Memory Printer: Exploring Everyday Reminiscing by Combining Slow Design with Generative AI-based Image Creation
2026-03-13 • Human-Computer Interaction
Human-Computer Interaction
AI summaryⓘ
The authors explore how using slow, hands-on interactions with Generative AI can improve people's control and experience when creating images from memories. They built a tool called Memory Printer, which lets users gradually reveal AI-generated images using a wooden scraper, similar to printmaking. Through a study with 24 participants, the authors found that this approach helps users feel more connected and creative but also raises concerns like false memories, biases in AI, and privacy. Their work highlights both the benefits and challenges of using generative AI for sensitive emotional content.
Generative Artificial IntelligenceTangible InteractionHuman-AI InteractionMemory ReconstructionSilk-screen PrintingText-to-Image GenerationAlgorithmic BiasFalse MemoryData Privacy
Authors
Zhou Fang, Janet Yi-Ching Huang
Abstract
Generative Artificial Intelligence (GAI) offers new opportunities for reconstructing these unrecorded memory scenes, yet existing web-based tools undermine users' sense of agency through disengaging and unpredictable interactions. In this work, we advance three design arguments about how slow, tangible interaction can reshape human-AI relationships by making temporality, embodied agency, and generative processes experientially legible. We instantiate these arguments by presenting Memory Printer, a tangible design that combines silk-screen printing metaphors with text-to-image generation. The design features layered reconstruction that decomposes image generation into incremental steps, a physical wooden scraper enabling embodied control over image revelation, and built-in printing that produces tangible photos. We examine these arguments through a comparative study with 24 participants, exploring how participants engage with, interpret, and respond to this interaction stance. The study surfaces both opportunities -- such as vivid memory evocation, heightened sense of control, and creative exploration -- and critical tensions, including risks of false memory formation, algorithmic bias, and data privacy. Together, these findings articulate important boundaries for deploying generative AI in emotionally sensitive contexts.