AI-Enabled Framework for Program and Course Design in Higher Education

Background: Artificial intelligence is reshaping higher education, yet most institutions still rely on ad-hoc experiments rather than a holistic, evidence-based strategy for curriculum innovation. Purpose: This study develops and proposes a comprehensive framework that helps universities integrate AI ethically and systematically into program and course design, ensuring alignment with learner needs, labour-market skills, and quality standards. Methods: Employing an integrative secondary research design, we conducted a structured review of peer-reviewed articles, policy documents, and institutional case studies published between 2018 and 2025. Forty high-quality sources passed rigorous screening for relevance, credibility, and methodological soundness. Extracted data were coded thematically and synthesised into recurring practices, enablers, challenges, and ethical considerations, which collectively informed framework construction. Results: AI adoption in curriculum design is global but uneven; leading institutions report gains in student retention, skills alignment, and design efficiency, while lagging peers cite insufficient faculty training, unclear policies, and ethical concerns. Synthesised findings yielded a three-layer framework: (1) program-level guidance that uses AI analytics for outcome formulation, skills mapping, and curriculum sequencing; (2) course-level guidance that positions AI as a co-designer for content generation, adaptive assessment, and personalised feedback; and (3) cross-cutting foundations covering governance, responsible AI use, quality assurance, capacity building, and sustainability. Conclusions: The proposed framework offers a scalable pathway for data-driven, learner-centred, and ethically responsible curriculum innovation. Its adoption can enhance institutional agility and graduate employability, though empirical validation across diverse contexts remains a priority for future research.