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The Industrial Revolution (IR) involves a centuries-long process of economic and societal transformation driven by industrial and technological innovation. From agrarian, craft-based societies to modern systems powered by Artificial Intelligence (AI), each IR has brought significant societal advancements yet raised concerns about future implications. As we transition from the Fourth Industrial Revolution (IR4.0) to the emergent Fifth Industrial Revolution (IR5.0), similar questions arise regarding human employment, technological control, and adaptation. During all these shifts, a recurring theme emerges as we fear the unknown and bring a concern that machines may replace humans’ hard and soft skills. Therefore, comprehensive preparation, critical discussion, and future-thinking policies are necessary to successfully navigate any industrial revolution. While IR4.0 emphasized cyber-physical systems, IoT (Internet of Things), and AI-driven automation, IR5.0 aims to integrate these technologies, keeping human, emotion, intelligence, and ethics at the center. This paper critically examines this transition by highlighting the technological foundations, socioeconomic implications, challenges, and opportunities involved. We explore the role of AI, blockchain, edge computing, and immersive technologies in shaping IR5.0, along with workforce reskilling strategies to bridge the potential skills gap. Learning from historic patterns will enable us to navigate this era of change and mitigate any uncertainties in the future.

This study explores nursing students’ stress responses while they are being trained in a mixed reality (MR) setting that replicates highly stressful clinical scenarios. Using measurements of physiological indices such as heart rate, electrodermal activity, and skin temperature, the study assesses the level of stress when the students interact with digital patients whose vital signs and symptoms interact dynamically to respond to student inputs. The simulation consists of six segments, during which critical events like hypotension and hypoxia occur, and the patient’s condition changes based on the nurse’s clinical decisions. Machine learning algorithms were then used to analyze the nurse’s physiological data and to classify different levels of stress. Among the models tested, the Stacking Classifier demonstrated the highest classification accuracy of 96.4%, outperforming both Random Forest (96.18%) and Gradient Boosting (95.35%). The results showed clear patterns of stress during the simulation segments. Statistical analysis also found significant differences in stress responses and identified key physiological markers linked to each stress level. This pioneering study demonstrates the effectiveness of MR as a training tool for healthcare professionals in high-pressured scenarios and lays the groundwork for further studies on stress management, adaptive training procedures, and real-time detection and intervention in MR-based nursing training.

Integrating Mixed Reality (MR) into nursing education and professional practice has recently captured significant interest as a transformative approach. This paper presents a comprehensive exploration and practical insights into designing and implementing an advanced MR training platform to provide nursing students with immersive experiences across various patient care scenarios. Further enhancing the platform’s utility is the incorporation of a unique conversational artificial intelligence (AI) module. This innovation breathes life into digital patients, enabling dynamic and realistic interactions that challenge nursing students to develop clinical reasoning skills in a controlled yet flexible MR environment. The AI’s capacity to understand and contextually react to the learner’s' verbal and behavioral inputs simulates authentic patient interactions. A total of 7 nursing students and 3 nursing faculty engaged in the pilot study, which served as a proving ground for the MR training system’s effectiveness. The study involved in-depth analysis, employing performance metrics, and evaluating situational awareness alongside cognitive workload using NASA Task Load Index (TLX) and learner’s thought verbalizations. The primary objective was to create a system that enhances nursing students' competencies and readiness for clinical healthcare practice. This system can potentially elevate the preparedness of new graduate nurses by providing a rich, interactive learning environment that mirrors the complexity of real-life clinical settings.

Integrating Mixed Reality (MR) into nursing education and professional practice has recently captured significant interest as a transformative approach. This paper presents a comprehensive exploration and practical insights into designing and implementing an advanced MR training platform to provide nursing students with immersive experiences across various patient care scenarios. Further enhancing the platform's utility is the incorporation of a unique conversational artificial intelligence (AI) module. This innovation breathes life into digital patients, enabling dynamic and realistic interactions that challenge nursing students to develop clinical reasoning skills in a controlled yet flexible MR environment. The AI's capacity to understand and contextually react to the learner's' verbal and behavioral inputs simulates authentic patient interactions. A total of 7 nursing students and 3 nursing faculty engaged in the pilot study, which served as a proving ground for the MR training system's effectiveness. The study involved in-depth analysis, employing performance metrics, and evaluating situational awareness alongside cognitive workload using NASA Task Load Index (TLX) and learner's thought verbalizations. The primary objective was to create a system that enhances nursing students' competencies and readiness for clinical healthcare practice. This system can potentially elevate the preparedness of new graduate nurses by providing a rich, interactive learning environment that mirrors the complexity of real-life clinical settings.

Delays in administering medication or treatment along with failure to rescue are the most common errors made by novice nurses, which lead to further financial challenges, inefficient use of medical equipment, and human resource challenges. Some nursing students do not receive sufficient training for treating various conditions caused by patient healthcare inequalities and disparities. In this work, we proposed a precise training system integrating augmented reality (AR), artificial intelligence conversation program, and multiple physiological sensors to provide real-time feedback and post training evaluations. The system generates a digital patient that interacts with the learner in the AR environment and gathers and records learner's physiological status, movement, think aloud responses and situation awareness with multiple sensors. The system modifies the training protocol based on the performance and physiological response of the learner. When assessing the conversation between the learner and the patient, the responses from the learner are categorized as functional, redundant, or lacking and the electrocardiogram (ECG), Electrodermal Activity sensor (EDA), eye tracking, and headset position data is gathered to reveal information about the learner's cognition. Specifically, we propose to evaluate learner perception by corelating eye-tracking data to evaluate learner perceptions and corresponding decisions. Based on the learner's behaviors, the system will deliver cues. The designed precise training system is aimed at providing flexible, objective, and personalized training for learners who are licensed nurses or nursing students.