In modern digital infrastructure, cyber systems are foundational, making resilience against sophisticated attacks essential. Traditional cybersecurity defenses primarily address technical vulnerabilities; however, the human element, particularly decision-making during cyber attacks, adds complexities that current behavioral studies fail to capture adequately. Existing approaches, including theoretical models, game theory, and simulators, rely on retrospective data and static scenarios. These methods often miss the real-time, context-specific nature of user responses during cyber threats. To address these limitations, this work introduces a framework that combines Extended Reality (XR) and Generative Artificial Intelligence (Gen-AI) within a gamified platform. This framework enables continuous, high-fidelity data collection on user behavior in dynamic attack scenarios. It includes three core modules: the Player Behavior Module (PBM), Gamification Module (GM), and Simulation Module (SM). Together, these modules create an immersive, responsive environment for studying user interactions. A case study in a simulated critical infrastructure environment demonstrates the framework’s effectiveness in capturing realistic user behaviors under cyber attack, with potential applications for improving response strategies and resilience across critical sectors. This work lays the foundation for adaptive cybersecurity training and user-centered development across critical infrastructure.

Background: Mixed Reality (MR) environments rely on multimodal feedback to enrich sensory integration and realism, which enhances User Experience (UX). Prior studies have shown the benefits of haptic feedback in audio–visual MR medical training environments, but researchers have not fully examined how audio cues influence Haptic–Visual (HV) training environments. Methods: We built a high-fidelity MR medical training environment that synchronized visual, haptic, and audio of the human pulse. We conducted a between-subjects study with thirty novice participants who performed pulse palpation tasks in HV and Haptic–Audio–Visual (HAV) modalities. We employ a multidimensional UX evaluation by measuring task performance, presence, usability, and task workload to assess the impact of adding audio feedback in MR pulse palpation training environments. Results: Participants in the HAV modality performed tasks more accurately and reported stronger presence and higher usability. They did not report any significant increase in workload compared to the HV modality. Conclusions: Audio feedback improved perceptual coherence and enhanced UX in pulse palpation tasks. Our findings highlight the training value of integrating multimodal feedback in MR pulse palpation training systems and provide practical guidelines for designing more immersive and effective MR environments.