AI Assistants and Generative AI for Knowledge Creation, Retention, and Use

Permanent URI for this collectionhttps://hdl.handle.net/10125/112518

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Design Features for Explainable Generative AI (GenXAI) Systems in Knowledge-Intensive Service Work
(2026-01-06) Reinhard, Philipp; Li, Mahei; Fina, Matteo; Peters, Christoph; Leimeister, Jan Marco
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).
AI, Deepfakes, and the Normalization of Digital Harm: A Social Media Cultivation Perspective
(2026-01-06) Kim, Garan; Durcikova, Alexandra
This research-in-progress study examines how repeated exposure to AI-generated deepfake content on social media contributes to the psychological normalization of unethical behavior, particularly among young adults. Deepfakes, along with other forms of manipulated media, are increasingly perceived as trivial or entertaining, even as their ethical and legal implications remain underexamined. Extending cultivation theory to the social media context, this study proposes an emotion–cognition framework in which users’ emotional and cognitive responses shape internalized attitudes and beliefs. These internal states lead to desensitization and moral disengagement, which in turn normalize deepfake content. Young adults, whose neurocognitive and moral regulation are still developing, may be especially vulnerable to these effects. The study contributes to the IS field by illuminating how generative AI media influence morality and reshape perceptions of authenticity, credibility, and digital knowledge practices. The conceptual framework is subject to further refinement, with empirical testing forthcoming.
Navigating Generative AI Disruptions: A Process Model of Occupational Resilience
(2026-01-06) Caya, Olivier; Gagnon, Elisa
Generative Artificial Intelligence (GenAI) is transforming knowledge workers by introducing automation capabilities that challenge traditional notions of expertise, creativity, and problem-solving. While GenAI offers new opportunities for productivity and innovation, it also disrupts professional identities, raising concerns about job displacement, occupational shifts, and ethical dilemmas. This study examines how knowledge workers (KWers) develop occupational resilience when faced with GenAI transformations. Building on prior literature on resilience, we propose that occupational resilience is critical for sustaining professional relevance and well-being in the GenAI era. Using a grounded theory approach, we present early qualitative insights from thirteen interviews to propose a process model of GenAI occupational resilience. By addressing these challenges, this study contributes to a deeper understanding of GenAI’s impact on KWers and offers insights for developing adaptation strategies.
Identifying Human-GenAI Relations for Knowledge Management with Action Design Research Approach
(2026-01-06) Shen, Wen-Cheng; Yang, Yi-Hsuan; Lin, Fu-Ren
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.
Using Google’s Natural Language Model to Measure Growth of Knowledge in Information Systems Research
(2026-01-06) Hassan, Nik; Marrone, Mauricio; Schryen, Guido; Yang, Jiaqi
The goal of this paper is to propose a new artificial-intelligence (AI) driven method to evaluate how well the information systems (IS) field engages with other disciplines in the process of building IS knowledge. The proposed method combines the veracity and objectivity of quantitative scientometric methods with the semantic depth and interpretive validity of qualitative content analysis methods, both building on theories of citations and disciplinarity. The results find that the IS field relies mostly on reviewed and perfunctory citation functions that do not truly engage with previous research. This evaluation presents a wake-up call to the field to better leverage and engage with theories from previous research. It also showcases the scientometric bases for enhancing the originality of IS research and help the field become intellectually and socially influential. Keywords: Disciplinary theory, citation theory, artificial intelligence (AI) and natural language processing, information systems (IS) knowledge.
Unlocking Tacit Knowledge in Industrial Production: Exploring Barriers, Practices, and LLM-Driven Potentials for Knowledge Management
(2026-01-06) Finkel, Pius; Wurster, Peter
In aging societies across western industrialized nations, the loss of expertise due to retiring skilled workers presents a critical challenge for industry. That is especially true on the shop floor, where much of the knowledge is tacitly gained through years of hands-on experience rather than formal documentation. This study explores current knowledge management (KM) challenges and systematically identifies high-potential applications for large language models (LLMs) as part of a broader research initiative aiming to develop human-centered KM solutions supported by generative artificial intelligence (GenAI). We conducted two structured workshops with 23 participants from 14 German manufacturing companies. Three core barriers and two prioritized LLM use cases were identified, contributing specific design recommendations for LLM-supported KM systems for companies. The results advance the understanding of GenAI-assisted knowledge retention in industrial settings and provide a practical foundation for addressing the demographic shift through intelligent, technology-driven solutions.
From Search to Dialogue: An Experimental Comparison of User Experience, Satisfaction and Success with ChatGPT and Google
(2026-01-06) Schätzle, Anna; Walenta, Danilo; Mehler, Maren; Buxmann, Peter
Generative Artificial Intelligence (GenAI) is increasingly shaping informal learning in everyday life, with tools like ChatGPT becoming part of many individuals’ daily routines. While prior research has focused primarily on student learning, the role of GenAI in adult everyday information-seeking remains underexplored. This study examines how GenAI influences user experience, learning satisfaction, and learning outcomes in informal contexts. In a randomized online experiment (N = 120), participants completed learning tasks using both ChatGPT and Google. Results show that ChatGPT significantly improves user experience and learning satisfaction compared to traditional search engines. Additionally, self-efficacy positively impacts learning satisfaction, and both user experience and satisfaction are associated with fewer unknown responses—leading to higher quiz scores and better learning outcomes. These findings underscore the value of conversational GenAI in enhancing informal learning, highlighting its potential to support more effective, satisfying, and self-directed digital knowledge acquisition.
From Prompts to Probes: How Large Language Models Improve Response Quality in Open-Ended Survey Research
(2026-01-06) Wieland, Dominik; Leyh, Nicolas; Ahrens, Fabian
Probing (i.e., asking follow-up questions to elicit elaboration) is a common method in qualitative research. While effective in human interviews, its benefits in AI-led surveys operated by chatbots remain underexplored. This paper investigates whether follow-up questions generated by large language models (LLMs) can improve the quality of open-ended survey responses. In a between-subjects experiment (N = 151), we compared different probing strategies and measured response quality by word count and thematic richness. Contextual probing significantly increased both response length and thematic richness. These findings indicate that LLMs can emulate key techniques of qualitative interviewing, enabling richer and more informative responses in online surveys. This positions LLM-driven probing as a scalable way to enhance data quality, bridging the gap between automation and qualitative depth. The study contributes to conversational AI research by showing how real-time adaptation fosters user elaboration, and offers practical guidance for integrating LLMs into surveys requiring nuanced input.
Introduction to the Minitrack on AI Assistants and Generative AI for Knowledge Creation, Retention, and Use
(2026-01-06) Hadaya, Pierre; Smolnik, Stefan; Bockshecker, Alina

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