DiaryMate: Understanding User Perceptions and Experience in Human-AI Collaboration for Personal Journaling

Abstract

With their generative capabilities, large language models (LLMs) have transformed the role of technological writing assistants from simple editors to writing collaborators. Such a transition emphasizes the need for understanding user perception and experience, such as balancing user intent and the involvement of LLMs across various writing domains in designing writing assistants. In this study, we delve into the less explored domain of personal writing, focusing on the use of LLMs in introspective activities. Specifcally, we designed DiaryMate, a system that assists users in journal writing with LLM. Through a 10-day feld study (N=24), we observed that participants used the diverse sentences generated by the LLM to refect on their past experiences from multiple perspectives. However, we also observed that they are over-relying on the LLM, often prioritizing its emotional expressions over their own. Drawing from these fndings, we discuss design considerations when leveraging LLMs in a personal writing practice.