Analyzing Factors Affecting the Transformation of the University Policy-Making System with an Artificial Intelligence Development Approach in Golestan Province Universities

Abstract

The present study aimed to analyze and explain the factors influencing the transformation of the university policy-making system based on artificial intelligence development and to present an empirically grounded conceptual model in Golestan province universities. This study was applied in purpose and qualitative in nature and was conducted using the grounded theory approach. The research population consisted of academic experts in university policymaking and emerging technologies in Golestan province universities. Participants were selected through purposive and theoretical sampling, and a total of 11 experts, including university presidents, educational and research deputies, planning managers, and faculty members familiar with emerging technologies, participated in the study. Data were collected through in-depth semi-structured interviews and analyzed concurrently with data collection. The data analysis process followed three stages: open coding, axial coding, and selective coding. To enhance the trustworthiness of the findings, repeated data review, conceptual consistency checks, and direct linkage between codes and raw interview data were ensured. Data analysis resulted in the identification of 75 open codes, which were categorized into two axial categories: “data-driven decision-making” (58.6%) and “increasing managerial transparency and accuracy” (41.4%). These two categories collectively formed the central phenomenon of “transformation in the university policy-making system.” The findings indicated that artificial intelligence enables the transition from traditional, intuition-based policymaking toward evidence-based policymaking by facilitating predictive analytics, improving evaluation accuracy, enhancing organizational justice, and reducing reliance on subjective judgments. Furthermore, a reinforcing relationship was identified between data-driven decision-making and managerial transparency, whereby increased reliance on data improves transparency and accountability, and greater transparency further strengthens data-oriented decision processes. The transformation of the university policy-making system through artificial intelligence represents a fundamental paradigm shift from static, subjective, and traditional policymaking toward dynamic, evidence-based, and forward-looking governance. This transformation enhances decision quality, promotes fairness and transparency, strengthens coordination among organizational units, and improves overall institutional effectiveness. The findings emphasize that the development of data infrastructure, managerial analytical capabilities, and regulatory mechanisms are critical prerequisites for achieving sustainable transformation. Implementing these elements can facilitate the emergence of an intelligent, adaptive, and effective university policy-making system capable of responding to complex and evolving organizational and societal demands.