Publications

Title

Moallem, A., Degen, H., & Ntoa, S. (Eds.). (2026). Artificial Intelligence and Large Language Models: A Scientific Perspective. CRC Press.

https://www.routledge.com/Artificial-Intelligence-and-Large-Language-Models-A-Scientific-Perspective/Moallem-Degen-Ntoa/p/book/9781032775128

Contribution to Bookchapter

Chapter 12 How Do AI and LLMs Change Our Lives? Reflections and Outlook

Helmut Degen, Stavroula Ntoa, Abbas Moallem, Joerg Beringer, Carrie Ching, Lance Chong, Thomas Geis, Pei‑Hsuan Hsieh, Khalid Kattan, Prabhat Kumar, André Frank Krause, Carsten Lanquillon, Rebecca McNulty, Mark Mittrick, Mark Nuppnau, Abraham Moore Odell, Ming Qian, Adrienne Raglin, Robert G. Reynolds, John Richardson, Yao Sun, Lijing Wang, and Carsten Wittenberg

Title

Sheth, P., Schneider, J., & Hassan, T. (2026). Integrating uncertainty-aware stress detection with spoken dialogue-based interaction for human-centered stress management. In Proceedings of the AHFE International Conference on Intelligent Human Systems Integration (IHSI 2026) (Vol. 200, pp. 617-627).

Abstract

Stress is a major factor influencing both mental and physical health, contributing to anxiety, depression, and cardiovascular disease. Traditional stress management tools, such as meditation apps and therapy, often depend on self-reports or fixed schedules, limiting their effectiveness in real-time situations. Physiological signals, such as heart rate, respiration rate, electrodermal activity, and inter-beat intervals, provide objective and non-invasive markers of stress that cannot be consciously manipulated, offering a reliable alternative. However, stress detection using these signals is complicated by inter-individual variability, sensor noise, and overlapping physiological patterns. Therefore, for building reliable and trustworthy stress management systems, it is essential to quantify the uncertainty in the stress predictions and to solicit assistance from the human user to resolve the uncertainty. This results in a human-centered approach for stress management. This work proposes a system that integrates physiological computing and machine learning with dialogue systems. Stress detection is performed using random forest classifiers and a convolutional neural network trained on the publicly available WESAD dataset. Feature extraction from electrodermal activity and inter-beat intervals enables classification of stress versus baseline states. To estimate uncertainty in the predictions, entropy-based measures are applied to the random forest and Monte Carlo dropout is used for the convolutional neural network. Predictions and their confidence scores are fed into a dialogue manager, which tailors stress management interventions accordingly. High-confidence predictions trigger context-appropriate stress recovery strategies, such as guided breathing exercises, while low-confidence cases prompt clarifying dialogue, allowing the user to confirm or correct the system prediction. Experiments demonstrated that a 60-second window provided the best trade-off between temporal resolution and classification accuracy, with the random forest achieving 76% accuracy and the convolutional neural network achieving 75%. Uncertainty quantification helped to identify low-confidence predictions and prevent inappropriate interventions. The system actively integrates people into the stress management cycle by using a dialogue manager to combine tailored, stress-level-based responses with a fallback to user input for low-confidence scenarios. The proposed system has applications in healthcare, especially in personal mental health management, particularly in contexts where immediate and adaptive stress support is required, such as high-pressure jobs or remote healthcare settings.

Title

Ching, C., & Krause, A. F. (2026). Evaluating Tang Poem Comprehension of Foreigners in Holistically Designed Spatial Experience in Virtual Reality. In International Conference on Human-Computer Interaction (pp. 136-147). Cham: Springer Nature Switzerland.

DOI: https://doi.org/10.1007/978-3-032-12764-8_12

Abstract

Virtual Reality (VR) is an intuitive and natural Human-Computer Interaction interface that can facilitate learning, entertainment, simulation and humanities. But it is often resource-demanding to design, develop and evaluate. In previous study, by expressing a Classical-Chinese-written Tang poem “Snow on the River” (SOR) via storytelling in English and holistically designed spatial experience in VR, a VR experience for reading SOR was created, and a design methodology for creating a VR for reading Tang poetry was generalized.

Title

Cheruvalath, S. S., Laporte, M., Bombassei De Bona, F., Hassan, T., & Gjoreski, M. (2025, October). Generating Explanations for Models Predicting Student Exam Performance. In Companion of the 2025 ACM International Joint Conference on Pervasive and Ubiquitous Computing (pp. 1679-1684).

Abstract

Understanding and improving student performance is a central concern in education, and predictive models can provide valuable insights—provided their decisions are transparent and explainable. However, many machine learning (ML) models used for this purpose lack interpretability, limiting their practical utility. In this work, we apply two complementary eXplainable AI (XAI) methods: SHapley Additive exPlanations (SHAP) and the Bayesian Counterfactual Generator (BayCon), to explain the predictions of an ML model trained on multimodal data to forecast student exam outcomes. SHAP is used to identify and visualise the most influential features contributing to each prediction, while BayCon generates actionable counterfactuals. These counterfactuals are then converted into natural language using a large language model (LLM), making them easier to understand. Our approach is designed to support both students and educators by offering clear, personalised insights into the factors affecting academic performance. We present the explanation generation pipeline and discuss its interpretability and potential benefits for educational contexts.

Title

Vered, M., Hassan, T., Ntekouli, M., Bae, S. W., & Gjoreski, M. (2025, October). XAI for U 2025: 2 nd International Workshop on Explainable AI for Ubiquitous, Pervasive and Wearable Computing. In Companion of the 2025 ACM International Joint Conference on Pervasive and Ubiquitous Computing (pp. 1670-1673).

Abstract

The workshop XAI for U aims to address the critical need for transparency in Artificial Intelligence (AI) systems that are increasingly integrated into our daily lives through mobile systems, wearables, and smart environments. Despite rapid advances in AI and machine learning, many of these systems remain opaque, making it difficult for users, designers, developers, and stakeholders to verify their reliability, fairness, and correctness. This lack of transparency can hinder trust, informed decision-making, and broader adoption. This workshop focuses on the pressing need to enable Explainable AI (XAI) tools tailored for ubiquitous, pervasive, and wearable computing, and highlights the unique challenges associated with this domain, such as generating explanations for time-series and multimodal data, interpreting interconnected machine learning components, and delivering user-centered explanations. It aims to foster interdisciplinary collaboration among researchers across related domains, share recent advancements, address open challenges, and propose future research directions to improve the development and applicability of XAI in ubiquitous, pervasive, and wearable computing. Ultimately, the workshop seeks to enhance user trust, understanding, interaction, and adoption, ensuring that AI-driven solutions are not only more explainable but also better aligned with ethical standards and user expectations.

Title

Yavuz, S., Grashof, R., Nitsche, T., Breil, B., and Naroska, E. (2025). Development of a pro-adaptive wrist-worn wearable device for Parkinson disease symptoms: Concept and initial approach, Abstracts of the 2025 Joint Annual Conference of the Austrian (ÖGBMT), German (VDE DGBMT) and Swiss (SSBE) Societies for Biomedical Engineering Biomedical Engineering / Biomedizinische Technik, vol. 70, no. s1, 2025, pp. 1-374.

DOI: https://doi.org/10.1515/bmt-2025-1001

Title

Schneider, J. (2025). Towards Intelligent Adaption in Cognitive Assistance Systems through Physiological Computing. In Proceedings of the 27th International Conference on Multimodal Interaction (ICMI ’25) (pp. 749-753).

Abstract

With the growing prevalence of cognitive impairments around the globe and in Europe, an increasing number of people are likely to experience cognitive decline during their working years. Supporting these individuals to remain in employment is imperative, both to promote personal well-being and to enable organizations to retain experienced and skilled workers. This research proposes the design of a physiologically adaptive cognitive assistance system to support individuals with mild cognitive impairment in sheltered workshops. This work adopts a design science research approach, combining laboratory and field experiments to achieve user-centred design. Expected outcomes include a modular framework for physiologically adaptive cognitive assistive systems, a multimodal machine learning pipeline for detecting psychological states from physiological signals and design principles to inform future research and development. By demonstrating the potential of such systems within work settings, this research aims to advance the social inclusion of individuals with mild cognitive impairment in the labour market.

Title

Grashof, R., Yavuz, S., Gräbel, J. and Breil, B. (2025). Interviews zur nutzerorientierten Entwicklung pro-adaptiver kognitiver Assistenzsysteme: Bedürfnisse von Alzheimer-Patienten. 70. Jahrestagung der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie e.V. (GMDS)

Abstract: grashof2025_gmds_abstract.pdf

Poster: grashof2025_gmds_poster.pdf

Title

Grashof R, Lipprandt M, Breil B. Cognitive assistive technologies for degenerative diseases and related evaluation methods: A scoping review. GMS Med Inform Biom Epidemiol. 2025;21:Doc09.

DOI: https://doi.org/10.3205/mibe000281

Abstract

Assistive technologies (ATs) are crucial for people with degenerative diseases that affect cognitive functions. To date, no comprehensive review has systematically examined these technologies and their evaluation methods. To outline the current state of research, we conducted a scoping review on cognitive ATs that provide direct assistance. From an initial pool of 107 review articles identified in Web of Science and other sources over the last five years we selected ten for further analysis. To enhance clarity and interpretability, the findings were organized into thematic categories, distinguishing types of assistive technologies as well as evaluation approaches used across studies.

Title

Pfeifer, J., Behnisch, C., Kannen, K., Büscher, S., Wild-Wall, N., Krause, F. A., & Mai, J. (2025, August). Exploring ECG and eye-tracking biomarkers for emotion recognition: A pilot study. (Poster presentation). 39th Annual Conference of the European Health Psychology Society (EHPS), Groningen, Netherlands.

Poster: pfeifer2026_poster_ehps.pdf