The digital transformation of construction increasingly depends on the integration of artificial intelligence (AI), data-driven decision systems, and robotics to improve operational efficiency, sustainability, and adaptability. However, the scalability of such technologies remains constrained by fragmented workflows, inconsistent data structures, limited predictive capabilities, and insufficient integration between intelligent software and physical construction processes. This research and review paper develops a conceptual framework for scalable AI- and robotics-enabled transformation of sustainable construction by synthesizing insights from the provided literature on machine learning, artificial neural networks, learning analytics, predictive modeling, digital engagement, and intelligent automation. Although the reviewed studies primarily address educational and information-system environments, their methodological principles provide transferable foundations for construction applications, particularly in predictive analytics, performance monitoring, early-warning systems, adaptive decision-making, and human–technology interaction. The proposed framework conceptualizes construction transformation as a continuous intelligence cycle involving data acquisition, AI-based prediction, decision optimization, robotic execution, performance evaluation, and feedback. The analysis indicates that scalability is not determined solely by computational capability but by the ability to connect predictive intelligence with operational processes and human decision-making. The study further identifies challenges related to data quality, interoperability, explainability, workforce adaptation, and implementation complexity. The framework provides a theoretical basis for integrating AI and robotics into sustainable construction while emphasizing measurable operational and environmental outcomes.

Artificial Intelligence, Construction Robotics, Digital Transformation, Sustainable Construction

K. Abhirami and M. K., “Student behavior modeling for an e-learning system offering personalized learning experiences,” Computer Systems Science and Engineering, vol. 40, no. 3, pp. 1127–1144, 2022.

M. Christie and E. D. Graaff, “The philosophical and pedagogical underpinnings of active learning in engineering education,” European Journal of Engineering Education, vol. 42, no. 1, pp. 5–16, 2017.

S. N. Ismail, S. Hamid, M. Ahmad, A. Alaboudi and N. Jhanjhi, “Exploring students engagement towards the learning management system (lms) using learning analytics,” Computer Systems Science and Engineering, vol. 37, no. 1, pp. 73–87, 2021.

Y. Lee, “Effect of uninterrupted time-on-task on students’ success in massive open online courses (MOOCs),” Computers in Human Behavior, vol. 86, no. 1, pp. 174–180, 2018.

Y. Mourdi, M. Sadgal, H. E. Kabtane and B. F. Wafaa, “A machine learning-based methodology to predict learners’ dropout, success or failure in MOOCs,” International Journal of Web Information Systems, vol. 15, no. 5, pp. 489–509, 2019.

S. A. Naser, I. Zaqout, M. A. Ghosh, R. Atallah and E. Alajrami, “Predicting student performance using artificial neural network: In the faculty of engineering and information technology,” International Journal of Hybrid Information Technology, vol. 8, no. 2, pp. 221–228, 2015.

Ramamurthy, K. (2023). AI-Driven Test Automation Frameworks for the Modern Software Quality Engineering. International Journal of Emerging Trends in Computer Science and Information Technology, 4(4), 257-269.

Z. Shen, H. Zhao and Z. Liu, “Impact of blended learning on students’ performance, classification and satisfaction in a practical introductory engineering course,” International Journal of Engineering Education, vol. 37, no. 6, pp. 1730–1742, 2021.

T. S. Yu, Z. Gan, X. Li and C. Yang, “Application of SVR regression in performance prediction and early warning,” Chinese Educational Informational, no. vol. 11, pp. 76–80, 2020.

Ramamurthy, K. (2023). AI-Driven Test Automation Frameworks for the Modern Software Quality Engineering. International Journal of Emerging Trends in Computer Science and Information Technology, 4(4), 257-269.

Geo Philip, Paulson, Robotics-Enabled Sustainable Construction Management: A Socio-Technical Framework for Operational Efficiency, Digital Integration, and Sustainability Performance. Available at SSRN: https://ssrn.com/abstract=6845167 or http://dx.doi.org/10.2139/ssrn.6845167