Abstract

The rapid evolution of science, technology, engineering, and mathematics (STEM) industries necessitates a dynamic approach to curriculum development that ensures educational programs remain aligned with emerging workforce demands. Traditional curriculum frameworks often struggle to adapt in real time, leading to skill gaps between graduates and industry expectations. This paper explores the development of a conceptual framework for artificial intelligence-driven curriculum adaptation, leveraging advanced technologies such as machine learning, natural language processing, and data analytics to enhance educational responsiveness. The study examines the theoretical foundations of STEM curriculum evolution, AI applications in education, and learning theories that support adaptive pedagogical approaches. It then introduces a structured framework for AI-driven curriculum adaptation, detailing its core components, technological enablers, data-driven decision-making processes, and mechanisms for integrating real-time industry feedback. The study further addresses key challenges, including data availability, ethical considerations, resistance from educators, and financial constraints, while proposing solutions to mitigate these barriers. The implications of AI-driven curriculum adaptation for education policy, curriculum designers, and industry partnerships are explored, emphasizing the need for regulatory frameworks, modular course structures, and collaborative stakeholder engagement. The paper also highlights the limitations of the proposed framework, particularly in terms of data bias, infrastructure gaps, and the need for inclusive AI governance. Future research directions focus on advancing AI capabilities for curriculum adaptation, conducting longitudinal impact studies, and fostering interdisciplinary collaborations to enhance the scalability and equity of AI-driven educational models.