Abstract

This study aimed to achieve four objectives: (1) to design the system architecture for an intelligent alter-management platform integrating hyperautomation technologies, (2) to develop the intelligent alert-management system based on this integrated hyperautomation framework, (3) to examine the operational outcomes of the system following implementation, and (4) to evaluate user satisfaction with the intelligent alert-management system. The research employed two primary instruments: (1) a system-architecture assessment form and a prototype performance evaluation form completed by five experts, and (2) a user-experience and satisfaction questionnaire administered to 101 administrators, teachers and staff members of Kongfah Wattana Wit School. Data were analyzed using percentage, mean, standard deviation, multiple regression analysis to test influencing factors and t-test/ANOVA for group comparisons at the .05 significance level.

The findings showed that the designed system architecture achieved a high level of suitability across all assessed dimensions, including risk management, system security, and the application of data and technology. The prototype system also demonstrated high performance in installation, effective communication, training and preparedness, system evaluation and improvement, and community participation. Real-world use was rated highly by all stakeholder groups, including administrators, teachers and staff, students, and parents or community members. User satisfaction was consistently high across all components of the Hyperautomation framework, with the highest scores reported for data analytics and adaptive learning models, followed by data collection and transmission efficiency, process design and management, data analysis and processing, software bots, applications and platforms, and understanding of workflow architecture guided by automated playbooks. The system integrated AI, orchestration, RPA, and cloud technologies, supported by operational targets such as alert time not exceeding 10 seconds, system availability of at least 99.5 percent, and detection accuracy of at least 95 percent to facilitate continuous improvement. Overall, the developed Intelligent Disaster Warning Systems (IDWS) model demonstrated strong technical feasibility, practical applicability, and user acceptance, contributing to workload reduction, faster and more accurate incident response, strengthened community collaboration and enhanced proactive school safety management.

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