Identifying Human-GenAI Relations for Knowledge Management with Action Design Research Approach

Recognizing the flexibility and autonomy of interactions between humans and Generative AI (GenAI), the conventional service system development approach faces challenges, and a new approach is needed to identify the roles of both users and GenAI. In this study, we adopted the Action Design Research(ADR) approach to develop a knowledge assistant (KA) that facilitates knowledge activities occurred in communities of practice. Lead users co-designed anticipated KA roles, aiming for enhancing role congruence between humans and KAs. Periodic interviews, tracing evolving human–KA relationships, provide insights to refine functions and strengthen collaboration. Using content analysis leveraged by ChatGPT’s natural language processing capabilities, we validated distinct patterns of role enactment between users and their KAs. This study demonstrates a new development cycle that emphasizes role congruence in developing human–AI collaborative service systems, particularly in relation to the socialization stage of the knowledge creation process.

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Publication Details

Journal
ScholarSpace (University of Hawaii at Manoa)
Published
2025-12-23
Primary Topic
AI in Service Interactions
Type
article
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article

Identifying Human-GenAI Relations for Knowledge Management with Action Design Research Approach

Fu‐ren Lin, Wen-Cheng Shen, Yi-Hsuan Yang
ScholarSpace (University of Hawaii at Manoa)
AI in Service Interactions
article

Identifying Human-GenAI Relations for Knowledge Management with Action Design Research Approach

Fu‐ren Lin, Wen-Cheng Shen, Yi-Hsuan Yang
article en

Abstract

Recognizing the flexibility and autonomy of interactions between humans and Generative AI (GenAI), the conventional service system development approach faces challenges, and a new approach is needed to identify the roles of both users and GenAI. In this study, we adopted the Action Design Research(ADR) approach to develop a knowledge assistant (KA) that facilitates knowledge activities occurred in communities of practice. Lead users co-designed anticipated KA roles, aiming for enhancing role congruence between humans and KAs. Periodic interviews, tracing evolving human–KA relationships, provide insights to refine functions and strengthen collaboration. Using content analysis leveraged by ChatGPT’s natural language processing capabilities, we validated distinct patterns of role enactment between users and their KAs. This study demonstrates a new development cycle that emphasizes role congruence in developing human–AI collaborative service systems, particularly in relation to the socialization stage of the knowledge creation process.

ScholarSpace (University of Hawaii at Manoa)
Openalex Percentile: Top 18%
AI in Service Interactions
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