This study explores adult learners’ perceptions of simulated interviews with artificial intelligence (AI) for understanding professional roles in the field of adult education as well as better understanding the capabilities and limitations of using AI as a learning tool. A total of forty-two adult learners across four sessions of the same asynchronous online graduate-level course engaged in the AI-driven simulation interview activity, followed by reflective discussions. Both sentiment analysis and thematic analysis of the data revealed predominantly positive attitudes toward using AI for learning through simulation interviews, emphasizing its efficiency, accessibility, and preparatory value in professional learning. While participants valued AI for its flexibility and immediate feedback, concerns were raised regarding the depth and authenticity of AI interactions, underlining its limitations as a sole learning tool. The findings highlight the potential of using AI to enhance learning, while indicating the critical need for integration with human interactions to maximize its educational benefits.

The integration of artificial intelligence (AI) into construction education is transforming how future professionals approach estimation tasks. This study examines the role of Togal AI—an AI‐powered estimation tool—alongside the industry‐preferred Bluebeam Revu 20 in undergraduate construction education. Through a structured experiment with 60 students, we tracked flooring area estimations for a school building, collecting both quantitative performance metrics and qualitative survey responses. Key findings show Togal AI accelerated task completion by 51.3%, improved measurement accuracy by 20.4%, enhanced team coordination by 28.4% and sped up change order processing by 75.7%, while boosting confidence by 55.2%. However, semi‐structured interviews revealed concerns that over‐reliance on automation might hinder critical thinking. This highlights the importance of curricular frameworks positioning AI as an educational support tool rather than a replacement for essential competencies. This study offers practical strategies for integrating AI tools into estimation education. While Togal AI automates measurement, freeing cognitive capacity for large, complex projects, its technical limitations, oversimplified markups and risk of over‐reliance on AI underscore the need for curricula that balance AI efficiency with manual estimation skills. These findings inform the modernization of academic curricula, ensuring AI enhances rather than replaces essential competencies in construction education.

Despite the widespread adoption of BIM, information exchange across disciplines remains hindered by heterogeneous structures at the tabular data level, particularly when integrating data across multiple discipline-specific models. Manual mapping, rigid templates, or one-off programming scripts are labor-intensive and difficult to scale, limiting automated querying, cross-model aggregation, and schedule-level analytics. This study proposes a lightweight, workflow-driven approach for semantic normalization and cross-model integration of BIM schedule data, with optional script-supported workflow configuration used only to assist the configuration of deterministic, rule-guided mapping logic, rather than serving as a core analytical method. By introducing a customizable subcategory layer, the workflow enables fine-grained semantic alignment and efficient normalization across diverse schedule datasets, implemented through lightweight Python scripting and rule-guided semantic matching used solely as a supporting mechanism for deterministic field mapping. Using structural, architectural, and HVAC models, we demonstrate a stepwise process including data cleaning, hierarchical classification, consistency checking, batch analytics, and automated computation of cross-model metrics such as opening-to-wall ratios. Sample-based validation confirms the workflow’s reliability, achieving semantic mapping agreement rates above 95% and reducing manual processing time by more than 85%. The workflow is readily extensible to other disciplines and modeling conventions, supporting high-throughput data integration for tasks such as design coordination, semantic alignment, RFI reduction, accelerated design reviews, and data-driven decision making. Overall, rather than introducing a new algorithm, the contribution of this work lies in formalizing a reusable, schedule-level workflow abstraction that enables consistent semantic alignment and automated cross-model aggregation without relying on rigid ontologies or training-intensive learning-based models. Any optional tooling used during workflow configuration is auxiliary and does not constitute a standalone learning-based method requiring model training or performance benchmarking. This provides a reusable methodological foundation for scalable, schedule-level BIM data integration and cross-model analytics.

Rapid adoption of digital surveying technologies in construction has highlighted the need for engineering education to equip students with technological competency as well as higher-order problem-solving skills. This experiment explores undergraduate students’ acceptance of emerging surveying technologies and their perceived learning results within a constructivist framework of experiential learning. Thirty-six students in a required construction surveying class interacted with traditional and advanced technologies such as total stations, terrestrial laser scanning, drones, and mobile LiDAR through structured, semi-structured, and unstructured lab activities. Data were gathered based on two post-course surveys: a technology acceptance survey grounded in Unified Theory of Acceptance and Use of Technology (UTAUT) and a self-perceived cognitive learning outcome survey through Bloom’s Taxonomy. Qualitative analysis along with quantitative analysis indicated a gap between technology acceptance and perceived learning gains. Laser scanner had the greatest acceptance scores followed by other advanced tools. Total station (widespread in hands-on lab activities) was perceived to have been most influential in terms of enhancing learning. Lower-order skills were strengthened in structured labs, while higher-order thinking emerged more unevenly in open-ended labs. These findings underscore that the mode of student engagement with technology matters more for learning than the sophistication of the tools themselves. By embedding UTAUT and Bloom’s Taxonomy in an authentic learning environment, this experiment provides engineering educators a mechanism to assess technology-enhanced learning and identifies strategies to facilitate higher-order skills aligned with industry needs.

Single-use disposable face masks, largely proliferated by the COVID-19 pandemic, have created substantial challenges in global waste management due to their persistent accumulation. Composed predominantly of non-biodegradable polypropylene (PP), these masks significantly contribute to plastic pollution. This paper explores an innovative approach to mitigate the environmental impact of discarded face masks by repurposing them as an additive in hot mix asphalt (HMA). Through incorporating shredded face masks into HMA, this study aims to enhance asphalt pavements' mechanical performance, particularly in rutting resistance, while offering an economically viable solution. A life cycle cost analysis (LCCA) evaluates the economic feasibility of mask-modified asphalt by comparing its long-term costs to conventional pavements. Findings indicate that integrating face masks into asphalt improves pavement durability and delivers substantial long-term cost savings through reduced maintenance. This research tackles the pressing issue of plastic pollution, proposing a sustainable, cost-effective solution for road construction that contributes to circular economy goals in the infrastructure sector.