Micro-narratives: A Scalable Method for Eliciting Stories of People's Lived Experience.

Amira Skeggs, Ashish Mehta, Valerie Yap, Seray B Ibrahim, Charla Rhodes, James J Gross, Sean A Munson, Predrag Klasnja, Amy Orben, Petr Slovak
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Abstract

Engaging with people's lived experiences is foundational for HCI research and design. This paper introduces a novel narrative elicitation method to empower people to easily articulate 'micro-narratives' emerging from their lived experiences, irrespective of their writing ability or background. Our approach aims to enable at-scale collection of rich, co-created datasets that highlight target populations' voices with minimal participant burden, while precisely addressing specific research questions. To pilot this idea, and test its feasibility, we: (i) developed an AI-powered prototype, which leverages LLM-chaining to scaffold the cognitive steps necessary for users' narrative articulation; (ii) deployed it in three mixed-methods studies involving over 380 users; and (iii) consulted with established academics as well as C-level staff at (inter)national non-profits to map out potential applications. Both qualitative and quantitative findings show the acceptability and promise of the micro-narrative method, while also identifying the ethical and safeguarding considerations necessary for any at-scale deployments.

微叙事:引出人们生活经历故事的可扩展方法。
参与人们的生活体验是HCI研究和设计的基础。本文介绍了一种新颖的叙事启发方法,使人们能够轻松地从他们的生活经历中表达“微叙事”,而不考虑他们的写作能力或背景。我们的方法旨在实现大规模收集丰富的、共同创建的数据集,以最小的参与者负担突出目标人群的声音,同时精确地解决特定的研究问题。为了试验这一想法并测试其可行性,我们:(i)开发了一个ai驱动的原型,它利用llm链来支撑用户叙事清晰所必需的认知步骤;(ii)在涉及超过380名使用者的三项混合方法研究中部署该系统;(iii)与知名学者和(国际)国内非营利组织的c级员工协商,以规划潜在的应用。定性和定量调查结果都表明微观叙述方法的可接受性和前景,同时也确定了任何大规模部署所需的道德和保障考虑。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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