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Abstract Robotic automation in construction has created the need for new competencies that will enable the workforce to engage with robots safely and effectively. However, differing perceptions between industry professionals and academia make aligning academic programs with industry needs challenging. This study evaluates these perceptions to guide the design of HRC training programs. A three-round Delphi study was conducted separately with panels of industry professionals and academic experts to assess their views on HRC competencies in construction. The findings revealed that both panels identified human–robot interfaces, HRC safety and standards, robot control systems, and construction robot applications as the top five HRC knowledge areas. Industry professionals also emphasized task planning knowledge, while academic experts focused on HRC ethics. Key HRC skills include effective communication, safety management, technical proficiency, and compliance with regulations and standards, with industry professionals prioritizing proficiency in task planning and academics emphasizing human–robot interface proficiency. Both expert panels prioritized teamwork, continuous learning, problem-solving, communication, and adaptability as top-rated HRC abilities. This study contributes to knowledge by defining key HRC competencies and identifying differences in priorities between industry and academia. These insights can guide the development of academic curricula that better align with industry needs, supporting the creation of training programs that equip the workforce with the competencies required for safe and effective robotic collaboration. The study also promotes collaboration between industry and academia, fostering innovation in HRC and robotics in construction. Future research directions are proposed to explore innovative training methods to equip the future workforce with HRC competencies. 

Active back-support exoskeleton has gained recognition as a potential solution to mitigate work- related musculoskeletal disorders. However, their utilization in the construction industry can introduce unintended consequences, such as increased fall hazards. This study examines the implications of using active back-support exoskeleton on fall risk during construction framing tasks, incorporating wearable pressure insoles for data collection. Two experimental conditions were established, one involving the simulation of construction framing tasks with exoskeleton and the other without exoskeleton. These tasks encompassed six subtasks: measuring, assembly, nailing, lifting, moving, and installation. Foot plantar pressure distribution was recorded across various spatial foot regions, including the arch, toe, metatarsal, and heel. Statistical analysis, employing a paired t-test on peak plantar pressure data, revealed that the use of active back-support exoskeleton significantly increased fall risks in at least one of the foot regions for all subtasks, except for the assembly subtask. These findings provide valuable insights for construction stakeholders when making decisions regarding the adoption of active back-support exoskeleton in the industry. Moreover, they inform exoskeleton manufacturers of the need to develop adaptive and customized exoskeleton solutions tailored to the unique demands of construction sites. 

With rising interest in innovative construction methodologies, global construction companies are actively exploring emerging sensing technologies and employing data analytics techniques to draw insights and improve their operations. While numerous educational disciplines employ Block-based Programming Interfaces to enhance domain-specific data-related inquiry and visualization skills, the construction sector has yet to fully explore this practical approach. Introducing block interfaces in construction education may overwhelm newcomers with excessive cognitive load. Past research has primarily relied on subjective measures, overlooking objective indicators for assessing cognitive responses to block interfaces’ interaction elements. This study evaluates the cognitive load induced using InerSens, a Block Programming Interface designed to address authentic construction challenges in ergonomic risk assessment. Electroencephalography is utilized to measure cognitive load, and the results are compared to those of a traditional tool, Excel. Theta Power Spectral Density in the frontal brain region, an indicator of cognitive load, demonstrates that in four out of six tasks, InerSens incurs lower cognitive load than Excel. The findings of this study underscore the potential of InerSens as a viable tool in managing cognitive load efficiency, paving the way for more effective and streamlined sensor data analytics learning experiences for future construction professionals.