Challenges of Artificial Intelligence in Curriculum Development at the University Level

Abstract

Artificial Intelligence (AI) is increasingly reshaping curriculum development in higher education as a socio-technical, multidimensional phenomenon rather than a neutral technological tool. This study explores how AI influences ethical governance, pedagogical design, institutional capacity, and contextual dynamics within curriculum systems. Despite growing scholarly attention, existing literature remains fragmented, with ethical, pedagogical, and institutional dimensions often examined in isolation, limiting a comprehensive understanding of their interdependencies. Using a narrative integrative review design, this study synthesizes findings from 55 peer-reviewed studies retrieved from major academic databases, including Scopus, Web of Science, ERIC, IEEE Xplore, and ScienceDirect. The analysis employed systematic coding and thematic synthesis across ethical-policy, pedagogical-design, and technical-institutional domains. Findings reveal that AI introduces systemic ethical risks, including algorithmic bias, transparency deficits, and data governance challenges, while simultaneously transforming pedagogical practices through personalization and adaptive learning. However, these advances also raise concerns regarding epistemic narrowing and the redistribution of human agency in teaching and learning processes. At the institutional level, AI implementation is constrained by infrastructure limitations, governance misalignment, and dependence on external platforms. Contextual factors further demonstrate that AI curriculum implementation is highly cultureand institution-specific, challenging the feasibility of universal models of adoption. The study identifies a persistent fragmentation in existing research and proposes the Integrated Challenge Model for AI Curriculum Development (ICM-AI-CD), which conceptualizes AI integration as a dynamic socio-technical system comprising interdependent ethical-policy, pedagogical-design, and technical-institutional domains. The study concludes that AI in curriculum development represents a systemic transformation of higher education, requiring integrated frameworks that capture its cascading and interdependent effects. This framework provides a foundation for future research, policy development, and institutional planning in AI-enabled curriculum systems.