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.

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.

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 yield 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.

Trust in digital systems depends on secure hardware, often assured through 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.

Enabling students to dynamically transition between individual and collaborative learning activities has great potential to support better learning. We explore how technology can support teachers in orchestrating dynamic transitions during class. Working with five teachers and 199 students over 22 class sessions, we conducted classroom-based prototyping of a co-orchestration technology ecosystem that supports the dynamic pairing of students working with intelligent tutoring systems. Using mixed-methods data analysis, we study the resulting observed classroom dynamics, and how teachers and students perceived and experienced dynamic transitions as supported by our technology. We discover a potential tension between teachers’ and students’ preferred level of control: students prefer a degree of control over the dynamic transitions that teachers are hesitant to grant. Our study reveals design implications and challenges for future human-AI co-orchestration in classroom use, bringing us closer to realizing the vision of highly-personalized smart classrooms that address the unique needs of each student.

Understanding of microchips, known as Hardware Reverse Engineering (HRE), is driven by analysts’ problem solving. This work sheds light on these hitherto poorly understood problem-solving processes. We propose a methodology addressing the problem of HRE experts being unavailable for research. We developed a training enabling students to acquire intermediate levels of HRE expertise. Besides one expert, we recruited eight top-performing students from this training for our exploratory study. All participants completed a realistic HRE task involving the removal of a copyright protection mechanism from a hardware circuit. We analyzed 2,445 log entries by applying an iterative open coding and developed a detailed hierarchical problem-solving model. Our exploration yielded insights into problem-solving strategies and revealed that two intermediates solved the task with a comparable solution time to the expert. We discuss that HRE problem solving may be a function of both expertise and cognitive abilities, and outline ideas for novel countermeasures.

Teachers play an essential role during collaborative learning. To provide effective support, teachers have to be constantly aware of students' activities and make fast decisions about which group to offer support, without disrupting students' collaborative process. Teacher dashboards are visual displays that provide analytics about learners to help teachers increase their awareness of the situation. However, if teachers are not able to efficiently and effectively distill information from the dashboard, the dashboard can become an obstacle instead of an aid. In the present study, we compared dashboards that provide information (mirroring) to dashboards that provide information and alert the teacher to groups that are in need of support (advising). Teachers were shown standardized, fictitious collaborative situations on one of the types of dashboards and were asked to detect the group that was in need of support. The results showed that teachers in the advising condition more often detected the problematic group, needed less effort to do so, and were more confident of their decisions. The teacher-dashboard interaction patterns showed that teachers in the advising condition generally started by checking the given alert, but also that they tried to look at as much information about other groups as they could. In the mirroring condition, teachers generally started by examining information from class overviews, but did not always have time to check information for individual groups. These findings are discussed in light of the role of a teacher dashboard in teachers' decision making in the context of student collaboration.

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.

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.

Successful knowledge integration, that is, systematic synthesis of unshared information, is key to suc-cess, but at the same time a challenging venture for teams with distributed knowledge collaborating online. For example, teams with heterogeneous knowledge often have only vague or even wrong ideas about who knows what. This situation is further complicated if the collaboration partners do not know each other and merely communicate online. Previous research has found meta-knowledge, that is, knowledge about one's own and the partner's knowledge areas, to be a promising but not yet sufficient-ly investigated approach to promote knowledge integration. With our experimental study we aimed to address this desideratum of research on the role of meta-knowledge in net-based collaborations. We "simulated" a chat-based collaboration between partners with heterogeneous knowledge by assigning specific information to students collaborating in dyads on a Hidden Profile task. To arrive at the correct joint solution for this task, collaborating partners had to pool their shared, but more importantly their unshared information. We compared two conditions: In the experimental condition meta-knowledge was promoted by providing the collaboration partners with self-presentations of each other's roles, which pointed to their unique fields of knowledge, while participants in the control condition did not receive this information. Results suggest a positive impact of the meta-knowledge manipulation on two key factors of collaboration: knowledge integration and construction of a transactive memory system (TMS).

This paper describes the development and evaluation of an affect-aware intelligent support component that is part of a learning environment known as iTalk2Learn. The intelligent support component is able to tailor feedback according to a student's affective state, which is deduced both from speech and interaction. The affect prediction is used to determine which type of feedback is provided and how that feedback is presented (interruptive or non-interruptive). The system includes two Bayesian networks that were trained with data gathered in a series of ecologically-valid Wizard-of-Oz studies, where the effect of the type of feedback and the presentation of feedback on students' affective states was investigated. This paper reports results from an experiment that compared a version that provided affect-aware feedback (affect condition) with one that provided feedback based on performance only (non-affect condition). Results show that students who were in the affect condition were less bored and less off-task, with the latter being statically significant. Importantly, students in both conditions made learning gains that were statistically significant, while students in the affect condition had higher learning gains than those in the non-affect condition, although this result was not statistically significant in this study's sample. Taken all together, the results point to the potential and positive impact of affect-aware intelligent support.

Designing interactive learning environments (ILEs; e.g., intelligent tutoring systems, educational games, etc.) is a challenging interdisciplinary process that needs to satisfy multiple stakeholders. ILEs need to function in real educational settings (e.g., schools) in which a number of goals interact. Several instructional design methodologies exist to help developers address these goals. However, they often lead to conflicting recommendations. Due to the lack of an established methodology to resolve such conflicts, developers of ILEs have to rely on ad-hoc solutions. We present a principled methodology to resolve such conflicts. We build on a well-established design process for creating Cognitive Tutors, a highly effective type of ILE. We extend this process by integrating methods from multiple disciplines to resolve design conflicts. We illustrate our methodology's effectiveness by describing the iterative development of the Fractions Tutor, which has proven to be effective in classroom studies with 3,000 4th-6th graders.

In this paper we present first results of a Delphi study on technology-enhanced learning (TEL). The study is carried out as part of the European Network of Excellence STELLAR (Sustaining Technology Enhanced Learning Large-scale multidisciplinary Research). In the 1st Delphi round an expert survey was conducted to identify future trends in TEL research: Forty-one European TEL researchers answered open-ended questions concerning future key societal demands and technological developments, and concerning research themes that could respond to these demands and developments. Answers were coded and categorized using a qualitative approach. To conclude this article we give an outlook on the next steps of the STELLAR Delphi study.

Although a solid understanding of fractions is foundational in mathematics, the concept of fractions remains a challenging one. Previous research suggests that multiple graphical representations (MGRs) may promote learning of fractions. Specifically, we hypothesized that providing students with MGRs of fractions, in addition to the conventional symbolic notation, leads to better learning outcomes as compared to instruction incorporating only one graphical representation. We anticipated, however, that MGRs would make the students' task more challenging, since they must link the representations and distill from them a common concept or principle. Therefore, we hypothesized further that self-explanation prompts would help students benefit from working with MGRs. To investigate these hypotheses, we conducted a classroom study in which 112 6th-grade students used intelligent tutors for fraction conversion and fraction addition. The results of the study show that students learned more with MGRs of fractions than with a single representation, but only when prompted to self-explain how the graphics relate to the symbolic fraction representations.

Giving effective help is an important collaborative skill that leads to improved learning for both the help-giver and help-receiver. Adding intelligent tutoring to student interaction may be one effective way of assisting students in giving and receiving better help. However, such systems have proven difficult to implement, in part due to the challenges of modeling productive dialogue in a collaborative activity. We present a theoretical model of good helping behavior in a peer tutoring context, and validate the model using student tutoring data, linking optimal and buggy behaviors to learning outcomes. We discuss the implications of the model with respect to providing intelligent tutoring for peer tutoring.