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.

A path-method is used as a mechanism in object-Our experiments show that the traversal algorithm of PMG is a very successful tool for aiding the user with the dificult task of querying and updating a large OODB.