In the field of Education, Educational Technology and Digital Pedagogy, rapid emergence of generative AI chatbots challenges traditional essay and exam assessment validity in higher education. This empirical investigation systematically examines Generative AI Integration into University Curricula: Academic Integrity, Assessment Redesign, and Student Perceptions through a multi-stage experimental methodology and rigorous quantitative analytical framework. Utilizing institutional multi-faculty survey of 850 faculty members and assessment redesign case studies incorporating authentic performance tasks, data were gathered across multiple operational cycles and validated against established international benchmarks. The statistical and computational results reveal that shifting to process-oriented, oral, and authentic problem-solving assessments reduced academic integrity violations while fostering critical AI literacy. Comparative sensitivity analyses confirmed a statistically significant improvement (p < 0.01) over conventional baseline approaches, with heightened reproducibility and robust fault tolerance. These comprehensive findings provide actionable theoretical insights and practical implementation guidelines for university academic senates, teaching centers, and accreditation quality assurance bodies. Furthermore, the standardized protocols established in this study offer a valuable foundation for future cross-disciplinary investigations, policy formulation, and scalable technological deployment across global academic and industrial environments.