MemoryLib: A Human-AI Collaborative Approach to Support Personalized Memory Meaning-Making Process

Human memory is inherently prone to fragmentation, especially when dealing with multimodal digital traces. While existing AI systems struggle to infer personal significance in meaning-making, manual organization often imposes substantial cognitive burden on users. Therefore, our study investigates: how human-AI collaborative systems can support meaning-making of personal memories and how such meaning-making enhances performance on downstream memory tasks. Through co-design workshops, we identified critical challenges in current meaning-making practices and derived actionable design guidelines. We then developed MemoryLib, a mixed-initiative interactive system that enables dynamic organization and interpretation of multimodal memories augmented through human-AI collaborative practices. Findings from a controlled user experiment demonstrated that MemoryLib significantly improved users' abilities to recall, establish relationships of, and reuse memory content. The study contributes a novel human-AI collaboration approach for everyday memory meaning-making and empirical evidence supporting the system's effectiveness in enhancing memory-related tasks.