Integrating Large Language Models (LLMs) into educational practice enables personalized learning by accommodating diverse learner behaviors. This study explored diverse learner profiles within a multi-agent, LLM-empowered learning environment. Data was collected from 312 undergraduate students at a university in China as they participated in a six-module course. Based on hierarchical cluster analyses of system profiles and student-AI interactive dialogues, we found that students exhibit varied behavioral, cognitive, and emotional engagement tendencies. This analysis allowed us to identify two types of dropouts (early dropouts and stagnating interactors) and three completer profiles (active questioners, responsive navigators, and lurkers). The results showed that high levels of interaction do not always equate to productive learning and vice versa. Prior knowledge significantly influenced interaction patterns and short-term learning benefits. Further analysis of the human-AI dialogues revealed that some students actively engaged in knowledge construction, while others displayed a high frequency of regulatory behaviors. Notably, both groups of students achieved comparable learning gains, demonstrating the effectiveness of the multi-agent learning environment in supporting personalized learning. These results underscore the complex and multifaceted nature of engagement in human-AI collaborative learning and provide practical implications for the design of adaptive educational systems.