Developing a Culturally Grounded, AI-Augmented UX Research Point of View (POV): An Exemplar Case Study from Telemedicine Dementia Care
- URL: http://arxiv.org/abs/2605.31147v1
- Date: Fri, 29 May 2026 10:56:57 GMT
- Title: Developing a Culturally Grounded, AI-Augmented UX Research Point of View (POV): An Exemplar Case Study from Telemedicine Dementia Care
- Abstract summary: This paper presents an exemplar case study demonstrating how a culturally grounded, AI-augmented UXR POV was developed to inform TeleDeCa.<n>Generative AI (GenAI) is integrated across the UXR POV framework as a bounded research collaborator.<n>The contribution of this paper lies in the extraction of reusable Play Cards and a Play that extend the UXR POV Playbook.
- Score: 1.499000509293768
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: User Experience Research (UXR) Points of View (POVs) distil complex and often fragmented research evidence into actionable perspectives that guide how teams interpret user needs, frame design decisions, and align stakeholders. Although POVs are widely used in industry practice, there are few published examples that explicitly document how POVs are constructed, particularly in culturally sensitive and low-resource contexts. This paper presents an exemplar case study demonstrating how a culturally grounded, AI-augmented UXR POV was developed to inform TeleDeCa, a telemedicine dementia care framework for family caregivers in Nigeria. Building on the UXR POV Playbook and pyramid framework, we illustrate how mixed-methods research, hypothesis generation, and ontology-based modelling can be combined to form a defensible POV without requiring a fully finalised system or validated outcomes. Generative AI (GenAI) is integrated across the UXR POV framework as a bounded research collaborator, supporting synthesis, hypothesis exploration, and narrative construction while preserving human judgment, ethical accountability, and cultural sensitivity. The contribution of this paper lies in the extraction of reusable Play Cards and a Play that extend the UXR POV Playbook and serve as exemplar material for the CHI 2026 workshop on developing AI-powered UXR POVs.
Related papers
- TVIR: Building Deep Research Agents Towards Text--Visual Interleaved Report Generation [63.89818340842003]
TVIR (Text--Visual Interleaved Report Generation) is a benchmark of 100 expert-curated multimodal deep research tasks.<n>TVIR-Agent is a hierarchical multi-agent framework that serves as a strong baseline for constructing outlines and retrieving images.<n>We develop a dual-path evaluation framework that combines Textual Assessment and Visual Assessment.
arXiv Detail & Related papers (2026-06-01T14:35:14Z) - From Evidence to Design: Developing an AI-Augmented UX Research Point of View for Digital Wellbeing in Emergency and Public Safety Contexts [5.241443752301431]
This paper investigates how User Experience Research (UXR) methods can be combined with AI-supported analysis to develop clearer design direction for digital wellbeing interventions.<n>This study applied an AI-supported literature analysis process to identify recurring psychological, behavioural, and design patterns.<n>Findings show that effective wellbeing systems for Emergency and Public Safety Personnel must minimise cognitive effort, adapt to operational context, and prioritise psychological safety.
arXiv Detail & Related papers (2026-05-29T10:53:57Z) - Developing an AI-Powered UX Research Point of View for Digital Health in A Regulatory Context: An Exemplar Case from MSM and Transgender HIV Care in Nigeria [5.025015307096222]
This paper introduces a Generative AI-augmented UXR methodology to guide the design of psychologically safe, low-cognitive-load digital health interventions.<n>The methodology is operationalised through a four-stage UXR process encompassing AI-supported hypothesis generation, foundational planning, and insight generation.<n>The output is a set of ten theory-informed UXR Play Cards translating psychological insight and empirical evidence into actionable design guidance.
arXiv Detail & Related papers (2026-05-29T10:47:44Z) - UXR PoV for Neuroinclusive Emotion Regulation [3.6279953233728084]
This paper introduces a Generative AI-augmented UXR methodology to support the design of emotionally intelligent digital ER interventions for adults with ADHD.<n>The approach integrates empirical evidence with established psychological frameworks Dialectical Behaviour Therapy (DBT), Self-Determination Theory (SDT), and the COM-B behavioural model.<n>The methodology is operationalized through a four-stage UXR process encompassing AI-supported hypothesis generation, foundational planning, insight generation via Building Blocks, and the construction of stakeholder-specific PoV narratives.
arXiv Detail & Related papers (2026-05-29T10:40:48Z) - MITRA: An AI Assistant for Knowledge Retrieval in Physics Collaborations [0.0]
Large-scale scientific collaborations produce a vast and ever-growing corpus of internal documentation.<n>We present a prototype of MITRA, a Retrieval-Augmented Generation (RAG) based system, designed to answer specific, context-aware questions about physics analyses.<n>MITRA employs a novel, automated pipeline using Selenium for document retrieval from internal databases and Optical Character Recognition (OCR) with layout parsing for high-fidelity text extraction.
arXiv Detail & Related papers (2026-03-10T15:28:35Z) - CoCoNUTS: Concentrating on Content while Neglecting Uninformative Textual Styles for AI-Generated Peer Review Detection [60.52240468810558]
We introduce CoCoNUTS, a content-oriented benchmark built upon a fine-grained dataset of AI-generated peer reviews.<n>We also develop CoCoDet, an AI review detector via a multi-task learning framework, to achieve more accurate and robust detection of AI involvement in review content.
arXiv Detail & Related papers (2025-08-28T06:03:11Z) - Advancing AI Research Assistants with Expert-Involved Learning [84.30323604785646]
Large language models (LLMs) and large multimodal models (LMMs) promise to accelerate biomedical discovery, yet their reliability remains unclear.<n>We introduce ARIEL (AI Research Assistant for Expert-in-the-Loop Learning), an open-source evaluation and optimization framework.<n>We find that state-of-the-art models generate fluent but incomplete summaries, whereas LMMs struggle with detailed visual reasoning.
arXiv Detail & Related papers (2025-05-03T14:21:48Z) - STRICTA: Structured Reasoning in Critical Text Assessment for Peer Review and Beyond [68.47402386668846]
We introduce Structured Reasoning In Critical Text Assessment (STRICTA) to model text assessment as an explicit, step-wise reasoning process.<n>STRICTA breaks down the assessment into a graph of interconnected reasoning steps drawing on causality theory.<n>We apply STRICTA to a dataset of over 4000 reasoning steps from roughly 40 biomedical experts on more than 20 papers.
arXiv Detail & Related papers (2024-09-09T06:55:37Z) - XrayGPT: Chest Radiographs Summarization using Medical Vision-Language Models [72.8965643836841]
We introduce XrayGPT, a novel conversational medical vision-language model.<n>It can analyze and answer open-ended questions about chest radiographs.<n>We generate 217k interactive and high-quality summaries from free-text radiology reports.
arXiv Detail & Related papers (2023-06-13T17:59:59Z) - Learning Contextualized Document Representations for Healthcare Answer
Retrieval [68.02029435111193]
Contextual Discourse Vectors (CDV) is a distributed document representation for efficient answer retrieval from long documents.
Our model leverages a dual encoder architecture with hierarchical LSTM layers and multi-task training to encode the position of clinical entities and aspects alongside the document discourse.
We show that our generalized model significantly outperforms several state-of-the-art baselines for healthcare passage ranking.
arXiv Detail & Related papers (2020-02-03T15:47:19Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.