AI-Assisted Document Tagging - Exploring Adaptation Effects among Domain Experts

Keeping a professional knowledge database with scientific publications up to date requires continuous scanning and annotating of newly published articles by domain experts. To shorten this time-consuming process, we study experts’ assistance through a domain-specific tag recommender. We introduce the real-life case of a knowledge management system for nursing practitioners and present its architecture and user interface for creating and assigning tag recommendations. While the original tagging interface was thought to assist experts in the tagging process and possibly challenge them to reconsider their tag selections or assign more tags to a document, a preliminary evaluation shows an uncritical adoption of the provided recommendations by experts. We conclude that future design iterations of the recommendation user interface should try to prevent blind trust in the system and encourage reflection on tag suggestions.