Skeggs Amira, Mehta Ashish, Yap Valerie, Ibrahim Seray B, Rhodes Charla, Gross James J, Munson Sean A, Klasnja Predrag, Orben Amy, Slovak Petr
MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, United Kingdom.
Department of Psychology, Stanford University, Stanford, California, USA.
Proc SIGCHI Conf Hum Factor Comput Syst. 2025 Apr-May;2025. doi: 10.1145/3706598.3713999. Epub 2025 Apr 25.
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.
关注人们的生活经历是人机交互（HCI）研究与设计的基础。本文介绍了一种新颖的叙事引出方法，使人们能够轻松地阐述源自其生活经历的“微叙事”，无论其写作能力或背景如何。我们的方法旨在大规模收集丰富的、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。为了试行这个想法并测试其可行性，我们：（i）开发了一个由人工智能驱动的原型，该原型利用大语言模型（LLM）链接来搭建用户叙事表达所需的认知步骤；（ii）在三项涉及380多名用户的混合方法研究中进行部署；（iii）与知名学者以及（国际）非营利组织的高层管理人员进行磋商，以规划潜在的应用。定性和定量研究结果都表明了微叙事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伦理和保障考量。
