Generative AI-Assisted Curriculum Design and Competency Mapping for Outcome-Based Education in Engineering and Medical Institutions

Abstract

Outcome-Based Education (OBE) has emerged as a transformative educational framework that emphasizes measurable learning outcomes, competency attainment, and continuous curriculum improvement across professional education systems. Engineering and medical institutions increasingly require dynamic curriculum models capable of adapting to rapidly evolving industrial technologies, healthcare practices, accreditation requirements, and interdisciplinary competencies. Conventional curriculum design processes remain highly manual, time-consuming, and often struggle to maintain alignment among program outcomes, course outcomes, graduate attributes, competency frameworks, assessment strategies, and stakeholder expectations. Recent advances in Generative Artificial Intelligence offer unprecedented opportunities to automate curriculum planning, generate learning resources, recommend competency-aligned instructional strategies, identify curriculum gaps, and support evidence-based academic decision-making. This paper presents a comprehensive academic investigation into the integration of Generative AI for curriculum design and competency mapping within Outcome-Based Education environments. The study explores AI-driven curriculum intelligence, automated outcome alignment, competency analytics, personalized learning pathways, and continuous quality enhancement mechanisms suitable for engineering and medical education. Furthermore, the paper discusses implementation strategies, evaluation metrics, ethical considerations, institutional readiness, and future research opportunities for sustainable AI-enabled academic ecosystems that enhance educational quality while preserving academic integrity and accreditation standards.