixed Reality WorkspacesTeleoperation & TelepresenceCHI
GhostAR: A Time-space Editor for Embodied Authoring of Human-Robot Collaborative Task with Augmented RealityWe present GhostAR, a time-space editor for authoring and acting Human-Robot-Collaborative (HRC) tasks in-situ. Our system adopts an embodied authoring approach in Augmented Reality (AR), for spatially editing the actions and programming the robots through demonstrative role-playing. We propose a novel HRC workflow that externalizes user's authoring as demonstrative and editable AR ghost, allowing for spatially situated visual referencing, realistic animated simulation, and collaborative action guidance. We develop a dynamic time warping (DTW) based collaboration model which takes the real-time captured motion as inputs, maps it to the previously authored human actions, and outputs the corresponding robot actions to achieve adaptive collaboration. We emphasize an in-situ authoring and rapid iterations of joint plans without an offline training process. Further, we demonstrate and evaluate the effectiveness of our workflow through HRC use cases and a three-session user study.2019YCYuanzhi Cao et al.Mixed Reality WorkspacesHuman-Robot Collaboration (HRC)UIST
SynchronizAR: Instant Synchronization for Spontaneous and Spatial Collaborations in Augmented RealityWe present SynchronizAR, an approach to spatially register multiple SLAM devices together without sharing maps or involving external tracking infrastructures. SynchronizAR employs a distance based indirect registration which resolves the transformations between the separate SLAM coordinate systems. We attach an Ultra-Wide Bandwidth~(UWB) based distance measurements module on each of the mobile AR devices which is capable of self-localization with respect to the environment. As users move on independent paths, we collect the positions of the AR devices in their local frames and the corresponding distance measurements. Based on the registration, we support to create a spontaneous collaborative AR environment to spatially coordinate users' interactions. We run both technical evaluation and user studies to investigate the registration accuracy and the usability towards spatial collaborations. Finally, we demonstrate various collaborative AR experience using SynchronizAR.2018KHKe Huo et al.AR Navigation & Context AwarenessMixed Reality WorkspacesUIST