Skip to main content

Generative AI in developing User Experience Research Point of View: A NotebookLM case study.

Giff, M., Giff, S. and Dogan, H., 2026. Generative AI in developing User Experience Research Point of View: A NotebookLM case study. In: CHI 2026, 13-17 April 2026, Barcelona.

Full text available as:

[thumbnail of 16 PoV_Giff_Current_Paper.pdf]
Preview
PDF
16 PoV_Giff_Current_Paper.pdf - Accepted Version

314kB

Official URL: https://chi2026.acm.org/workshops/accepted/

DOI: 10.1145/3772318

Abstract

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 11 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.

Item Type:Conference or Workshop Item (Paper)
Uncontrolled Keywords:Generative AI; User Experience Research (UXR); Point of View (PoV)
Group:Faculty of Media, Science and Technology
ID Code:41989
Deposited By: Symplectic RT2
Deposited On:17 Jun 2026 11:38
Last Modified:17 Jun 2026 11:38

Downloads

Downloads per month over past year

More statistics for this item...
Repository Staff Only -