Design Features for Explainable Generative AI (GenXAI) Systems in Knowledge-Intensive Service Work
Journal
Hawaii International Conference on System Sciences (HICSS)
Type
conference paper
Date Issued
2026-01-06
Author(s)
Research Team
IWI6
Abstract
The use of generative AI (GenAI) and large language models (LLMs) in knowledge-intensive fields like customer support is rapidly growing. While GenAI responses often appear persuasive, they carry the risk of inaccuracies and hallucinations. Hence, users must critically evaluate responses to reach appropriate reliance and knowledge utilization. Despite technological advancements, design knowledge for enhancing human-GenAI interaction from an explainable AI (XAI) perspective remains lacking. Thus, this study applies the design science research (DSR) approach to develop explanations that aid human interaction with GenAI systems. Drawing from XAI literature and human reasoning theories, we built and evaluated seven design features and instantiated a prototype that contributes to the development of reliable explainable GenAI (GenXAI).
Language
English
Keywords
generative ai
xai
large language models
design science
HSG Classification
contribution to scientific community
Refereed
Yes
Publisher place
Maui, Hawaii, USA
Pages
10
Event Title
Hawaii International Conference on System Sciences (HICSS)
Event Location
Maui, Hawaii, USA
Event Date
06.01.2026
Subject(s)
Division(s)
File(s)![Thumbnail Image]()
open.access
Name
JML_1038.pdf
Size
742.66 KB
Format
Adobe PDF
Checksum (MD5)
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