From AI-Driven Curriculum Design to AI-Enabled Teaching and Assessment: Bridging the Implementation Gap

Artificial Intelligence (AI) has emerged as a transformative force in curriculum development and management, enabling data-driven curriculum design, personalization, and alignment with evolving societal and labor market demands. While substantial scholarly attention has been given to AI-driven curriculum planning, there remains a significant gap between curriculum design and its practical implementation through teaching and assessment. This disconnect limits the full realization of AI’s potential in improving learning outcomes. This paper examines the transition from AI-driven curriculum design to AI-enabled teaching and assessment, with particular emphasis on instructional delivery, assessment practices, and institutional readiness. Using a conceptual and qualitative review of extant literature, the study explores AI tools that support teaching and assessment, identifies pedagogical and organizational challenges, and discusses strategies for effective implementation. The findings underscore the importance of teacher capacity building, pedagogical alignment, ethical governance, and institutional support in bridging the implementation gap. The paper contributes to the discourse on intelligent education systems and provides a foundation for future empirical research on AI-enabled teaching and assessment.