A Literature Review on the Integration of Artificial Intelligence in Academic Data Systems

Abstract

The increasing complexity of educational data has pushed academic institutions to explore advanced technologies that can enhance data management, analysis, and decision-making processes. Artificial Intelligence (AI) has emerged as a promising solution for improving the efficiency, accuracy, and responsiveness of academic data systems. This literature review examines recent research on the integration of AI in academic data systems, with particular focus on current applications, reported challenges, and proposed strategies for responsible adoption. Using a systematic review approach, peer reviewed studies published between 2019 and 2025 were collected from major academic databases, including IEEE Xplore, Springer, ScienceDirect, ACM Digital Library, MDPI, ResearchGate, and Google Scholar. A total of 38 empirical studies were selected after applying defined inclusion and exclusion criteria. The findings indicate that AI is commonly applied in areas such as predictive analytics, learning analytics, automated reporting, and performance monitoring within academic institutions. However, persistent challenges were identified, including data privacy and security concerns, algorithmic bias, limited technical infrastructure, lack of AI expertise, and the absence of clear institutional governance frameworks. While the literature proposes solutions such as ethical AI frameworks, governance models, and capacity-building initiatives, many challenges remain unresolved due to organizational, financial, and socio-technical factors. The review concludes that successful AI integration in academic data systems requires a balanced, system-wide approach that aligns technological innovation with ethical responsibility, institutional readiness, and long-term sustainability