arXiv subjects

Efe Bozkir

Publications and source records attributed to Efe Bozkir.

At least 19 recordsLinked to original sources

arXiv2026

Skin-Deep Bias: How Avatar Appearances Shape Perceptions of AI Hiring

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 justicerelated 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.

cs.HC↗

arXiv2026

Safeguarding Privacy: Privacy-Preserving Detection of Mind Wandering and Disengagement Using Federated Learning in Online Education

Since the COVID-19 pandemic, online courses have expanded access to education, yet the absence of direct instructor support challenges learners' ability to self-regulate attention and engagement. Mind wandering and disengagement can be detrimental to learning outcomes, making their automated detection via video-based indicators a promising approach for real-time learner support. However, machine learning-based approaches often require sharing sensitive data, raising privacy concerns. Federated learning offers a privacy-preserving alternative by enabling decentralized model training while also distributing computational load. We propose a framework exploiting cross-device federated learning to address different manifestations of behavioral and cognitive disengagement during remote learning, specifically behavioral disengagement, mind wandering, and boredom. We fit video-based cognitive disengagement detection models using facial expressions and gaze features. By adopting federated learning, we safeguard users' data privacy through privacy-by-design and introduce a novel solution with the potential for real-time learner support. We further address challenges posed by eyeglasses by incorporating related features, enhancing overall model performance. To validate the performance of our approach, we conduct extensive experiments on five datasets and benchmark multiple federated learning algorithms. Our results show great promise for privacy-preserving educational technologies promoting learner engagement.

cs.LG↗

arXiv2026

Exploring Organizational Readiness and Ecosystem Coordination for Industrial XR

Extended Reality (XR) offers transformative potential for industrial support, training, and maintenance; yet, widespread adoption lags despite demonstrated occupational value and hardware maturity. Organizations successfully implement XR in isolated pilots, yet struggle to scale these into sustained operational deployment, a phenomenon we characterize as the ``Pilot Trap.'' This study examines this phenomenon through a qualitative ecosystem analysis of 17 expert interviews across technology providers, solution integrators, and industrial adopters. We identify a ``Great Inversion'' in adoption barriers: critical constraints have shifted from technological maturity to organizational readiness (e.g., change management, key performance indicator alignment, and political resistance). While hardware ergonomics and usability remain relevant, our findings indicate that systemic misalignments between stakeholder incentives are the primary cause of friction preventing enterprise integration. We conclude that successful industrial XR adoption requires a shift from technology-centric piloting to a problem-first, organizational transformation approach, necessitating explicit ecosystem-level coordination.

cs.HC↗

arXiv2025

Exploring User Acceptance and Concerns toward LLM-powered Conversational Agents in Immersive Extended Reality

The rapid development of generative artificial intelligence (AI) and large language models (LLMs), and the availability of services that make them accessible, have led the general public to begin incorporating them into everyday life. The extended reality (XR) community has also sought to integrate LLMs, particularly in the form of conversational agents, to enhance user experience and task efficiency. When interacting with such conversational agents, users may easily disclose sensitive information due to the naturalistic flow of the conversations, and combining such conversational data with fine-grained sensor data may lead to novel privacy issues. To address these issues, a user-centric understanding of technology acceptance and concerns is essential. Therefore, to this end, we conducted a large-scale crowdsourcing study with 1036 participants, examining user decision-making processes regarding LLM-powered conversational agents in XR, across factors of XR setting type, speech interaction type, and data processing location. We found that while users generally accept these technologies, they express concerns related to security, privacy, social implications, and trust. Our results suggest that familiarity plays a crucial role, as daily generative AI use is associated with greater acceptance. In contrast, previous ownership of XR devices is linked to less acceptance, possibly due to existing familiarity with the settings. We also found that men report higher acceptance with fewer concerns than women. Regarding data type sensitivity, location data elicited the most significant concern, while body temperature and virtual object states were considered least sensitive. Overall, our study highlights the importance of practitioners effectively communicating their measures to users, who may remain distrustful. We conclude with implications and recommendations for LLM-powered XR.

cs.HC↗

arXiv2025

CycleSL: Server-Client Cyclical Update Driven Scalable Split Learning

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.

cs.LG↗

arXiv2025

The Impact of Device Type, Data Practices, and Use Case Scenarios on Privacy Concerns about Eye-tracked Augmented Reality in the United States and Germany

Augmented reality technology will likely be prevalent with more affordable head-mounted displays. Integrating novel interaction modalities, such as eye trackers into head-mounted displays could lead to collecting vast amounts of biometric data, which may allow inference of sensitive user attributes like health status or sexual preference, posing privacy issues. While previous works broadly examined privacy concerns about augmented reality, ours is the first to extensively explore privacy concerns on behavioral data, particularly eye tracking in augmented reality. We crowdsourced four survey studies in the United States (n1 = 48, n2 = 525) and Germany (n3 = 48, n4 = 525) to understand the impact of user attributes, augmented reality devices, use cases, data practices, and country on privacy concerns. Our findings indicate that participants are generally concerned about privacy when they know what inferences can be made based on the collected data. Despite the more prominent use of smartphones in daily life than augmented reality glasses, we found no indications of differing privacy concerns depending on the device type. In addition, our participants are more comfortable when a particular use case benefits them and less comfortable when other humans can consume their data. Furthermore, participants in the United States are less concerned about their privacy than those in Germany. Based on our findings, we provide several recommendations to practitioners and policymakers for privacy-aware augmented reality.

cs.HC↗

arXiv2025

Examining the Role of LLM-Driven Interactions on Attention and Cognitive Engagement in Virtual Classrooms

Transforming educational technologies through the integration of large language models (LLMs) and virtual reality (VR) offers the potential for immersive and interactive learning experiences. However, the effects of LLMs on user engagement and attention in educational environments remain open questions. In this study, we utilized a fully LLM-driven virtual learning environment, where peers and teachers were LLM-driven, to examine how students behaved in such settings. Specifically, we investigate how peer question-asking behaviors influenced student engagement, attention, cognitive load, and learning outcomes and found that, in conditions where LLM-driven peer learners asked questions, students exhibited more targeted visual scanpaths, with their attention directed toward the learning content, particularly in complex subjects. Our results suggest that peer questions did not introduce extraneous cognitive load directly, as the cognitive load is strongly correlated with increased attention to the learning material. Considering these findings, we provide design recommendations for optimizing VR learning spaces.

cs.HC↗

arXiv2025

Multimodal Assessment of Classroom Discourse Quality: A Text-Centered Attention-Based Multi-Task Learning Approach

Classroom discourse is an essential vehicle through which teaching and learning take place. Assessing different characteristics of discursive practices and linking them to student learning achievement enhances the understanding of teaching quality. Traditional assessments rely on manual coding of classroom observation protocols, which is time-consuming and costly. Despite many studies utilizing AI techniques to analyze classroom discourse at the utterance level, investigations into the evaluation of discursive practices throughout an entire lesson segment remain limited. To address this gap, our study proposes a novel text-centered multimodal fusion architecture to assess the quality of three discourse components grounded in the Global Teaching InSights (GTI) observation protocol: Nature of Discourse, Questioning, and Explanations. First, we employ attention mechanisms to capture inter- and intra-modal interactions from transcript, audio, and video streams. Second, a multi-task learning approach is adopted to jointly predict the quality scores of the three components. Third, we formulate the task as an ordinal classification problem to account for rating level order. The effectiveness of these designed elements is demonstrated through an ablation study on the GTI Germany dataset containing 92 videotaped math lessons. Our results highlight the dominant role of text modality in approaching this task. Integrating acoustic features enhances the model's consistency with human ratings, achieving an overall Quadratic Weighted Kappa score of 0.384, comparable to human inter-rater reliability (0.326). Our study lays the groundwork for the future development of automated discourse quality assessment to support teacher professional development through timely feedback on multidimensional discourse practices.

cs.CY↗

arXiv2025

Automated Visual Attention Detection using Mobile Eye Tracking in Behavioral Classroom Studies

Teachers' visual attention and its distribution across the students in classrooms can constitute important implications for student engagement, achievement, and professional teacher training. Despite that, inferring the information about where and which student teachers focus on is not trivial. Mobile eye tracking can provide vital help to solve this issue; however, the use of mobile eye tracking alone requires a significant amount of manual annotations. To address this limitation, we present an automated processing pipeline concept that requires minimal manually annotated data to recognize which student the teachers focus on. To this end, we utilize state-of-the-art face detection models and face recognition feature embeddings to train face recognition models with transfer learning in the classroom context and combine these models with the teachers' gaze from mobile eye trackers. We evaluated our approach with data collected from four different classrooms, and our results show that while it is possible to estimate the visually focused students with reasonable performance in all of our classroom setups, U-shaped and small classrooms led to the best results with accuracies of approximately 0.7 and 0.9, respectively. While we did not evaluate our method for teacher-student interactions and focused on the validity of the technical approach, as our methodology does not require a vast amount of manually annotated data and offers a non-intrusive way of handling teachers' visual attention, it could help improve instructional strategies, enhance classroom management, and provide feedback for professional teacher development.

cs.CV↗

arXiv2025

Exploring Context-aware and LLM-driven Locomotion for Immersive Virtual Reality

Locomotion plays a crucial role in shaping the user experience within virtual reality environments. In particular, hands-free locomotion offers a valuable alternative by supporting accessibility and freeing users from reliance on handheld controllers. To this end, traditional speech-based methods often depend on rigid command sets, limiting the naturalness and flexibility of interaction. In this study, we propose a novel locomotion technique powered by large language models (LLMs), which allows users to navigate virtual environments using natural language with contextual awareness. We evaluate three locomotion methods: controller-based teleportation, voice-based steering, and our language model-driven approach. Our evaluation combines eye-tracking data analysis, including exploratory explainable machine learning analysis with SHAP, and standardized questionnaires (SUS, IPQ, CSQ-VR, NASA-TLX) to examine user experience through both objective gaze-based measures and subjective self-reports of usability, presence, cybersickness, and cognitive load. Our findings show no statistically significant differences in usability, presence, or cybersickness between LLM-driven locomotion and established methods such as teleportation, suggesting its potential as a viable, natural language-based, hands-free alternative. In addition, eye-tracking analysis revealed patterns suggesting tendency toward increased user attention and engagement in the LLM-driven condition. Complementary to these findings, exploratory SHAP analysis revealed that fixation, saccade, and pupil-related features vary across techniques, indicating distinct patterns of visual attention and cognitive processing. Overall, we state that our method can facilitate hands-free locomotion in virtual spaces, especially in supporting accessibility.

cs.HC↗

arXiv2025

Trade-offs in Privacy-Preserving Eye Tracking through Iris Obfuscation: A Benchmarking Study

Recent developments in hardware, computer graphics, and AI may soon enable AR/VR head-mounted displays (HMDs) to become everyday devices like smartphones and tablets. Eye trackers within HMDs provide a special opportunity for such setups as it is possible to facilitate gaze-based research and interaction. However, estimating users' gaze information often requires raw eye images and videos that contain iris textures, which are considered a gold standard biometric for user authentication, and this raises privacy concerns. Previous research in the eye-tracking community focused on obfuscating iris textures while keeping utility tasks such as gaze estimation accurate. Despite these attempts, there is no comprehensive benchmark that evaluates state-of-the-art approaches. Considering all, in this paper, we benchmark blurring, noising, downsampling, rubber sheet model, and iris style transfer to obfuscate user identity, and compare their impact on image quality, privacy, utility, and risk of imposter attack on two datasets. We use eye segmentation and gaze estimation as utility tasks, and reduction in iris recognition accuracy as a measure of privacy protection, and false acceptance rate to estimate risk of attack. Our experiments show that canonical image processing methods like blurring and noising cause a marginal impact on deep learning-based tasks. While downsampling, rubber sheet model, and iris style transfer are effective in hiding user identifiers, iris style transfer, with higher computation cost, outperforms others in both utility tasks, and is more resilient against spoof attacks. Our analyses indicate that there is no universal optimal approach to balance privacy, utility, and computation burden. Therefore, we recommend practitioners consider the strengths and weaknesses of each approach, and possible combinations of those to reach an optimal privacy-utility trade-off.

cs.CV↗

arXiv2025

Iris Style Transfer: Enhancing Iris Recognition with Style Features and Privacy Preservation through Neural Style Transfer

Iris texture is widely regarded as a gold standard biometric modality for authentication and identification. The demand for robust iris recognition methods, coupled with growing security and privacy concerns regarding iris attacks, has escalated recently. Inspired by neural style transfer, an advanced technique that leverages neural networks to separate content and style features, we hypothesize that iris texture's style features provide a reliable foundation for recognition and are more resilient to variations like rotation and perspective shifts than traditional approaches. Our experimental results support this hypothesis, showing a significantly higher classification accuracy compared to conventional features. Further, we propose using neural style transfer to obfuscate the identifiable iris style features, ensuring the protection of sensitive biometric information while maintaining the utility of eye images for tasks like eye segmentation and gaze estimation. This work opens new avenues for iris-oriented, secure, and privacy-aware biometric systems.

cs.CV↗

arXiv2025

CUIfy the XR: An Open-Source Package to Embed LLM-powered Conversational Agents in XR

Recent developments in computer graphics, machine learning, and sensor technologies enable numerous opportunities for extended reality (XR) setups for everyday life, from skills training to entertainment. With large corporations offering affordable consumer-grade head-mounted displays (HMDs), XR will likely become pervasive, and HMDs will develop as personal devices like smartphones and tablets. However, having intelligent spaces and naturalistic interactions in XR is as important as technological advances so that users grow their engagement in virtual and augmented spaces. To this end, large language model (LLM)--powered non-player characters (NPCs) with speech-to-text (STT) and text-to-speech (TTS) models bring significant advantages over conventional or pre-scripted NPCs for facilitating more natural conversational user interfaces (CUIs) in XR. This paper provides the community with an open-source, customizable, extendable, and privacy-aware Unity package, CUIfy, that facilitates speech-based NPC-user interaction with widely used LLMs, STT, and TTS models. Our package also supports multiple LLM-powered NPCs per environment and minimizes latency between different computational models through streaming to achieve usable interactions between users and NPCs. We publish our source code in the following repository: https://gitlab.lrz.de/hctl/cuify

cs.HC↗

arXiv2025

From Passive Watching to Active Learning: Empowering Proactive Participation in Digital Classrooms with AI Video Assistant

In online education, innovative tools are crucial for enhancing learning outcomes. SAM (Study with AI Mentor) is an advanced platform that integrates educational videos with a context-aware chat interface powered by large language models. SAM encourages students to ask questions and explore unclear concepts in real time, offering personalized, context-specific assistance, including explanations of formulas, slides, and images. We evaluated SAM in two studies: one with 25 university students and another with 80 crowdsourced participants, using pre- and post-knowledge tests to compare a group using SAM and a control group. The results demonstrated that SAM users achieved greater knowledge gains specifically for younger learners and individuals in flexible working environments, such as students, supported by a 97.6% accuracy rate in the chatbot's responses. Participants also provided positive feedback on SAM's usability and effectiveness. SAM's proactive approach to learning not only enhances learning outcomes but also empowers students to take full ownership of their educational experience, representing a promising future direction for online learning tools.

cs.AI↗

arXiv2024

Adaptive Gen-AI Guidance in Virtual Reality: A Multimodal Exploration of Engagement in Neapolitan Pizza-Making

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 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.

cs.HC↗

arXiv2024

User Intent Recognition and Satisfaction with Large Language Models: A User Study with ChatGPT

The rapid evolution of LLMs represents an impactful paradigm shift in digital interaction and content engagement. While they encode vast amounts of human-generated knowledge and excel in processing diverse data types, they often face the challenge of accurately responding to specific user intents, leading to user dissatisfaction. Based on a fine-grained intent taxonomy and intent-based prompt reformulations, we analyze the quality of intent recognition and user satisfaction with answers from intent-based prompt reformulations of GPT-3.5 Turbo and GPT-4 Turbo models. Our study highlights the importance of human-AI interaction and underscores the need for interdisciplinary approaches to improve conversational AI systems. We show that GPT-4 outperforms GPT-3.5 in recognizing common intents but is often outperformed by GPT-3.5 in recognizing less frequent intents. Moreover, whenever the user intent is correctly recognized, while users are more satisfied with the intent-based reformulations of GPT-4 compared to GPT-3.5, they tend to be more satisfied with the models' answers to their original prompts compared to the reformulated ones. The collected data from our study has been made publicly available on GitHub (https://github.com/ConcealedIDentity/UserIntentStudy) for further research.

cs.HC↗

arXiv2024

Wrapped in Anansi's Web: Unweaving the Impacts of Generative-AI Personalization and VR Immersion in Oral Storytelling

Oral traditions, vital to cultural identity, are losing relevance among youth due to the dominance of modern media. This study addresses the revitalization of these traditions by reconnecting young people with folklore. We introduce Anansi the Spider VR, a novel virtual space that combines first-person virtual reality (VR) with generative artificial intelligence (Gen-AI)-driven narrative personalization. This space immerses users in the Anansi Spider story, empowering them to influence the narrative as they envision themselves as the `protagonists,' thereby enhancing personal reflection. In a 2 by 2 between-subjects study with 48 participants, we employed a mixed-method approach to measure user engagement and changes in interest, complemented by semi-structured interviews providing qualitative insights into personalization and immersion. Our results indicate that personalization in VR significantly boosts engagement and cultural learning interest. We recommend that future studies using VR and Gen-AI to revitalize oral storytelling prioritize respecting cultural integrity and honoring original storytellers and communities.

cs.HC↗

arXiv2024

Evaluating Usability and Engagement of Large Language Models in Virtual Reality for Traditional Scottish Curling

This paper explores the innovative application of Large Language Models (LLMs) in Virtual Reality (VR) environments to promote heritage education, focusing on traditional Scottish curling presented in the game ``Scottish Bonspiel VR''. Our study compares the effectiveness of LLM-based chatbots with pre-defined scripted chatbots, evaluating key criteria such as usability, user engagement, and learning outcomes. The results show that LLM-based chatbots significantly improve interactivity and engagement, creating a more dynamic and immersive learning environment. This integration helps document and preserve cultural heritage and enhances dissemination processes, which are crucial for safeguarding intangible cultural heritage (ICH) amid environmental changes. Furthermore, the study highlights the potential of novel technologies in education to provide immersive experiences that foster a deeper appreciation of cultural heritage. These findings support the wider application of LLMs and VR in cultural education to address global challenges and promote sustainable practices to preserve and enhance cultural heritage.

cs.HC↗