LLM-assisted technologies are increasingly used to support cognitive processing and information interpretation, yet their role in aiding memory recall—and how people choose to engage with them—remains underexplored. We studied participants who watched a short robbery video (approximating a one-time eyewitness scenario) and composed recall statements using either a default GPT or a guided GPT prompted with a standardized eyewitness protocol. Results show that default-condition participants who believed they had a clearer understanding of the event were more likely to trust GPT’s output, whereas guided-condition participants showed stronger alignment between subjective clarity and actual recall. Additionally, participants evaluated the legitimacy of the individuals in the incident differently across conditions. Interaction analysis further revealed that default-GPT users spontaneously developed diverse strategies, including building on existing recollections, requesting potentially missing details, and treating GPT as a recall coach. This work shows how GPT–user interplay subconsciously affects beliefs and perceptions of remembered events.
ACM CHI Conference on Human Factors in Computing Systems