Artificial intelligence-generated content (AIGC) is increasingly prevalent on social media, bringing substantial changes to traditional user-generated content (UGC). Previous studies have examined the role of UGC, but there is a lack of comprehensive understanding of user perception between AIGC and UGC, and how AIGC alters user behavior. This study aims to bridge these gaps by exploring the mediating roles of perceived cognitive effort, content quality, authenticity, and novelty in the relationship between AIGC, UGC, and user engagement. Drawing on the Appraisal-Tendency Framework and Uses and Gratifications Theory, this study explains users' ambivalent attitudes toward AIGC, stemming from their conflicting perceptions. By testing competing hypotheses through a mixed-method approach, we identify underlying mechanisms and explain users' preferences. This study aims to enhance the theoretical understanding of AIGC adoption and provide actionable insights for optimizing content strategies on social media.