In the context of global sustainable development strategies and the rise of intelligent construction, it has become increasingly urgent for universities to adapt construction management curricula to meet the demands of this new era. However, prior education-reform-based studies rarely offer a systematic, educator-centered prioritization of knowledge areas, limiting actionable guidance for course sequencing and credit-hour allocation. To address this gap, this study identifies eight essential knowledge categories for construction management education through a comprehensive literature review and a survey of faculty members with strong theoretical and practical experience. An improved Analytic Hierarchy Process (AHP) model, weighted by the Consistency Ratio (CR), is applied to prioritize these areas. Results show that Fundamentals of Construction (18.50%), BIM (18.08%), and AI and Big Data (17.07%) received the highest importance values. These findings emphasize the need for curriculum reorientation to align with intelligent construction. This study contributes to modernizing construction management education and offers practical insights for curriculum development, ensuring alignment with industry trends and technological advancements.

Governments often plan multiple fire stations simultaneously to improve firefighting capabilities, but constructing them within limited resources and time remains a challenge. A spatial decision support model is proposed in this study to determine the prioritized construction sequence of urban fire stations. Two simulation environments were established: one with only existing fire stations and another with both existing and proposed stations as fire service supply points (FSSPs). Response times were simulated using real-time traffic data. The construction urgency of the proposed fire stations was assessed using the construction sequence scoring equation. To validate the model, a case study of Shaoyang City, China, was conducted. A total of 30,968 fire service demand points were gathered, with 20 existing fire stations and 13 proposed fire stations designated as FSSPs. Twenty-five evaluation scenarios were established, resulting in 1,297,025 valid simulation results. The scoring results revealed a maximum score of 119,320, a minimum of 23,420, and an average of 61,412. Based on these results, recommendations for the construction sequence of proposed fire stations in Shaoyang City were made, and the improvements in fire protection levels were calculated. By prioritizing the construction of higher-performance fire stations, this model maximizes resource efficiency and enhances public safety.

The authors would like to make the following corrections in the published paper [1]: (1)Add the Institutional Review Board Statement:Ethical review and approval were waived for this study by Changsha University of Science and Technology as this research falls under social science research conducted through anonymous surveys/interviews