As the use of Large Language Model (LLM) chatbots by students and researchers becomes more prevalent, universities are pressed to develop AI strategies. One strategy that many universities pursue is to customize pre-trained LLM-as-a-service (LLMaaS) chatbots. While most studies on LLMaaS chatbots prioritize technical adaptations, these systems are often mainly characterized by user-salient front-end customizations, e. g., interface changes. Yet, no existing studies have examined how users perceive such systems compared to commercial LLM chatbots. In a field study, we investigate how students and employees (N = 526) at a German university perceive and use their institution’s customized LLMaaS chatbot compared to ChatGPT. Participants using both systems (n = 116) reported greater trust, higher perceived privacy, and less perceived hallucinations with their university’s customized LLMaaS chatbot compared to ChatGPT. We discuss implications for research on users’ trustworthiness assessment process, and offer guidance for the design and deployment of LLMaaS chatbots.

Appropriate reviewer assignment significantly impacts the quality of proposal evaluation, as accurate and fair reviews are contingent on their assignment to relevant reviewers. The crucial task of assigning reviewers to submitted proposals is the starting point of the review process and is also known as the reviewer assignment problem (RAP). Due to the obvious restrictions of manual assignment, journal editors, conference organizers, and grant managers demand automatic reviewer assignment approaches. Many studies have proposed assignment solutions in response to the demand for automated procedures since 1992. The primary objective of this survey paper is to provide scholars and practitioners with a comprehensive overview of available research on the RAP. To achieve this goal, this article presents an in-depth systematic review of 103 publications in the field of reviewer assignment published in the past three decades and available in the Web of Science, Scopus, ScienceDirect, Google Scholar, and Semantic Scholar databases. This review paper classified and discussed the RAP approaches into two broad categories and numerous subcategories based on their underlying techniques. Furthermore, potential future research directions for each category are presented. This survey shows that the research on the RAP is becoming more significant and that more effort is required to develop new approaches and a framework.