arXiv subjects

Nikol Rummel

Publications and source records attributed to Nikol Rummel.

15 recordsLinked to original sources

arXiv2026

With a Thermomix You Lose the Ability to Cook: A Kitchen Machine Analogy for Applications of Generative AI in Education

The rapid adoption of generative AI tools such as ChatGPT has sparked intense debate about their risks and opportunities for education, as well as the ways researchers should investigate them. In this paper, we approach these discussions through an analogy with the Thermomix, a smart kitchen appliance that has similarly provoked both enthusiasm and critique. By mapping Thermomix use cases onto examples of learning with generative AI, and situating them within the ICAP and SAMR frameworks, we show how different modes of tool use can either support or undermine meaningful engagement and learning. The Thermomix metaphor underscores that the central question is not whether learners employ AI, but how such use shapes their learning processes. In doing so, we provide a conceptual lens for researchers and practitioners to critically examine - and more effectively guide - the integration of generative AI into educational practice.

cs.CY↗

arXiv2026

Designing a Hardware Reverse Engineering Course: Lessons from Eight Years in a Rapidly Evolving Tech Domain

Integrated Circuits (ICs) are omnipresent, yet their globalized manufacturing process remains vulnerable to supply chain threats. Hardware Reverse Engineering (HRE) is essential for detecting such threats and re-establishing trust; however domain experts remain scarce due to a lack of educational programs. To contribute educational insights in this critical and rapidly evolving technology domain, we present our HRE course focusing on digital circuit analysis and digital circuit extraction from ICs. The course targets junior-level undergraduates at a major European research university. The curriculum has been refined over nine iterations (2017-2025), with several alumni subsequently pursuing careers in the HRE field. By reflecting on the evolution of the course organization, content, and assignments, we derive key lessons learned. We further distill these insights into actionable design priorities for educators developing courses in rapidly evolving technological domains, emphasizing iterative growth and sustainable workload management for both students and instructors.

cs.CY↗

arXiv2026

Balancing Teacher and Student Agency: Co-Orchestration Tool Design Supporting Real-Time Dynamic Pairing

In human-AI interaction, respecting user agency is essential for fostering trust and sustaining effective use of technology. In educational settings, dynamically integrating individual and collaborative learning offers pedagogical value by supporting personalized, self-paced learning experiences. Prior research has demonstrated the feasibility of this approach through intelligent tutoring systems and human-AI co-orchestration tools. However, how to balance teacher and student control in this process remains largely unexplored. This work explores the design space of how control can be distributed between teachers and students across the orchestration process, using participatory speed dating and a mixed-method analysis. We focus on three stages of the pairing process: before, during, and after, taking context in designing classroom orchestration tools that support teachers in dynamically coordinating student transitions between individual practice and collaborative problem-solving. It contributes empirical insights to the fields of educational technology and HCI by framing these findings within a theoretical design space, emphasizing the balance of multi-stakeholder agency and control. We propose design recommendations for achieving hybrid-control in analytic-based orchestration tools in pairing contexts. We recommend ensuring structured teacher guidance in the beginning, while progressively increasing student autonomy over time as activities unfold.

cs.HC↗

arXiv2026

The Future of Feedback: How Can AI Help Transform Feedback to Be More Engaging, Effective, and Scalable?

With digital learning environments becoming more prevalent, the ease with which generative AI enables the scalable production of real-time, automated feedback holds the potential to reshape learning and teaching experiences. This meeting report synthesizes the interdisciplinary perspectives of 50 scholars from educational psychology, computer science, science education, and the learning sciences on the use of generative AI for feedback and its promises and risks in educational practice. We highlight points of convergence in the scholarship, identify areas of debate and unresolved challenges, and outline open questions and future directions for research and educational practice that emerged from structured small-group activities designed to bridge disciplinary barriers.

cs.CY↗

arXiv2026

Protecting and Promoting Human Agency in Education in the Age of Artificial Intelligence

Human agency is crucial in education and increasingly challenged by the use of generative AI. This meeting report synthesizes interdisciplinary insights and conceptualizes four aspects that delineate human agency: human oversight, AI-human complementarity, AI competencies, and relational emergence. We explore practical dilemmas for protecting and promoting agency, focusing on normative constraints, transparency, and cognitive offloading, and highlight key tensions and implications to inform ethical and effective AI integration in education.

cs.HC↗

arXiv2026

Are they just delegating? Cross-Sample Predictions on University Students' & Teachers' Use of AI

Mutual trust between teachers and students is a prerequisite for effective teaching, learning, and assessment in higher education. Accurate predictions about the other group's use of generative artificial intelligence (AI) are fundamental for such trust. However, the disruptive rise of AI has transformed academic work practices, raising important questions about how teachers and students use these tools and how well they can estimate each other's usage. While the frequency of use is well studied, little is known about how AI is used, and comparisons with similar practices are rare. This study surveyed German university teachers (N = 113) and students (N = 123) on the frequency of AI use and the degree of delegation across six identical academic tasks. Participants also provided incentivized cross-sample predictions of the other group's AI use to assess the accuracy of their predictions. We find that students reported higher use of AI and greater delegation than teachers. Both groups significantly overestimated the other group's use, with teachers predicting very frequent use and high delegation by students, and students assuming teachers use AI similarly to themselves. These findings reveal a perception gap between teachers' and students' expectations and actual AI use. Such gaps may hinder trust and effective collaboration, underscoring the need for open dialogue about AI practices in academia and for policies that support the equitable and transparent integration of AI tools in higher education.

cs.HC↗

arXiv2024

An Evidence-Based Curriculum Initiative for Hardware Reverse Engineering Education

The increasing importance of supply chain security for digital devices -- from consumer electronics to critical infrastructure -- has created a high demand for skilled cybersecurity experts. These experts use Hardware Reverse Engineering (HRE) as a crucial technique to ensure trust in digital semiconductors. Recently, the US and EU have provided substantial funding to educate this cybersecurity-ready semiconductor workforce, but success depends on the widespread availability of academic training programs. In this paper, we investigate the current state of education in hardware security and HRE to identify efficient approaches for establishing effective HRE training programs. Through a systematic literature review, we uncover 13 relevant courses, including eight with accompanying academic publications. We identify common topics, threat models, key pedagogical features, and course evaluation methods. We find that most hardware security courses do not prioritize HRE, making HRE training scarce. While the predominant course structure of lectures paired with hands-on projects appears to be largely effective, we observe a lack of standardized evaluation methods and limited reliability of student self-assessment surveys. Our results suggest several possible improvements to HRE education and offer recommendations for developing new training courses. We advocate for the integration of HRE education into curriculum guidelines to meet the growing societal and industry demand for HRE experts.

cs.CY↗

arXiv2024

I see an IC: A Mixed-Methods Approach to Study Human Problem-Solving Processes in Hardware Reverse Engineering

Trust in digital systems depends on secure hardware, often assured through Hardware Reverse Engineering (HRE). This work develops methods for investigating human problem-solving processes in HRE, an underexplored yet critical aspect. Since reverse engineers rely heavily on visual information, eye tracking holds promise for studying their cognitive processes. To gain further insights, we additionally employ verbal thought protocols during and immediately after HRE tasks: Concurrent and Retrospective Think Aloud. We evaluate the combination of eye tracking and Think Aloud with 41 participants in an HRE simulation. Eye tracking accurately identifies fixations on individual circuit elements and highlights critical components. Based on two use cases, we demonstrate that eye tracking and Think Aloud can complement each other to improve data quality. Our methodological insights can inform future studies in HRE, a specific setting of human-computer interaction, and in other problem-solving settings involving misleading or missing information.

cs.HC↗

arXiv2023

REVERSIM: An Open-Source Environment for the Controlled Study of Human Aspects in Hardware Reverse Engineering

Hardware Reverse Engineering (HRE) is a technique for analyzing integrated circuits. Experts employ HRE for security-critical tasks, like detecting Trojans or intellectual property violations, relying not only on their experience and customized tools but also on their cognitive abilities. In this work, we introduce ReverSim, a software environment that models key HRE subprocesses and integrates standardized cognitive tests. ReverSim enables quantitative studies with easier-to-recruit non-experts to uncover cognitive factors relevant to HRE. We empirically evaluated ReverSim in three studies. Semi-structured interviews with 14 HRE professionals confirmed its comparability to real-world HRE processes. Two online user studies with 170 novices and intermediates revealed effective differentiation of participant performance across a spectrum of difficulties, and correlations between participants' cognitive processing speed and task performance. ReverSim is available as open-source software, providing a robust platform for controlled experiments to assess cognitive processes in HRE, potentially opening new avenues for hardware protection.

cs.CR↗

arXiv2022

What should AI see? Using the Public's Opinion to Determine the Perception of an AI

Deep neural networks (DNN) have made impressive progress in the interpretation of image data, so that it is conceivable and to some degree realistic to use them in safety critical applications like automated driving. From an ethical standpoint, the AI algorithm should take into account the vulnerability of objects or subjects on the street that ranges from "not at all", e.g. the road itself, to "high vulnerability" of pedestrians. One way to take this into account is to define the cost of confusion of one semantic category with another and use cost-based decision rules for the interpretation of probabilities, which are the output of DNNs. However, it is an open problem how to define the cost structure, who should be in charge to do that, and thereby define what AI-algorithms will actually "see". As one possible answer, we follow a participatory approach and set up an online survey to ask the public to define the cost structure. We present the survey design and the data acquired along with an evaluation that also distinguishes between perspective (car passenger vs. external traffic participant) and gender. Using simulation based $F$-tests, we find highly significant differences between the groups. These differences have consequences on the reliable detection of pedestrians in a safety critical distance to the self-driving car. We discuss the ethical problems that are related to this approach and also discuss the problems emerging from human-machine interaction through the survey from a psychological point of view. Finally, we include comments from industry leaders in the field of AI safety on the applicability of survey based elements in the design of AI functionalities in automated driving.

cs.LG↗

arXiv2021

An Exploratory Study of Hardware Reverse Engineering Technical and Cognitive Processes

Understanding the internals of Integrated Circuits (ICs), referred to as Hardware Reverse Engineering (HRE), is of interest to both legitimate and malicious parties. HRE is a complex process in which semi-automated steps are interwoven with human sense-making processes. Currently, little is known about the technical and cognitive processes which determine the success of HRE. This paper performs an initial investigation on how reverse engineers solve problems, how manual and automated analysis methods interact, and which cognitive factors play a role. We present the results of an exploratory behavioral study with eight participants that was conducted after they had completed a 14-week training. We explored the validity of our findings by comparing them with the behavior (strategies applied and solution time) of an HRE expert. The participants were observed while solving a realistic HRE task. We tested cognitive abilities of our participants and collected large sets of behavioral data from log files. By comparing the least and most efficient reverse engineers, we were able to observe successful strategies. Moreover, our analyses suggest a phase model for reverse engineering, consisting of three phases. Our descriptive results further indicate that the cognitive factor Working Memory (WM) might play a role in efficiently solving HRE problems. Our exploratory study builds the foundation for future research in this topic and outlines ideas for designing cognitively difficult countermeasures ("cognitive obfuscation") against HRE.

cs.CR↗

arXiv2021

Promoting the Acquisition of Hardware Reverse Engineering Skills

This full research paper focuses on skill acquisition in Hardware Reverse Engineering (HRE) - an important field of cyber security. HRE is a prevalent technique routinely employed by security engineers (i) to detect malicious hardware manipulations, (ii) to conduct VLSI failure analysis, (iii) to identify IP infringements, and (iv) to perform competitive analyses. Even though the scientific community and industry have a high demand for HRE experts, there is a lack of educational courses. We developed a university-level HRE course based on general cognitive psychological research on skill acquisition, as research on the acquisition of HRE skills is lacking thus far. To investigate how novices acquire HRE skills in our course, we conducted two studies with students on different levels of prior knowledge. Our results show that cognitive factors (e.g., working memory), and prior experiences (e.g., in symmetric cryptography) influence the acquisition of HRE skills. We conclude by discussing implications for future HRE courses and by outlining ideas for future research that would lead to a more comprehensive understanding of skill acquisition in this important field of cyber security.

cs.CR↗

arXiv2019

Hardware Reverse Engineering: Overview and Open Challenges

Hardware reverse engineering is a universal tool for both legitimate and illegitimate purposes. On the one hand, it supports confirmation of IP infringement and detection of circuit malicious manipulations, on the other hand it provides adversaries with crucial information to plagiarize designs, infringe on IP, or implant hardware Trojans into a target circuit. Although reverse engineering is commonplace in practice, the quantification of its complexity is an unsolved problem to date since both technical and human factors have to be accounted for. A sophisticated understanding of this complexity is crucial in order to provide a reasonable threat estimation and to develop sound countermeasures, i.e. obfuscation transformations of the target circuit, to mitigate risks for the modern IC landscape. The contribution of our work is threefold: first, we systematically study the current research branches related to hardware reverse engineering ranging from decapsulation to gate-level netlist analysis. Based on our overview, we formulate several open research questions to scientifically quantify reverse engineering, including technical and human factors. Second, we survey research on problem solving and on the acquisition of expertise and discuss its potential to quantify human factors in reverse engineering. Third, we propose novel directions for future interdisciplinary research encompassing both technical and psychological perspectives that hold the promise to holistically capture the complexity of hardware reverse engineering.

cs.CR↗

arXiv2019

Teaching Hardware Reverse Engineering: Educational Guidelines and Practical Insights

Since underlying hardware components form the basis of trust in virtually any computing system, security failures in hardware pose a devastating threat to our daily lives. Hardware reverse engineering is commonly employed by security engineers in order to identify security vulnerabilities, to detect IP violations, or to conduct very-large-scale integration (VLSI) failure analysis. Even though industry and the scientific community demand experts with expertise in hardware reverse engineering, there is a lack of educational offerings, and existing training is almost entirely unstructured and on the job. To the best of our knowledge, we have developed the first course to systematically teach students hardware reverse engineering based on insights from the fields of educational research, cognitive science, and hardware security. The contribution of our work is threefold: (1) we propose underlying educational guidelines for practice-oriented courses which teach hardware reverse engineering; (2) we develop such a lab course with a special focus on gate-level netlist reverse engineering and provide the required tools to support it; (3) we conduct an educational evaluation of our pilot course. Based on our results, we provide valuable insights on the structure and content necessary to design and teach future courses on hardware reverse engineering.

cs.CR↗

arXiv2019

Towards Cognitive Obfuscation: Impeding Hardware Reverse Engineering Based on Psychological Insights

In contrast to software reverse engineering, there are hardly any tools available that support hardware reversing. Therefore, the reversing process is conducted by human analysts combining several complex semi-automated steps. However, countermeasures against reversing are evaluated solely against mathematical models. Our research goal is the establishment of cognitive obfuscation based on the exploration of underlying psychological processes. We aim to identify problems which are hard to solve for human analysts and derive novel quantification metrics, thus enabling stronger obfuscation techniques.

cs.CR↗