Generative artificial intelligence (GAI) has the potential to reshape workflows across the Architecture, Engineering, and Construction (AEC) sector. While previous research has offered valuable technical demonstrations and conceptual analyses, empirical evidence quantifying GAI-related impacts across AEC occupations and systematic assessment of adoption readiness remain limited. This study develops a domain-specific socio-technical evaluation framework that provides occupational-level analysis of technical capabilities, social risks, and adoption barriers across thirteen O*NET-defined AEC occupations. Data were collected through a six-month survey of 162 AEC professionals, complemented by six expert interviews and a systematic literature review. The findings reveal: (1) Technical Capability , measured using exposure scores ranging from −1 (low applicability) to +1 (high applicability), shows moderate applicability in design-oriented roles (e.g., architectural drafters: 0.16) and minimal alignment for site-based and manual activities (e.g., construction laborers: −0.89). (2) Social Risks , assessed on a 0–1 scale of concern, identify hallucinations (0.71), data privacy (0.70), and intellectual property issues (0.69) as critical concerns. (3) Socio-Technical Adoption highlights limited technical expertise (26.0%) and uncertain return on investment (16.8%) as primary barriers, while respondents emphasized the need for usage guidelines and standards (29.6%) and targeted training (29.2%) to facilitate responsible integration. Based on these findings, the study outlines strategic priorities for responsible GAI deployment, including AEC-specific standards, targeted workforce training, human-in-the-loop validation mechanisms, and domain-tailored digital infrastructure. The framework and empirical evidence provide a foundation for researchers, practitioners, and policymakers seeking to guide the safe and effective integration of GAI into AEC workflows. • A socio-technical evaluation framework tailored to AEC GAI integration. • Task- and occupation-level analysis of capability readiness across thirteen O*NET roles. • Quantitative assessment of risks, adoption barriers, and organizational support needs. • Evidence-based recommendations on guidelines, training, human oversight, and digital infrastructure.

Predicting separation errors in the daily tasks of air traffic controllers (ATCOs) is essential for the timely implementation of mitigation strategies before performance declines and the prevention of loss of separation and aircraft collisions. However, three challenges impede accurate separation errors forecasting: 1) compounding relationships between many human factors and control processes require sufficient operation process data to capture how separation errors occur and propagate within controller-in-the-loop processes; 2) previous human factor measurement approaches are disruptive to controllers’ daily operations because they use invasive sensors, such as electroencephalography (EEG) and electrocardiography (ECG), 3) errors accumulated in using the tasks and human behaviors for estimating system dynamics challenge accurate separation error predictions with sufficient leading time for proactive control actions. This study proposed a separation error prediction framework with a long leading time (>50 s) to address the above challenges, including 1) a multi-factorial model that characterizes the inter-relationships between task complexity, behavioral activity, cognitive load, and operational performance; 2) a multimodal data analytics approach to non-intrusively extract the task features (i.e., traffic density) from high-fidelity simulation systems and visual behavioral features (i.e., head pose, eyelid movements, and facial expressions) from ATCOs’ facial videos; 3) an encoder-decoder Long Short-Term Memory (LSTM) network to predict long-time-ahead separation errors by integrating multimodal features for reducing accumulated errors. A user study with six experienced ATCOs tested the proposed framework using the Phoenix Terminal Radar Approach Control (TRACON) simulator. The authors evaluated the model performance through two types of metrics: 1) point-level metrics, including precision, recall, and F1-score, and 2) sequence-level metrics, including alignment accuracy and sequence similarity. The results showed that 1) the model using the task and visual behavioral features significantly improved the prediction performance compared to the model using one single feature (eyelid movements), with an improvement of up to 26.95% in alignment accuracy for 10s-ahead prediction; 2) the model that combined task and visual behavioral features had a higher or comparable performance to models with different hybrid features, achieving an alignment accuracy of 82.38% for 50s-ahead error prediction; and (3) the proposed method outperformed three baseline models – Convolutional Neural Network (CNN), Gated Recurrent Unit (GRU), and classic LSTM – by 8.21%, 3.47%, and 3.14% in alignment accuracy, respectively, for predicting 50s-ahead separation errors. These results suggest that the proposed model can effectively predict separation errors in air traffic control.