Artificial intelligence is increasingly used in hiring, raising concerns about how applicants perceive these systems. While prior work on algorithmic fairness has emphasized technical bias mitigation, little is known about how avatar identity cues influence applicants' justice attributions in an interview context. We conducted a crowdsourcing study with 215 participants who completed an interview with photorealistic AI avatars varied in phenotypic traits (race and sex), followed by a standardized rejection. Using self-reports, sentiment analysis, and eye tracking, we measured perceptions of trust, fairness, and bias. Results show that racial mismatch heightened perceptions of ethnic bias, while partial match (sharing only one identity) reduced fairness judgments compared to both full and no match. This work extends the Computers-Are-Social-Actors paradigm by demonstrating that avatar appearances shape justice-related evaluations of AI. We contribute to HCI by revealing how identity cues influence fairness attributions and offer actionable insights for designing equitable AI interview systems.

Split learning emerges as a promising paradigm for collaborative distributed model training, akin to federated learning, by partitioning neural networks between clients and a server without raw data exchange. However, sequential split learning suffers from poor scalability, while parallel variants like parallel split learning and split federated learning often incur high server resource overhead due to model duplication and aggregation, and generally exhibit reduced model performance and convergence owing to factors like client drift and lag. To address these limitations, we introduce CycleSL, a novel aggregation-free split learning framework that enhances scalability and performance and can be seamlessly integrated with existing methods. Inspired by alternating block coordinate descent, CycleSL treats server-side training as an independent higher-level machine learning task, resampling client-extracted features (smashed data) to mitigate heterogeneity and drift. It then performs cyclical updates, namely optimizing the server model first, followed by client updates using the updated server for gradient computation. We integrate CycleSL into previous algorithms and benchmark them on five publicly available datasets with non-iid data distribution and partial client attendance. Our empirical findings highlight the effectiveness of CycleSL in enhancing model performance. Our source code is available at https://gitlab.lrz.de/hctl/CycleSL.

Virtual reality (VR) offers promising opportunities for procedural learning, particularly in preserving intangible cultural heritage. Advances in generative artificial intelligence (Gen-AI) further enrich these experiences by enabling adaptive learning pathways. However, evaluating such adaptive systems using traditional temporal metrics remains challenging due to the inherent variability in Gen-AI response times. To address this, our study employs multimodal behavioural metrics, including visual attention, physical exploratory behaviour, and verbal interaction, to assess user engagement in an adaptive VR environment. In a controlled experiment with (n = 54) participants, we compared three levels of adaptivity (high, moderate, and non-adaptive baseline) within a Neapolitan pizza-making VR experience. Results show that moderate adaptivity optimally enhances user engagement, significantly reducing unnecessary exploratory behaviour and increasing focused visual attention on the AI avatar. Our findings suggest that a balanced level of adaptive AI provides the most effective user support, offering practical design recommendations for future adaptive educational technologies.

The latest developments in computer hardware, sensor technologies, and artificial intelligence can make virtual reality (VR) and virtual spaces an important part of human everyday life. Eye tracking offers not only a hands-free way of interaction but also the possibility of a deeper understanding of human visual attention and cognitive processes in VR. Despite these possibilities, eye-tracking data also reveal users’ privacy-sensitive attributes when combined with the information about the presented stimulus. To address all, this survey first covers major works in eye tracking, VR, and privacy areas between 2012 and 2022. While eye tracking in VR part covers the computational eye-tracking pipeline from pupil detection and gaze estimation to offline data analysis, for privacy and security, we focus on eye-based authentication as well as computational methods to preserve the privacy of individuals and their eye-tracking data in VR. Later, we outline three main directions by focusing on privacy. In summary, this survey presents an extensive literature review of the utmost possibilities of eye tracking in VR and their privacy implications.

With recent advances in machine learning and big data, it is now possible to create synthetic images that look real. Face generation is often of particular interest, as faces can be used for various purposes. However, improper use of such content can lead to the dissemination of false information, such as fake news, and thus pose a threat to society. This work studies whether people believe the truthfulness of faces using eye tracking and self-reports, including free-form textual explanations when participants encounter real and computer-generated faces. We used three different datasets for our evaluations, and our experimental results show that while people are relatively better at identifying the truthfulness of real faces and faces generated by earlier machine learning algorithms with different gazing behaviors in viewing and rating phases, they perform less accurately when deciding the truthfulness of synthetic face images that are generated by newer algorithms. Our findings provide important insights for society and policymakers.

Federated learning is a distributed collaborative machine learning paradigm that has gained strong momentum in recent years. In federated learning, a central server periodically coordinates models with clients and aggregates the models trained locally by clients without necessitating access to local data. Despite its potential, the implementation of federated learning continues to encounter several challenges, predominantly the slow convergence that is largely due to data heterogeneity. The slow convergence becomes particularly problematic in cross-device federated learning scenarios where clients may be strongly limited by computing power and storage space, and hence counteracting methods that induce additional computation or memory cost on the client side such as auxiliary objective terms and larger training iterations can be impractical. In this paper, we propose a novel federated aggregation strategy, TurboSVM-FL, that poses no additional computation burden on the client side and can significantly accelerate convergence for federated classification task, especially when clients are "lazy" and train their models solely for few epochs for next global aggregation. TurboSVM-FL extensively utilizes support vector machine to conduct selective aggregation and max-margin spread-out regularization on class embeddings. We evaluate TurboSVM-FL on multiple datasets including FEMNIST, CelebA, and Shakespeare using user-independent validation with non-iid data distribution. Our results show that TurboSVM-FL can significantly outperform existing popular algorithms on convergence rate and reduce communication rounds while delivering better test metrics including accuracy, F1 score, and MCC.

Learners often experience aware and unaware mind wandering during educational tasks, both negatively impacting learning outcomes. Differentiating these types of task-unrelated thoughts is crucial, as they stem from different cognitive processes and warrant tailored support that addresses the specific nature of mind wandering. Automated detection of these episodes could help mitigate their adverse effects, for example, by developing adaptive, attention-aware learning environments. In this study (N = 87), we explored a novel multimodal approach, combining eye tracking, facial videos, and physiological wristbands (i.e., electrodermal activity and heart rate), to predict aware and unaware mind wandering during lecture video watching. In addition, to allow comparison to previous research, we also predicted an integrated mind-wandering category. Mind wandering was assessed using 15 two-stage thought probes to determine task-unrelated thoughts and the participants’ awareness of their mind wandering. Our findings indicate that a multimodal approach outperforms unimodal methods, utilizing the top 100 features from the fused data. Specifically, aware mind wandering was detected at 20% above chance (AUC-PR = 0.396), unaware mind wandering at 14% above chance (AUC-PR = 0.267), and the combined category at 40% above chance (AUC-PR = 0.637). Eye tracking and video features proved more predictive than physiological measures when used as standalone modalities. SHAP analysis, employed to explain the results, highlighted the significance of integrating features from all three modalities for effective detection, particularly emphasizing the role of video-based facial expressions in identifying unaware mind wandering. Going beyond the current state of the art, this study demonstrates the potential of leveraging multimodal data to enhance the precision of aware and unaware mind-wandering detection and differentiation, setting a foundation for advancing educational technologies that respond dynamically to learners’ cognitive states.

Successful learning depends on learners' ability to sustain attention, which is particularly challenging in online education due to limited teacher interaction. A potential indicator for attention is gaze synchrony, demonstrating predictive power for learning achievements in video-based learning in controlled experiments focusing on manipulating attention. This study (N=84) examines the relationship between gaze synchronization and self-reported attention of learners, using experience sampling, during realistic online video learning. Gaze synchrony was assessed through Kullback-Leibler Divergence of gaze density maps and MultiMatch algorithm scanpath comparisons. Results indicated significantly higher gaze synchronization in attentive participants for both measures and self-reported attention significantly predicted post-test scores. In contrast, synchrony measures did not correlate with learning outcomes. While supporting the hypothesis that attentive learners exhibit similar eye movements, the direct use of synchrony as an attention indicator poses challenges, requiring further research on the interplay of attention, gaze synchrony, and video content type.

As eye tracking becomes pervasive with screen-based devices and head-mounted displays, privacy concerns regarding eye-tracking data have escalated. While state-of-the-art approaches for privacy-preserving eye tracking mostly involve differential privacy and empirical data manipulations, previous research has not focused on methods for scanpaths. We introduce a novel privacy-preserving scanpath comparison protocol designed for the widely used Needleman-Wunsch algorithm, a generalized version of the edit distance algorithm. Particularly, by incorporating the Paillier homomorphic encryption scheme, our protocol ensures that no private information is revealed. Furthermore, we introduce a random processing strategy and a multi-layered masking method to obfuscate the values while preserving the original order of encrypted editing operation costs. This minimizes communication overhead, requiring a single communication round for each iteration of the Needleman-Wunsch process. We demonstrate the efficiency and applicability of our protocol on three publicly available datasets with comprehensive computational performance analyses and make our source code publicly accessible.

Currently, VR technology is increasingly being used in applications to enable immersive yet controlled research settings. One such area of research is expertise assessment, where novel technological approaches to collecting process data, specifically eye tracking, in combination with explainable models, can provide insights into assessing and training novices, as well as fostering expertise development. We present a machine learning approach to predict teacher expertise by leveraging data from an off-the-shelf VR device collected in a VirATec study. By fusing eye-tracking and controller-tracking data, teachers’ recognition and handling of disruptive events in the classroom are taken into account or considered. Three classification models were compared, including SVM, Random Forest, and LightGBM, with Random Forest achieving the best ROC-AUC score of 0.768 in predicting teacher expertise. The SHAP approach to model interpretation revealed informative features (e.g., fixations on identified disruptive students) for distinguishing teacher expertise. Our study serves as a pioneering effort in assessing teacher expertise using eye tracking within an interactive virtual setting, paving the way for future research and advancements in the field.

As technology companies develop mass market augmented reality (AR) glasses that are increasingly sensor-laden and affordable, uses of such devices pose potential privacy and security problems. Though prior work has broadly addressed some of these problems, our work specifically addresses the potential data collection of 15 data types by AR glasses and five potential data uses. Via semi-structured interviews, we explored the attitudes and concerns of 21 current AR technology users regarding potential data collection and data use by hypothetical consumer-grade AR glasses. Participants expressed diverse concerns and suggested potential limits to AR data collection and use, evoking privacy concepts and informational norms. We discuss how participants’ attitudes and reservations about data collection and use, like definitions of privacy, are varying and context-dependent, and make recommendations for designers and policy makers, including customizable and multidimensional privacy solutions.

With the increasing use of the Internet, people encounter a variety of news in online media and social media every day. For digital content without fact-checking mechanisms, it is likely that people perceive fake news as real when they do not have extensive knowledge about the news topic. In this paper, we study human eye movements when reading fake news and real news. Our results suggest that people regress more with their eyes when reading fake news, while the time until the first fixation in the text area of interest is not a distinguishing factor between real and fake content. Our results show that although the truthfulness of the content is not known to people in advance, their visual behavior differs when reading such content, indicating a higher level of confusion when reading fake content.

Virtual humans presented in VR learning environments have been suggested in previous research to increase immersion and further positively influence learning outcomes. However, how virtual human animations affect students’ real-time behavior during VR learning has not yet been investigated. This work examines the effects of social animations (i.e., hand raising of virtual peer learners) on students’ cognitive response and visual attention behavior during immersion in a VR classroom based on eye movement analysis. Our results show that animated peers that are designed to enhance immersion and provide companionship and social information elicit different responses in students (i.e., cognitive, visual attention, and visual search responses), as reflected in various eye movement metrics such as pupil diameter, fixations, saccades, and dwell times. Furthermore, our results show that the effects of animations on students differ significantly between conditions (20%, 35%, 65%, and 80% of virtual peer learners raising their hands). Our research provides a methodological foundation for investigating the effects of avatar animations on users, further suggesting that such effects should be considered by developers when implementing animated virtual humans in VR. Our findings have important implications for future works on the design of more effective, immersive, and authentic VR environments.

With rapid developments in consumer-level head-mounted displays and computer graphics, immersive VR has the potential to take online and remote learning closer to real-world settings. However, the effects of such digital transformations on learners, particularly for VR, have not been evaluated in depth. This work investigates the interaction-related effects of sitting positions of learners, visualization styles of peer-learners and teachers, and hand-raising behaviors of virtual peer-learners on learners in an immersive VR classroom, using eye tracking data. Our results indicate that learners sitting in the back of the virtual classroom may have difficulties extracting information. Additionally, we find indications that learners engage with lectures more efficiently if virtual avatars are visualized with realistic styles. Lastly, we find different eye movement behaviors towards different performance levels of virtual peer-learners, which should be investigated further. Our findings present an important baseline for design decisions for VR classrooms.

Recent developments in computer graphics and hardware technology enable easy access to virtual reality headsets along with integrated eye trackers, leading to mass usage of such devices. The immersive experience provided by virtual reality and the possibility to control environmental factors in virtual setups may soon help to create realistic digital alternatives to conventional classrooms. The importance of such settings has become especially evident during the COVID-19 pandemic, forcing many schools and universities to provide the digital teaching. Researchers foresee that such transformations will continue in the future with virtual worlds becoming an integral part of education. Until now, however, students' behaviors in immersive virtual environments have not been investigated in depth. In this work, we study students' attention by exploiting object-of-interests using eye tracking in different classroom manipulations. More specifically, we varied sitting positions of students, visualization styles of virtual avatars, and hand-raising percentages of peer-learners. Our empirical evidence shows that such manipulations play an important role in students' attention towards virtual peer-learners, instructors, and lecture material. This research may contribute to understanding of how visual attention relates to social dynamics in the virtual classroom, including significant considerations for the design of virtual learning spaces.

Crashes involving pedestrians on urban roads can be fatal. In order to prevent such crashes and provide safer driving experience, adaptive pedestrian warning cues can help to detect risky pedestrians. However, it is difficult to test such systems in the wild, and train drivers using these systems in safety critical situations. This work investigates whether low-cost virtual reality (VR) setups, along with gaze-aware warning cues, could be used for driver training by analyzing driver attention during an unexpected pedestrian crossing on an urban road. Our analyses show significant differences in distances to crossing pedestrians, pupil diameters, and driver accelerator inputs when the warning cues were provided. Overall, there is a strong indication that VR and Head-Mounted-Displays (HMDs) could be used for generating attention increasing driver training packages for safety critical situations.

To this day, a variety of information has been obtained from human eye movements, which holds an imense potential to understand and classify cognitive processes and states - e.g., through scanpath classification. In this work, we explore the task of scanpath classification through a combination of unsupervised feature learning and convolutional neural networks. As an amusement factor, we use an Emoji space representation as feature space. This representation is achieved by training generative adversarial networks (GANs) for unpaired scanpath-to-Emoji translation with a cyclic loss. The resulting Emojis are then used to train a convolutional neural network for stimulus prediciton, showing an accuracy improvement of more than five percentual points compared to the same network trained using solely the scanpath data. As a side effect, we also obtain novel unique Emojis representing each unique scanpath. Our goal is to demonstrate the applicability and potential of unsupervised feature learning to scanpath classification in a humorous and entertaining way.

Eye tracking is handled as key enabling technology to VR and AR for multiple reasons, since it not only can help to massively reduce computational costs through gaze-based optimization of graphics and rendering, but also offers a unique opportunity to design gaze-based personalized interfaces and applications. Additionally, the analysis of eye tracking data allows to assess the cognitive load, intentions and actions of the user. In this work, we propose a person-independent, privacy-preserving and gaze-based cognitive load recognition scheme for drivers under critical situations based on previously collected driving data from a driving experiment in VR including a safety critical situation. Based on carefully annotated ground-truth information, we used pupillary information and performance measures (inputs on accelerator, brake, and steering wheel) to train multiple classifiers with the aim of assessing the cognitive load of the driver. Our results show that incorporating eye tracking data into the VR setup allows to predict the cognitive load of the user at a high accuracy above 80%. Beyond the specific setup, the proposed framework can be used in any adaptive and intelligent VR/AR application.