In the era of generative artificial intelligence (GenAI), educators and students face both practical opportunities and pressing challenges for software engineering (SE) education. GenAI powered by large language models is capable of completing complex software development tasks, becoming increasingly integrated into the development process, and is rephaping how students can learn to design, develop, and test software systems. With the advancement of GenAI, it is important to understand how students and educators perceive its role, benefits, and challenges in educational contexts. Also, many existing GenAI tools are adapted from general-purpose models without considering how they align with curriculum goals, cognitive development, or instructional strategies in SE courses. To address these problems, my research works addresses these emerging challenges and opportunities by examining the role of GenAI in programming education from: (1) understanding the perceptions, practices, and expectations of students and instructors regarding genAI; (2) exploring how intelligent systems powered by GenAI can align with pedagogical goals and support active, collaborative and engaging learning environments; (3) investigating how GenAI could reshape SE knowledge areas and developing a validated concept inventory to guide curriculum design and assessment. Through a combination of empirical studies, system development, data analysis, and concept inventory development, my research contributes both insights and practical frameworks to guide the pedagogical integration of GenAI in SE education.