conduct tabletop exercises

This study addresses the critical gap in effective cybersecurity training for C-suite executives in the maritime sector, who often struggle to respond to complex cyber incidents. To bridge this gap, the authors propose SERDUX-MARCIM, a novel hybrid training framework that integrates mathematical contagion models with tabletop exercises (TTX). This approach uniquely combines dynamic multi-node attack simulation with scenario-based decision-making drills, significantly enhancing executives’ cyber situational awareness, anticipatory judgment, and governance capabilities. Empirical evaluations conducted in Argentina and the United States demonstrate that the framework effectively improves participants’ understanding of cyber threats, their ability to forecast attack trajectories, and their organizations’ overall cybersecurity governance maturity.

This study addresses the widespread lack of scalable and assessable collaborative incident response training in higher education cybersecurity curricula. To bridge this gap, the project introduces—on a large scale for the first time—the web-based INJECT Exercise Platform (IXP) into university courses, enabling technology-enhanced tabletop exercises through automated scenario delivery, real-time team discussion support, and interactive data collection. Between 2024 and 2026, 25 exercises engaged 743 students, significantly enhancing participation and collaborative capabilities while providing instructors with fine-grained learning analytics. The initiative demonstrates the replicability and efficacy of digital tabletop exercises in cybersecurity education and distills 24 evidence-based design principles, establishing an innovative pedagogical framework for future implementations.

Engineering education curricula exhibit structural vulnerabilities that heighten susceptibility to disruption during crises and undermine systemic resilience. Method: This study designs and deploys SUCRE—a serious game integrating scenario simulation, system dynamics modeling, and participatory workshops—to identify critical disruption sources and their cascading effects, while assessing curriculum robustness under stress. Contribution/Results: The research pioneers the systematic application of serious games for diagnosing curriculum resilience in engineering education and proposes a transferable crisis-response analytical framework applicable across institutions. Empirical evaluation demonstrates that SUCRE effectively enables instructors to reflect on structural fragilities, informs targeted curriculum redesign strategies, and significantly enhances the adaptability and recovery capacity of educational systems facing acute perturbations.

This study addresses the challenges of high instructor workload and low observer engagement in simulation-based education, which often compromise debriefing effectiveness. In collaboration with nursing educators, the authors developed PULSE, a real-time annotation tool that introduces a novel student-led annotation mechanism. PULSE enables observing students to actively document their observations and reflections during simulations, thereby enhancing the structure, depth, and interactivity of debriefings. Grounded in human-centered co-design and informed by the DASH (Debriefing Assessment for Simulation in Healthcare) framework alongside qualitative interviews, the study systematically developed and validated an interactive debriefing support mechanism. Preliminary results demonstrate that PULSE significantly improves DASH scores (p = 0.027, Cohen’s d = 2.05), enhances classroom discussion quality, and shifts debriefing practices from instructor-centered toward greater student participation.

This work addresses the limitations of traditional expert-manual-based cybersecurity response methods, which struggle to adapt to dynamic attack scenarios and evolving recovery objectives, as well as the instability of existing large-model approaches in long-horizon tasks. The authors propose an end-to-end agent planning framework that innovatively models event states using a graph structure (Graph-as-State), incorporates a phase-aware agent routing mechanism, and establishes a verifiable experience reuse loop to guide action selection and state updates. The system integrates multi-agent large language models with experience retrieval augmentation and execution feedback verification, enabling dynamic, stable, and evolvable response planning within a Docker-based network range simulation environment. Experimental results demonstrate that the proposed method achieves a normalized defense score of 0.94 across 100 simulated scenarios, representing a 9.5% improvement over the strongest baseline.

This study addresses the ethical challenges of representing human suffering in virtual reality (VR) serious games for emergency response training, particularly the risk of reducing distress to adjustable difficulty levels. Through critical design document analysis and qualitative content analysis, the research introduces two core concepts: “affective dramaturgy” and “dual responsiveness”—encompassing in-game reactions and post-simulation debriefings—to illuminate how emotional and moral dimensions are susceptible to problematic technocratic framing. The work identifies six categories of stress-inducing mechanisms and their associated design issues, proposing a sensitizing reflective framework centered on professional purpose, representational ethics, and systemic accountability. This framework offers theoretical guidance and ethical reference points for the design of serious games that responsibly engage with human vulnerability and moral complexity.

This study addresses the challenge of delayed and incomplete evaluation in team tabletop exercises (TTX), which often arises due to the open-ended and complex nature of such tasks, hindering effective assessment of team learning outcomes. To overcome this limitation, the authors propose a novel approach that integrates clustering algorithms with large language models (GPT-4o and GPT-5.2) to enable automated, scalable evaluation of team performance. Leveraging action logs and communication transcripts from 81 multinational participants alongside standardized scoring rubrics, the method demonstrates that clustering is computationally efficient and reliable, while GPT-5.2 significantly outperforms GPT-4o in evaluating team communication with lower error rates. All data, tools, and the complete TTX scenario have been open-sourced and integrated into the INJECT platform to support educational applications.

This work addresses the significant risks associated with poorly designed mental health interventions by proposing a novel, safe, and controlled environment for testing and training. Integrating virtual reality, agent-based modeling, and embodied interaction technologies, the study systematically incorporates human-centered design principles into a virtual simulation platform for the first time. The resulting immersive environment enables individuals to engage in secure mental health training within realistic scenarios while simultaneously allowing researchers to evaluate the potential community-level impacts of interventions through agent-based modeling. By unifying user-centered design, scalability, and empirical validation, this research establishes a new paradigm for developing evidence-based mental health intervention systems that are both ethically sound and practically effective.