The construction sector is increasingly characterized by fragmented information flows, variable operational conditions, resource-intensive processes, and complex decision-making requirements. Artificial intelligence (AI) and robotics provide opportunities to address these challenges through predictive analytics, adaptive decision support, automated execution, and continuous performance monitoring. However, the value of these technologies depends on their integration into a coherent socio-technical framework rather than their isolated deployment. This research develops an integrated conceptual framework connecting AI-based analytics, robotics-enabled execution, digital learning, operational feedback, and sustainability-oriented decision-making in construction. The methodology synthesizes the conceptual and empirical implications of the seven provided studies, particularly their findings concerning machine learning prediction, learning analytics, behavioral modeling, uninterrupted task engagement, active learning, and blended learning. Although the references originate primarily from educational and learning environments, their methodological principles provide transferable foundations for designing intelligent construction systems in which data are converted into predictions, predictions inform decisions, and decisions guide human or robotic action. The proposed framework contains five interconnected layers: data acquisition, AI intelligence, decision orchestration, robotic execution, and feedback-based learning. The analysis indicates that integration can improve operational visibility, resource allocation, adaptive planning, workforce learning, and sustainability monitoring. Nevertheless, limitations associated with contextual transferability, data quality, interoperability, human acceptance, and the absence of construction-specific empirical validation remain significant. The study therefore positions the framework as a research-oriented conceptual architecture that requires field experimentation and longitudinal evaluation before claims of measurable construction performance improvement can be generalized.