@unpublished{GaubeJussupowKokjeetal., author = {Gaube, Susanne and Jussupow, Ekaterina and Kokje, Eesha and Khan, Jowaria and Bondi-Kelly, Elizabeth and Schicho, Andreas and Kitamura, Felipe Campos and Koch, Timo Kevin and Ezer, Timur and Mottok, J{\"u}rgen and Lermer, Eva and Ghassemi, Marzyeh and Colak, Errol}, title = {Examining Reliance Patterns on AI Advice in Medical Imaging: a Mixed-Methods Randomized Crossover Experiment}, doi = {10.31219/osf.io/4wv8j_v3}, pages = {20}, abstract = {Background: Artificial intelligence (AI) holds significant potential to support diagnostic decision-making; however, evidence regarding its clinical utility remains mixed. Often, the collaboration between clinicians and AI systems does not surpass the individual performance of unaided humans or standalone AI. Yet, currently, the mechanisms that limit human-AI synergy are poorly understood. This study examined the impact of AI advice on diagnostic decision-making among experts and novices, focusing on reliance patterns. Methods: We used a mixed-methods crossover experimental design with a think-aloud and an eye-tracking study arm. Participants were 50 task experts (radiologists) and 75 novices (non-radiologist physicians and medical trainees) from 10 countries. They reviewed 50 head CT scans and every case was examined in three time-separate sessions in randomized order. In each session, participants were exposed to different experimental conditions: (a) control, no AI prediction; (b) basic advice, AI prediction without annotations; and (c) XAI advice, AI prediction with scan annotations. For each case, participants had to determine if the patients had an intracranial hemorrhage (ICH). The main outcomes were diagnostic performance, confidence in the diagnosis, case reading time, and AI advice usefulness ratings. Findings: Both overreliance on incorrect advice and underreliance on correct advice occurred. Underreliance was associated with high uncertainty and, in absolute terms, had a more detrimental impact on diagnostic performance than overreliance. Correct XAI advice reduced underreliance, improved performance (OR=1·84, p<0·0001), and confidence (b=0·15, p<0·0001), particularly when reviewing more difficult cases with ICH. Surprisingly, correct XAI did not reduce reading time (b=1·81, p=0·0713). XAI was perceived as more useful than basic AI advice (b=0·12, p=0·0029), especially among novices. Interpretation: The occurrence of both under- and overreliance highlights the need for efficient counterstrategies beyond classic XAI methods to foster appropriate reliance and synergy.}, language = {en} } @inproceedings{PozzobonWeissMottoketal., author = {Pozzobon, Enrico and Weiß, Nils and Mottok, J{\"u}rgen and Matoušek, V{\´a}clav}, title = {Fuzzy fault injection attacks against secure automotive bootloaders}, series = {21th escar Europe : The World's Leading Automotive Cyber Security Conference (Hamburg, 15. - 16.11.2023)}, booktitle = {21th escar Europe : The World's Leading Automotive Cyber Security Conference (Hamburg, 15. - 16.11.2023)}, publisher = {Ruhr-Universit{\"a}t Bochum}, doi = {10.13154/294-10381}, url = {http://nbn-resolving.de/urn:nbn:de:hbz:294-103817}, pages = {20}, abstract = {Secure embedded bootloaders are the trust anchors for modern vehicles' software. The secure software update process of ECUs is well-defined across the entire automotive industry. Every OEM has his own implementation, but follows the general software update process. This paper demonstrates code execution attacks by combining software and hardware weaknesses in secure automotive bootloaders. The attack can be performed entirely automated, no static code analysisis required. Random fault injection parameters were sufficient to obtain code execution in a reasonable time. All experiments were conducted with electromagnetic fault injection and without any hardware modifications of the targets. We successfully performed our attack on two entirely different gateway Electronic Control Units (ECUs) used in current vehicles (at the time of this research) from Volkswagen and BMW. As a result of this attack, consisting of a combination of a hardware and asoftware attack, the general secure software update process used in the automotive industry needs to be revised.}, language = {en} } @inproceedings{HammerMottokKrauseetal., author = {Hammer, Pascal and Mottok, J{\"u}rgen and Krause, Veronika and Probst, Tobias}, title = {Approach for High-Performance Random Number Generators for Critical Systems}, series = {Proceeding of the 12th European Congress on Embedded Real Time Software and Systems (ERTS2024) , Toulouse, 11-12 June 2024}, booktitle = {Proceeding of the 12th European Congress on Embedded Real Time Software and Systems (ERTS2024) , Toulouse, 11-12 June 2024}, doi = {10.5281/zenodo.14848832}, pages = {9}, abstract = {In times of digitalization, the encryption and signing of sensitive data is becoming increasingly important. These cryptographic processes require large quantities of high-quality random numbers. Which is why a high-performance random number generator (RNG) is to be developed. For this purpose, existing concepts of RNGs and application standards are first analyzed. The proposed approach is to design a physical true random number generator (PTRNG) with a high output of random numbers. Based on this, the development begins with the analog part of the RNG, the noise signal source and a suitable amplifier for the analog noise signal. Therefore, a special noise diode from Noisecom and an amplifier from NXP were chosen and analyzed in different measurements. From the results of the measurements, it can be concluded that both components are suitable for use in the RNG.}, language = {en} } @misc{GrabingerHauserMottok, author = {Grabinger, Lisa and Hauser, Florian and Mottok, J{\"u}rgen}, title = {Study: Notation of Causal Graphs [Data set]}, doi = {10.5281/zenodo.7241158}, abstract = {This repository contains the material and obtained data of an eye tracking study on the topic "Notation of Causal Graphs".}, language = {en} } @misc{GrabingerHauserMottok, author = {Grabinger, Lisa and Hauser, Florian and Mottok, J{\"u}rgen}, title = {Study: Layout of Causal Graphs [Data set]}, doi = {10.5281/zenodo.7241097}, abstract = {This repository contains the material and obtained data of an eye tracking study on the topic "Layout of Causal Graphs".}, language = {en} } @misc{GrabingerHomannHauseretal., author = {Grabinger, Lisa and Homann, Alexander and Hauser, Florian and Mottok, J{\"u}rgen}, title = {Study: MISRA C coding guidelines [Data set]}, doi = {10.5281/zenodo.7898606}, abstract = {This repository contains the material and obtained data of an eye tracking study on the topic "MISRA C coding guidelines".}, language = {en} } @misc{BittnerEzerGrabingeretal., author = {Bittner, Dominik and Ezer, Timur and Grabinger, Lisa and Hauser, Florian and Mottok, J{\"u}rgen}, title = {Eye Tracking based Learning Style Identification for Learning Management Systems [Data set]}, doi = {10.5281/zenodo.8349468}, abstract = {In recent years, universities have been faced with increasing numbers of students dropping out. This is partly due to the fact that students are limited in their ability to explore individual learning paths through different course materials. However, a promising remedy to this issue is the implementation of adaptive learning management systems. These systems recommend customised learning paths to students - based on their individual learning styles. Learning styles are commonly classified using questionnaires and learning analytics, but both methods are prone to error. Questionnaires may yield superficial responses due to time constraints or lack of motivation, while learning analytics ignore offline learning behaviour. To address these limitations, this study aims to integrating Eye Tracking for a more accurate classification of students' learning styles. Ultimately, this comprehensive approach could not only open up a deeper understanding of subconscious processes, but also provide valuable insights into students' unique learning preferences.}, language = {en} } @inproceedings{NadimpalliMaierStauferetal., author = {Nadimpalli, Vamsi Krishna and Maier, Robert and Staufer, Susanne and R{\"o}hrl, Simon and Ezer, Timur and Grabinger, Lisa and Hauser, Florian and Mottok, J{\"u}rgen}, title = {EXPERT SURVEYS TO REAL TIME ADAPTATION OF LEARNING PATHS}, series = {ICERI2025 Proceedings}, booktitle = {ICERI2025 Proceedings}, editor = {G{\´o}mez Chova, Luis and Gonz{\´a}lez Mart{\´i}nez, Chelo and Lees, Joanna}, publisher = {IATED}, doi = {10.21125/iceri.2025.1571}, pages = {5677 -- 5687}, abstract = {Learning management systems rely on adaptive algorithms that use learner preferences to personalize the instructional content in form of learning paths. However, these preferences are uncertain in nature, and change over time. The present solutions are either static or purely data-driven missing the dynamic adaption to changes in the preferences and infusion of pedagogical nuances respectively. This paper introduces an extended variant of Nestor, our Bayesian network engine that models personality traits, learning styles, and learning strategies. This extension overlays a lightweight rule-based mechanism whose "secret recipe'' lies in the infusion of expert-derived weights adapting learning paths dynamically whenever a learner selects new material in Moodle. To parameterise these rules, we conducted a structured survey with 12 hand-picked professors and researchers in educational science. Each expert responded to 4 demographic items and 12 item that are distributed across algorithm-overview, scenario-based, and example-based categories, thereby supplying the nuanced weightings that result the personalised recommendations. This hybrid system (Nestor plus the expert-infused rule layer) operated during the winter term of 2025. 18 students completed an end-of-term questionnaire. Although their learning gains were not recorded, the majority of respondents reported positive or neutral experiences with the dynamically adapted learning paths. The {Future work} will compare three engines: (i) the present dynamic, expert-infused rule layer on top of the static Bayesian network, (ii) purely data-driven machine-learning models that neglect expert weighting, and (iii) the original static-adaptation Bayesian network without rules. Analyses of log files, intermediate satisfaction surveys, and pre/post term surveys will clarify whether this on-the-fly adaptation and pedagogical nuance lead to measurable learning benefits.}, language = {en} } @inproceedings{SchafferEzerRoehrletal., author = {Schaffer, Josefa and Ezer, Timur and R{\"o}hrl, Simon and Hauser, Florian and Staufer, Susanne and Nadimpalli, Vamsi Krishna and Grabinger, Lisa and Antoni, Erika and Mottok, J{\"u}rgen}, title = {EYE TRACKING GLASSES IN EDUCATIONAL SETTINGS: GUIDELINES ON DATA QUALITY}, series = {ICERI2025 Proceedings}, booktitle = {ICERI2025 Proceedings}, editor = {G{\´o}mez Chova, Luis and Gonz{\´a}lez Mart{\´i}nez, Chelo and Lees, Joanna}, publisher = {IATED}, doi = {10.21125/iceri.2025.1419}, pages = {5027 -- 5038}, abstract = {Eye movement modeling examples, so-called EMME videos, are a valuable tool in education, helping learners better understand instructional content. Like conventional educational videos, EMME videos combine text, images, and voiceovers. However, they also display the instructor's or teacher's gaze, guiding learners attention to key elements. Although various approaches exist for creating EMME videos, there is currently no standardised guideline for ensuring gaze data quality. Eye tracking technology is essential to capture gaze behaviour, and in educational settings without a fixed computer monitor - such as when using blackboards or conducting live experiments - the usage of mobile eye tracking glasses is beneficial. An accuracy study is conducted using mobile eye tracking glasses to provide empirical guidance for the development of high-quality educational EMME videos and ensure that the instructor's or teacher's gaze is captured with high precision. The study uses the Tobii Pro Glasses 3 and involves a static and a dynamic setup with 34 participants. To gain insight into the effects of visual impairments on accuracy, we also include participants who wear contact lenses. In the static setup, participants are seated at a desk with a headrest and focus on a poster with nine fixation points. In the dynamic setup, participants are walking in a controlled half-circle around the poster while maintaining focus on its centre. Each setup is performed multiple times under varying lighting levels (300 lux, 700 lux) and distances between participant and poster (80 cm, 120 cm, 180 cm). This enables the simulation of diverse educational environments, including the possibility of a teacher's or instructor's movement. The study results will be evaluated regarding lighting conditions, the distance between the person wearing eye tracking glasses and the object, and possible influences of contact lenses. Based on these findings, favourable conditions for creating EMME videos in educational settings are collected, especially when working without a fixed computer monitor. The results address the outlined research gap by providing instructors and teachers with guidelines enabling them to produce high-quality educational EMME videos.}, language = {en} } @inproceedings{AlhulaibiFrauenschlaegerMottok, author = {Alhulaibi, Ayham and Frauenschl{\"a}ger, Tobias and Mottok, J{\"u}rgen}, title = {Towards Post-Quantum-Ready Automated Certificate Lifecycle Management in Operational Technology}, series = {SECURWARE 2025, The Nineteenth International Conference on Emerging Security Information, Systems and Technologies, 26.-30.10.2025, Barcelona}, booktitle = {SECURWARE 2025, The Nineteenth International Conference on Emerging Security Information, Systems and Technologies, 26.-30.10.2025, Barcelona}, publisher = {IARIA}, isbn = {978-1-68558-306-4}, pages = {112 -- 116}, abstract = {Operational Technology (OT) systems increasingly depend on robust and automated certificate lifecycle management to maintain secure operations across long device lifespans and constrained environments. As quantum-capable adversaries emerge, these systems must also support cryptographic agility and prepare for a seamless transition to Post-Quantum Cryptography (PQC). This work presents a crypto-agile, post-quantum-ready testbed architecture that extends existing standards, such as Enrollment over Secure Transport (EST) and Bootstrapping Remote Secure Key Infrastructure (BRSKI), to support hybrid certificates, hardware-based key storage, and protocol flexibility for device bootstrapping and certificate management. A work-in-progress prototype implementation demonstrates support for both traditional and PQC algorithms across device types. Planned evaluations target performance on constrained devices, PQC readiness, and compatibility with alternative protocols. The system lays a foundation for secure and standards-compliant certificate management in future-proof OT deployments.}, language = {en} } @inproceedings{HauserStauferRoehrletal., author = {Hauser, Florian and Staufer, Susanne and R{\"o}hrl, Simon and Nadimpalli, Vamsi Krishna and Ezer, Timur and Grabinger, Lisa and Mottok, J{\"u}rgen and Falter, Thomas}, title = {LEVERAGING FIVE QUESTIONNAIRES TO ANALYZE STUDENT LEARNING STRATEGIES AND GENERATE AI-POWERED INDIVIDUALIZED LEARNING PATHS}, series = {ICERI2025 Proceedings}, booktitle = {ICERI2025 Proceedings}, editor = {G{\´o}mez Chova, Luis and Gonz{\´a}lez Mart{\´i}nez, Chelo and Lees, Joanna}, publisher = {IATED}, doi = {10.21125/iceri.2025.0658}, pages = {1775 -- 1784}, abstract = {Background: The COVID-19 pandemic has significantly accelerated the shift toward online and blended learning in higher education, placing renewed emphasis on the individualization of learning content to meet diverse student needs. Even high-quality learning materials may fail to engage learners if they do not align with students' personal preferences and learning styles. Identifying these learner preferences, therefore, emerges as a critical challenge. Objectives: This paper presents ongoing work within a larger research project aimed at employing artificial intelligence to recommend optimal learning path for students in specific courses. Beyond mere optimization, the goal is to ensure the best possible fit between learning materials and individual learners. Sample \& Methods: A total of 27 students from technical degree programs took part in this survey. All participation was voluntary, and data were handled in full compliance with GDPR regulations. Although our broader project integrates fine-grained learning analytics from Moodle, the present abstract focuses exclusively on the self-report questionnaire results. Participants completed five instruments: 1. Index of Learning Styles (ILS) 2. LIST-K (Learning and Study Strategies Inventory - Short version) 3. BFI-10 (Big Five Inventory - 10 items) 4. Custom Preferences Instrument, capturing preferences for specific learning elements (e.g. instructional videos, lecture notes, summaries) and basic demographic data 5. Motivational Value Systems Questionnaire (MVSQ), piloted last semester to assess value orientations and motivational drivers Results: Preliminary analyses of the questionnaire data reveal: - Learning Styles (ILS): The majority lean toward the visual learning type (M = 5.740, SD = 3.430). - Learning Strategies (LIST-K): High scores on metacognitive strategies (M = 3.000; SD = 0.520) and collaboration with peers (M = 3.190; SD = 0.540). - Preferred Learning Elements: Summaries, overviews, and self-checks are most favored. - Value Orientations (MVSQ): Students are primarily driven by the pursuit of personal achievement (M = 4.400; SD = 11.140). Conclusion \& Significance: By integrating these five standardized questionnaires, we gain valuable insights into student learning preferences—insights that complement our Moodle analytics in the broader project. Observed trends suggest that learning materials should be concise and designed to facilitate peer interaction and knowledge deepening. These findings will guide the refinement of our AI-driven recommendation engine, enhancing its ability to deliver personalized learning paths that boost both engagement and effectiveness.}, language = {en} } @inproceedings{EzerRoehrlMottok, author = {Ezer, Timur and R{\"o}hrl, Simon and Mottok, J{\"u}rgen}, title = {Towards accurate eye tracking: quantifying error in linear pixel-to-degree conversion}, series = {ICERI2025 Proceedings}, booktitle = {ICERI2025 Proceedings}, editor = {G{\´o}mez Chova, Luis and Gonz{\´a}lez Mart{\´i}nez, Chelo and Lees, Joanna}, publisher = {IATED}, doi = {10.21125/iceri.2025.1841}, pages = {6709 -- 6717}, abstract = {Eye tracking has become a powerful tool for analyzing cognitive processes in educational research. Educators and researchers can utilize eye tracking to model learners by determining their gaze patterns. In addition, eye tracking can be used directly for teaching by extracting experts' gaze behavior in certain areas and learning from it. However, eye tracking data is captured in a variety of coordinate systems, which can differ across individual studies. Consequently, algorithms, such as eye movement classifiers, must frequently convert gaze data between different coordinate systems. In particular, the conversion from screen pixels into visual degrees is typically approximated by a linear conversion in current literature and standard practice. However, this approximation introduces inaccuracies, thus potentially obscuring eye movements relevant to educational research. This paper provides a detailed geometric and analytical examination of the commonly applied linear approximation, quantifying its error in comparison to the exact coordinate conversion from screen pixels to angular degrees. For this purpose, the exact conversion formulas are mathematically derived from geometric optics, enabling researchers and educators to use them in their work. Utilizing these derived transformations can improve the robustness of analyses, for example, when detecting subtle eye movements. The present work supports educational research using eye tracking to achieve more insightful findings that may have previously been obscured by measurement inaccuracies. Thereby, we provide an important contribution toward more reliable and valid research and educational practices in eye tracking.}, language = {en} } @inproceedings{StauferEzerRoehrletal., author = {Staufer, Susanne and Ezer, Timur and R{\"o}hrl, Simon and Grabinger, Lisa and Hauser, Florian and Nadimpalli, Vamsi Krishna and Antoni, Erika and Mottok, J{\"u}rgen and Schaffer, Josefa}, title = {TYCHE ALGORITHM 2.0: Learning Paths from Questionnaire Responses and Learning Analytics}, series = {ICERI2025 Proceedings}, booktitle = {ICERI2025 Proceedings}, publisher = {IATED}, isbn = {978-84-09-78706-7}, doi = {10.21125/iceri.2025.0831}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:898-opus4-87846}, pages = {2562 -- 2572}, abstract = {The digitalization of learning processes has increased the need for adaptive learning paths tailored individually to learners. A novel algorithm for learning path generation is presented in this paper, namely Tyche 2.0. It extends the original Tyche approach after Staufer et al. - a Markov model for generating learning paths - by integrating additional learner data beyond learning styles (Index of Learning Styles (ILS)), including learning strategies (LIST-K questionnaire), personality traits (BFI-10 questionnaire), and learning analytics captured through screen recordings. In order to be able to use the screen recordings, a heuristic evaluates them. Furthermore, this enhanced algorithm employs Markov models to dynamically generate personalized learning paths. These are based on both questionnaire responses and real-time engagement data, the weights of which undergo dynamic adjustment over time. We made a small evaluation of Tyche 2.0 without the learning analytics influence, which shows that there is room for further improvements. Future research will focus on evaluating whole Tyche 2.0 in another university setting to further improve personalization and user engagement.}, language = {en} }