Structuring the Factors for Responsible AI Integration in Construction Education: An ISM-MICMAC Analysis

Generative AI (GenAI) is entering construction education faster than many institutional and pedagogical systems can adapt. Although students already use AI for writing, explanation, and project-related tasks, universities still lack a clear understanding of the conditions required for responsible integration. This study identifies and structurally models the key factors influencing responsible AI integration in construction education using Interpretive Structural Modelling (ISM) and MICMAC analysis. Fourteen factors were identified from the literature and evaluated through a questionnaire completed by eight academic experts in Egypt. Their responses informed the structural self-interaction matrix, initial and final reachability matrices, level partitioning, and MICMAC classification. The findings reveal a nine-level hierarchy in which alignment with professional, ethical, and accreditation standards emerged as the primary driving factor, followed by institutional AI policy, governance, and programme leadership commitment. In contrast, student AI literacy, critical-use competence, guidance, and continuous evaluation emerged as the most dependent conditions. The findings provide a context-specific framework for Egyptian higher education and may offer a useful starting point for similar developing-country settings where AI adoption is advancing faster than institutional policy, although further validation across different national and institutional contexts is required. These relationships should therefore remain provisional.