Social Risk Management of Artificial Intelligence in Higher Education: A Conceptual Framework

With the rapid uptake of artificial intelligence (AI) in higher education, academic risks need to be viewed as not only technical issues but as socio-institutional harms that impact equity, trust, and accountability. The methodology used for this study is a conceptual literature review and qualitative synthesis of existing academic research. This study tackles the problem existing in the literature, which is unstructured definitions, by synthesizing the literature to create a complete conceptual framework for Social Risk Management (SRM). The proposed framework places SRM in a unique role, serving as the institutional interface between the introduction of AI and social outcomes, with the role of a mediator and moderator between the raw technical inputs and the negative effects of AI on society. There are four main operational pillars of SRM identified in the synthesis. This study also adds the competency of SRM as a core competency of governance, not only for its ethical dimension but also for being a proactive competency. Moreover, it offers a clear research program for future empirical studies, detailing potential ways to measure SRM and to investigate its differences in various application areas of universities, such as admissions, financial aid, tutoring, and even assessment.