Date of Award
5-2026
Document Type
Dissertation
Degree Name
Doctor of Philosophy (PhD)
Department
School of Computing
Committee Chair/Advisor
Dr. Emma Dixon
Committee Member
Dr. Andrew Duchowski
Committee Member
Dr. Long Cheng
Committee Member
Dr. Guo Freeman
Abstract
This dissertation examines the accessibility, readability, emotional tone, and trustworthiness of digital, text‑based dementia information and investigates how older adults with and without dementia cognitively and affectively engage with such content. It further explores the potential of generative AI to improve dementia information delivery through the development of a domain‑specific retrieval‑augmented generation (RAG) large language model (LLM) chatbot designed to deliver accessible, stigma‑free information. The dissertation comprises four studies using complementary quantitative and qualitative methods. Study I conducts a large‑scale content analysis of 300 medical articles, 35 websites, and 50 blogs, applying readability metrics, LIWC‑based linguistic and sentiment analysis, and thematic analysis of intended audiences. Study II employs a mixed factorial design with webcam‑based eye‑tracking and facial expression analysis to examine visual attention, cognitive effort, and emotional responses among people with dementia (PWD) and older adults without dementia engaging with digital dementia information of varying complexity and credibility. Study III uses qualitative focus groups to investigate how PWD interact with and attempt to co‑customize commercially available generative AI chatbots. Study IV develops and evaluates two versions of a domain‑specific RAG‑LLM chatbot, comparing a medical‑only knowledge base with a hybrid knowledge base integrating medical evidence and lived‑experience sources. Findings show that most digital dementia information is too difficult to read and overly negative, despite targeting PWD. While PWD visually scan content similarly to other older adults, they experience higher cognitive load, less stable eye movements, and stronger emotional responses, particularly with complex medical content. Co‑customizing existing generative AI tools was found to be cognitively demanding and fatiguing. The hybrid RAG‑LLM chatbot outperformed the medical‑only version, delivering more readable, emotionally balanced, accurate responses with fewer hallucinations. Overall, this dissertation demonstrates a person‑centered approach to delivering accessible, trustworthy, and emotionally sensitive dementia information.
Recommended Citation
Engineer, Margi, "Towards Dementia-Inclusive Digital Text-Based Health Information: A Multi-Method Investigation of Cognitive and Affective Accessibility" (2026). All Dissertations. 4224.
https://open.clemson.edu/all_dissertations/4224
Author ORCID Identifier
0009-0002-5074-2013
Included in
Accessibility Commons, Alternative and Complementary Medicine Commons, Cognitive Science Commons, Disability Studies Commons, Health Communication Commons, Health Information Technology Commons, Other Social and Behavioral Sciences Commons, Semantics and Pragmatics Commons