Work

From Information-Seeking to Information-Asking: Designing RefuGPT, a Chatbot for Ukrainian Refugees in Switzerland

Year: 2025

Type: article

Abstract: A key challenge for refugees is accessing relevant information about their host country. However, refugees often face disorientation during their information-seeking process. Disorientation is driven by language barriers, a digital divide, high urgency, and an evolving situation, which hinders the refugees’ ability to access information. Addressing the issue of disorientation, we propose an IT artifact, i.e., a chatbot called RefuGPT , designed through a rigorous Design Science Research Methodology. RefuGPT leverages the power of a large language model (LLM), GPT-4 while accessing two distinct sources of information, i.e., official and community-based information utilizing retrieval augmented generation and prompt-based learning (Figure 1 ). We assess RefuGPT's usefulness and usability through an ex-post evaluation in a naturalistic setting with Ukrainian refugees. Hence, we show that our bi-layered LLM-based chatbot can effectively assist refugees. They thus move from information-seeking to a more effortless information-asking. Based on those insights, we propose design principles as well as practical recommendations for refugee chatbots.

Source: Digital Government Research and Practice

DOI: 10.1145/3735140

Authors: Kilian Sprenkamp, Sven Eckhardt, Liudmila Zavolokina, Gerhard Schwabe

Institutions: University of Zurich, University of Lausanne

Language: English

Topic: AI in Service Interactions

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Reduced inequalities

Open Access status: bronze

Funder: Universität Zürich