User Experience Research (UXR) is currently undergoing a transition from traditional usability testing towards design-led and data-driven approaches, yet it faces an identity crisis due to a lack of methodological grounding in UXR and time-intensive methodologies which often lag behind product decision cycles. To address this, the UXR Point of View (PoV) framework formalises the UXR process by transitioning from raw data collection to forming an evidence-based PoV which drives strategic product impact. Furthermore, the use of GenAI in UXR has been investigated, but researchers often face increased work intensity when using GenAI, attributed to time spent on prompt engineering, data cleaning, and verification of AI outputs. This paper proposes and evaluates a formalised methodology for leveraging GenAI, specifically Google's NotebookLM, to augment the UXR PoV process. The methodology consists of five prompts across four stages: (1) leveraging the framework, (2) establishing roadmaps, (3) applying best-practices, and (4) crafting PoV narratives; and was tested on eleven UXR papers. Results showed that by using the proposed methodology, NotebookLM successfully leveraged the UXR PoV framework across all stages of PoV creation. These findings demonstrate that NotebookLM can serve as an effective collaborative partner in UXR, so long as it is provided with sufficient context and specific prompting.

翻译：用户研究（UXR）当前正经历从传统可用性测试向设计主导与数据驱动方法的转型，但因缺乏方法论基础且其时间密集型流程常滞后于产品决策周期，正面临身份认同危机。为此，用户研究视角框架通过将原始数据收集转化为驱动战略产品影响力的循证型视角，对用户研究流程进行了形式化规范。此外，生成式人工智能在用户研究中的应用虽已有探索，但研究者常因投入大量时间进行提示工程、数据清洗及AI输出验证而面临工作强度增大的问题。本文提出并评估了一种形式化方法，通过利用生成式AI（具体为谷歌NotebookLM）增强用户研究视角流程。该方法包含四个阶段的五个提示词：(1)框架应用；(2)路线图制定；(3)最佳实践实施；(4)视角叙事构建；并在十一篇用户研究论文中验证。结果表明，通过采用该形式化方法，NotebookLM在视角创建各阶段均成功运用了用户研究视角框架。这些发现证明，只要提供充分的上下文与特定提示，NotebookLM即可作为用户研究中有效的协作伙伴。