The arrival of generative Artificial Intelligence (AI) in educational settings offers a unique opportunity to explore the intersection of human cognitive processes and AI, especially in complex tasks like writing. This study adopts a process-oriented approach to investigate the self-regulated learning (SRL) strategies employed by 21 doctoral and master’s students during a writing task facilitated by generative AI. It aims to identify and analyze the SRL strategies that emerge within the framework of hybrid intelligence, emphasizing the collaboration between human intellect and artificial capabilities. Utilizing a learning analytics methodology, specifically lag sequential analysis (LSA), the research examines process data to reveal the patterns of learners’ interactions with generative AI in writing, shedding light on how learners navigate different SRL strategies. This analysis facilitates an understanding of how learners adaptively manage their writing task with the support of AI tool. By delineating the SRL strategies in AI-assisted writing, this research provides valuable implications for the design of educational technologies and the development of pedagogical interventions aimed at fostering successful human-AI collaboration in various learning environments.