Speaker
Description
Evaluating commercial search engines is severely limited because providers offer no API access for collecting search results or AI summaries. Furthermore, independent data collection is limited by the dynamic nature of client-side rendering for these generative features. To overcome this, we present the updated Result Assessment Tool (RAT 2.0), an open-source Python toolkit comprising three integrated tools. First, the Query Sampler systematically generates topical query sets. Second, a custom Chrome extension simulates authentic user interactions to capture dynamic AI content, bypassing the limitations of traditional headless scrapers. Third, a web platform empowers researchers to design studies and archive full Search Engine Result Page metadata for consistent human assessments. Therefore, RAT enables data-driven investigations across information science and the social sciences.