Recent advancements in generative artificial intelligence (GenAI) have drawn significant attention from educators and researchers. However, its effects on learners' programming performance, self-efficacy and learning processes remain inconclusive, while the mechanisms underlying its efficiency-enhancing potential are underexplored. This study addresses these gaps through a quasi-experiment comparing an experimental group using GenAI for self-directed programming learning with a control group relying on alternative tools. Additionally, the experimental group was divided into high- and low-performance subgroups to examine the relationship between learning behaviour patterns and academic outcomes using process mining techniques. The findings reveal that (a) GenAI demonstrates strong potential to enhance learning outcomes and self-efficacy but negatively affects long-term knowledge transfer; (b) excessive reliance on GenAI and cognitive outsourcing impede effective knowledge acquisition; (c) high-performing learners exhibit greater epistemic agency, actively critiquing and engaging with AI-generated content to construct knowledge proactively. This study underscores the risks of over-reliance on GenAI and the detrimental effects of cognitive offloading, highlighting the critical role of cognitive engagement and epistemic agency in fostering hybrid intelligence. It provides empirical and theoretical insights to inform the design of GenAI-supported programming education strategies and interventions.