From Gaze to Data: Privacy and Societal Challenges of Using Eye-tracking Data to Inform GenAI Models

Abdrabou, Y., Ozdel, S., Maquiling, V., Bozkir, E., & Kasneci, E. (2025). From gaze to data: Privacy and societal challenges of using eye-tracking data to inform GenAI models. In Proceedings of the 2025 Symposium on Eye Tracking Research and Applications (ETRA’25).  Tokyo, Japan. https://doi.org/10.1145/3715669.3726788

PerVRML: ChatGPT-Driven Personalized VR Environments for Machine Learning Education

Gao, H., Xie, Y., & Kasneci, E. (2025). PerVRML: ChatGPT-Driven Personalized VR Environments for Machine Learning Education. International Journal of Human–Computer Interaction, 1–15. https://doi.org/10.1080/10447318.2025.2504188 

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

K. B. Buldu et al., 2025 IEEE International Conference on Artificial Intelligence and eXtended and Virtual Reality (AIxVR), Lisbon, Portugal, 2025, pp. 192-197, doi: 10.1109/AIxVR63409.2025.00037.

Taking the next step with generative artificial intelligence: The transformative role of multimodal large language models in science education

Bewersdorff, A., Hartmann, C., Hornberger, M., Seßler, K., Bannert, M., Kasneci, E., Kasneci, G., Zhai, X., & Nerdel, C. (2024). Taking the next step with generative artificial intelligence: The transformative role of multimodal large language models in science education. Learning and Individual Differences, 109, 102601. https://doi.org/10.1016/j.lindif.2024.102601

Towards Adaptive Feedback with AI: Comparing the Feedback Quality of LLMs and Teachers on Experimentation Protocols

Seßler, K., Bewersdorff, A., Nerdel, C., & Kasneci, E. (2025). Towards adaptive feedback with AI: Comparing the feedback quality of LLMs and teachers on experimentation protocols. arXiv preprint arXiv:2502.12842. https://doi.org/10.48550/arXiv.2502.12842

On opportunities and challenges of large multimodal foundation models in education

Küchemann, S., Avila, K. E., Dinc, Y., Hortmann, C., Revenga, N., Ruf, V., Stausberg, N., Steinert, S., F. Fischer, M. Fischer, Kasneci, E., Kasneci, G., Kuhr, T., Kutyniok, G., Malone, S., Sailer, M., Schmidt, A., Stadler, M., Weller, J., & Kuhn, J. (2025). On opportunities and challenges of large multimodal foundation models in education. npj Science of Learning, 10, Article 11. https://doi.org/10.1038/s41539-024-00265-7

Beyond the Script: Testing LLMs for Authentic Patient Communication Styles in Healthcare

Bodonhelyi, A., Stegemann-Philipps, C., Sonanini, A., Herschbach, L., Szep, M., Herrmann-Werner, A., Festl-Wietek, T., Kasneci, E., & Holderried, F. (2025). Modeling challenging patient interactions: LLMs for medical communication training. arXiv preprint arXiv:2503.22250. https://doi.org/10.48550/arXiv.2503.22250

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

Wang, M., Bozkir, E., & Kasneci, E. (2025). Iris style transfer: Enhancing iris recognition with style features and privacy preservation through neural style transfer. Proceedings of the 2025 ACM Symposium on Eye Tracking Research & Applications (ETRA), Article 21. https://doi.org/10.1145/3729413

Modeling Challenging Patient Interactions: LLMs for Medical Communication Training

Bodonhelyi, A., Stegemann-Philipps, C., Sonanini, A., Herschbach, L., Szep, M., Herrmann-Werner, A., Festl-Wietek, T., Kasneci, E., & Holderried, F. (2025). Modeling challenging patient interactions: LLMs for medical communication training. arXiv preprint arXiv:2503.22250. https://doi.org/10.48550/arXiv.2503.22250

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

Wang, M., Bozkir, E., & Kasneci, E. (2025). Trade-offs in privacy-preserving eye tracking through iris obfuscation: A benchmarking study. arXiv preprint arXiv:2504.10267. https://doi.org/10.48550/arXiv.2504.10267 
(25th International Conference on Digital Signal Processing (DSP 2025))

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

Özdel, S., Buldu, K. B., Kasneci, E., & Bozkir, E. (2025). Exploring context-aware and LLM-driven locomotion for immersive virtual reality. arXiv preprint arXiv:2504.17331. https://doi.org/10.48550/arXiv.2504.17331

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

Bozkir, E., Kosel, C., Seidel, T., & Kasneci, E. (2025). Automated visual attention detection using mobile eye tracking in behavioral classroom studies. arXiv preprint arXiv:2505.07552. https://doi.org/10.48550/arXiv.2505.07552

User Identification with LFI-Based Eye Movement Data Using Time and Frequency Domain Features

Ozdel, S., Meyer, J., Abdrabou, Y., & Kasneci, E. (2025). User identification with LFI-based eye movement data using time and frequency domain features. arXiv preprint arXiv:2505.07326. https://doi.org/10.48550/arXiv.2505.07326

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

Ozdel, S., Sarpkaya, C., Bozkir, E., Gao, H., & Kasneci, E. (2025). Examining the role of LLM-driven interactions on attention and cognitive engagement in virtual classrooms. arXiv preprint arXiv:2505.07377. https://doi.org/10.48550/arXiv.2505.07377

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

Hou, R., Bühler, B., Fütterer, T., Bozkir, E., Gerjets, P., Trautwein, U., & Kasneci, E. (2025). Multimodal assessment of classroom discourse quality: A text-centered attention-based multi-task learning approach. arXiv preprint arXiv:2505.07902. https://doi.org/10.48550/arXiv.2505.07902

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

Bodonhelyi, A., Thaqi, E., Özdel, S., Bozkir, E., & Kasneci, E. (2025). In Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems (Article 16, pp. 1–21). Association for Computing Machinery. https://doi.org/10.1145/3706598.3713513

LEyes: A lightweight framework for deep learning-based eye tracking using synthetic eye images

Byrne, S. A., Maquiling, V., Nyström, M., & others. (2025). LEyes: A lightweight framework for deep learning-based eye tracking using synthetic eye images. Behavior Research Methods, 57(1), 129. https://doi.org/10.3758/s13428-025-02645-y

Europe’s AI Imperative - A Pragmatic Blueprint for Global Tech Leadership

Kasneci, G., Gasser, U., Hofmann, T. F., Kramer, G., Müller, G., Peus, C., Schönenberger, H., & Kasneci, E. (2025). Europe’s AI Imperative – A Pragmatic Blueprint for Global Tech Leadership. https://doi.org/10.31235/osf.io/8uyrc_v1