From Search to GenAI Queries: Global Trends in Physics Information-Seeking Across Topics and Regions
Yossi Ben-Zion (1), Omer Michaeli (1), Noah D. Finkelstein (2) ((1) Department of Physics, Bar-Ilan University, Ramat Gan, Israel, (2) Department of Physics, University of Colorado Boulder, Boulder, USA)

TL;DR
This study analyzes global trends in physics information-seeking, revealing a significant decline in traditional search activity across topics and regions, likely due to the rise of generative AI tools, with variations based on language and domain.
Contribution
It provides a comprehensive, cross-platform analysis of how generative AI influences physics-related search behaviors worldwide, highlighting domain and regional differences.
Findings
Significant decline in search activity across physics topics.
Greater decline in Mechanics than Electromagnetism.
Regional and linguistic heterogeneity in search behavior.
Abstract
The emergence of generative artificial intelligence (GenAI) marks a potential inflection point in the way academic information is accessed, raising fundamental questions about the evolving role of search in student learning. This study examines this shift by analyzing longitudinal trends in physics-related search and page-view activity, using declines in traditional search behavior as a quantitative proxy for changes in independent information-seeking practices. We analyze Google Trends data for core concepts in Classical Mechanics and Electromagnetism across three academic years (2022-2025) in more than 20 countries, and complement this analysis with Wikipedia page-view data across seven major languages to establish platform independence. The results reveal a substantial, systematic, and persistent global decline in search and page-view activity across most examined physics topics. The…
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Taxonomy
TopicsEducational Strategies and Epistemologies · Misinformation and Its Impacts · Computational and Text Analysis Methods
