Enabling Cyber Security Education through Digital Twins and Generative AI

Digital Twins (DTs) are gaining prominence in cybersecurity for their ability to replicate complex IT (Information Technology), OT (Operational Technology), and IoT (Internet of Things) infrastructures, allowing for real time monitoring, threat analysis, and system simulation. This study investigates how integrating DTs with penetration testing tools and Large Language Models (LLMs) can enhance cybersecurity education and operational readiness. By simulating realistic cyber environments, this approach offers a practical, interactive framework for exploring vulnerabilities and defensive strategies. At the core of this research is the Red Team Knife (RTK), a custom penetration testing toolkit aligned with the Cyber Kill Chain model. RTK is designed to guide learners through key phases of cyberattacks, including reconnaissance, exploitation, and response within a DT powered ecosystem. The incorporation of Large Language Models (LLMs) further enriches the experience by providing intelligent, real-time feedback, natural language threat explanations, and adaptive learning support during training exercises. This combined DT LLM framework is currently being piloted in academic settings to develop hands on skills in vulnerability assessment, threat detection, and security operations. Initial findings suggest that the integration significantly improves the effectiveness and relevance of cybersecurity training, bridging the gap between theoretical knowledge and real-world application. Ultimately, the research demonstrates how DTs and LLMs together can transform cybersecurity education to meet evolving industry demands.

Explanation-Driven Interventions for Artificial Intelligence Model Customization: Empowering End-Users to Tailor Black-Box AI in Rhinocytology

The integration of Artificial Intelligence (AI) in modern society is heavily shifting the way that individuals carry out their tasks and activities. Employing AI-based systems raises challenges that designers and developers must address to ensure that humans remain in control of the interaction process, particularly in high-risk domains. This article presents a novel End-User Development (EUD) approach for black-box AI models through a redesigned user interface in the Rhino-Cyt platform, a medical AI-based decision-support system for medical professionals (more precisely, rhinocytologists) to carry out cell classification. The proposed interface empowers users to intervene in AI decision-making process by editing explanations and reconfiguring the model, influencing its future predictions. This work contributes to Human-Centered AI (HCAI) and EUD by discussing how explanation-driven interventions allow a blend of explainability, user intervention, and model reconfiguration, fostering a symbiosis between humans and user-tailored AI systems.

Building Symbiotic AI: Reviewing the AI Act for a Human-Centred, Principle-Based Framework

Artificial Intelligence (AI) spreads quickly as new technologies and services take over modern society. The need to regulate AI design, development, and use is strictly necessary to avoid unethical and potentially dangerous consequences to humans. The European Union (EU) has released a new legal framework, the AI Act, to regulate AI by undertaking a risk-based approach to safeguard humans during interaction. At the same time, researchers offer a new perspective on AI systems, commonly known as Human-Centred AI (HCAI), highlighting the need for a human-centred approach to their design. In this context, Symbiotic AI (a subtype of HCAI) promises to enhance human capabilities through a deeper and continuous collaboration between human intelligence and AI. This article presents the results of a Systematic Literature Review (SLR) that aims to identify principles that characterise the design and development of Symbiotic AI systems while considering humans as the core of the process. Through content analysis, four principles emerged from the review that must be applied to create Human-Centred AI systems that can establish a symbiotic relationship with humans. In addition, current trends and challenges were defined to indicate open questions that may guide future research for the development of SAI systems that comply with the AI Act.

Detecting Brain Tumors through Multimodal Neural Networks

Tumors can manifest in various forms and in different areas of the human body. Brain tumors are specifically hard to diagnose and treat because of the complexity of the organ in which they develop. Detecting them in time can lower the chances of death and facilitate the therapy process for patients. The use of Artificial Intelligence (AI) and, more specifically, deep learning, has the potential to significantly reduce costs in terms of time and resources for the discovery and identification of tumors from images obtained through imaging techniques. This research work aims to assess the performance of a multimodal model for the classification of Magnetic Resonance Imaging (MRI) scans processed as grayscale images. The results are promising, and in line with similar works, as the model reaches an accuracy of around 98\%. We also highlight the need for explainability and transparency to ensure human control and safety.

End-User Development for Artificial Intelligence: A Systematic Literature Review

In recent years, Artificial Intelligence has become more and more relevant in our society. Creating AI systems is almost always the prerogative of IT and AI experts. However, users may need to create intelligent solutions tailored to their specific needs. In this way, AI systems can be enhanced if new approaches are devised to allow non-technical users to be directly involved in the definition and personalization of AI technologies. End-User Development (EUD) can provide a solution to these problems, allowing people to create, customize, or adapt AI-based systems to their own needs. This paper presents a systematic literature review that aims to shed the light on the current landscape of EUD for AI systems, i.e., how users, even without skills in AI and/or programming, can customize the AI behavior to their needs. This study also discusses the current challenges of EUD for AI, the potential benefits, and the future implications of integrating EUD into the overall AI development process.