Master’s Thesis Presentation • Human-Computer Interaction • Memolet: Reifying the Reuse of User-AI Conversational Memories

Please note: This master’s thesis presentation will take place in DC 2310.

Hen-Chen Yen, Master’s candidate
David R. Cheriton School of Computer Science

Supervisor: Professor Jian Zhao

As users engage more frequently with AI conversational agents, conversations may exceed their “memory” capacity, leading to failures in correctly leveraging certain memories for better responses. Therefore, users have to revisit related memories and re-provide these memories to the agents, ensuring that the generation refers to the accurate memories. However, the process of finding past memories to reuse is cumbersome, requiring users to retrieve related information across various conversations and articulate their intentions for reusing these memories to the AI.

To support users in recalling and reusing relevant memories, we introduce Memolet, an interactive object that reifies memory reuse. Users can directly manipulate Memolet to specify which memories to reuse and how to use them. We developed a system demonstrating Memolet’s interaction across various memory reuse stages, including memory extraction, organization, prompt articulation, and generation refinement. Through a user study, we gained insights into users’ experiences with Memolet for memory reuse in AI conversations. The study validates the system’s usefulness and provides design implications for future systems that support user-AI conversational memory reusing.