AI Article Synopsis
- The research focuses on creating an intelligent query system for COVID-19 literature to improve knowledge accumulation and support the fight against the pandemic.
- The user evaluation highlighted several usability issues, including differing user needs and functionality errors, which need to be addressed in future redesigns.
- Ongoing refinements and evaluations of the system aim to enhance its capabilities for various user roles and foster collaborative information seeking across institutions.
Article Abstract
As the fight against COVID-19 continues, it is critical to discover and accumulate knowledge in scientific literature to combat the pandemic. In this work, we shared the experience in developing an intelligent query system on COVID-19 literature. We conducted a user-centered evaluation with 12 researchers in our institution and identified usability issues in four categories: distinct user needs, functionality errors, suboptimal information display, and implementation errors. Furthermore, we shared two lessons for building such a COVID-19 literature search engine. We will deploy the system and continue refining it through multiple phases of evaluation to aid in redesigning the system to accommodate different user roles as well as enhancing repository features to support collaborative information seeking. The successful implementation of the COVID-IQS can support knowledge discovery and hypothesis generation in our institution and can be shared with other institutions to make a broader impact.
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| http://dx.doi.org/10.3233/SHTI220194 | DOI Listing |
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