Designing for Trust: Integrating Self-referencing in Large Language Model-Based Health Coaching
Journal
Local Solutions for Global Challenges. DESRIST 2025. Lecture Notes in Computer Science
ISBN
978-3-031-93975-4
Type
conference paper
Date Issued
2025
Author(s)
Research Team
IWI6
Abstract
This research applies the Design Science Research (DSR) methodology to investigate how self-referencing in Large Language Model (LLM)-based health coaching influences user trust and perceptions of anthropomorphism. We synthesized theory-driven design principles to guide the integration of self-referencing and demonstrated them in a vignette-based prototype. Through a single-factorial between-subjects experiment, analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) and qualitative feedback, we identified a dual effect of self-referencing: while professional self-referencing enhances trust via increased anthropomorphism, overly personal references can directly undermine trust. Based on these findings, we refined our design principles to optimize trust-building in LLM-based coaching. Our contributions provide actionable design guidelines for creating more effective and trustworthy AI-driven health interventions, advancing the understanding of anthropomorphic design in digital coaching contexts.
Language
English
Keywords
Health behavior change
Coaching
Large language models
Trust
Anthropomorphism
HSG Classification
contribution to scientific community
Refereed
Yes
Publisher
Springer Nature Switzerland
Volume
Vol 15703
Start page
296
End page
309
Pages
14
Event Title
Local Solutions for Global Challenges. DESRIST 2025. Lecture Notes in Computer Science
Event Location
Cham
Event Date
02.06.2025
Subject(s)
Division(s)
File(s)![Thumbnail Image]()
Name
JML_1031.pdf
Size
723.04 KB
Format
Adobe PDF
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