Strategic AI adoption for academic promotion systems: integrating GPT and Crawl4AI within knowledge management frameworks in the public sector

Abstract

This study aims to explore the development of a prototype AI-driven knowledge base system, leveraging large language models (LLMs) and Crawl4AI technology, to enhance transparency, efficiency and accountability in academic promotion processes within Iraqi universities. It addresses a critical challenge in integrating unstructured, diverse Web-based knowledge into institutional decision-making systems aligned with environmental, social and governance (ESG) principles and the evolving knowledge economy. Drawing on the technology–organisation–environment (TOE) framework and necessary condition analysis (NCA), this research identifies the key enabling conditions for adopting AI-enhanced knowledge management systems. A structured questionnaire was administered across 51 Iraqi public universities. Using partial least squares structural equation modelling and NCA, the study examines the relative importance and necessity of technological, organisational and environmental dimensions in driving the adoption of the proposed system. The results underscore that organisational factors, particularly internal structures and leadership support, are the most significant drivers of system adoption, followed by technological readiness and environmental enablers. All three dimensions were confirmed as statistically significant and necessary – though not sufficient in isolation – for the successful implementation of AI-powered knowledge systems. The integration of Crawl4AI and GPT-4 in a prototype system demonstrated effective knowledge extraction capabilities and practical potential for informed, data-driven academic decision-making. This study introduces a novel, scalable approach to developing AI-enabled knowledge infrastructures tailored for higher education governance, particularly in resource-constrained contexts. By combining LLMs with Web crawling and retrieval-augmented generation, the system advances ESG-aligned academic practices and fosters responsible AI integration. The findings extend the theoretical discourse on TOE and NCA within AI and knowledge management domains, offering actionable insights for institutions seeking to transition towards more transparent, knowledge-intensive and sustainable decision-making frameworks.