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From search to GenAI queries: Global trends in physics information-seeking across topics and regions

Yossi Ben-Zion1,*, Omer Michaeli1, and Noah D. Finkelstein2

  • *Contact author: benzioy@biu.ac.il
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Phys. Rev. Phys. Educ. Res. 22, 020120 – Published 17 August, 2026

DOI: https://doi.org/10.1103/g1vh-fl9s

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, electromagnetism, and optics 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 magnitude of this decline is domain-dependent, with mechanics-related content showing sharper and more consistent reductions than content related to electromagnetism and optics. Pronounced geographic and linguistic heterogeneity is observed: while English-speaking regions show relative stability or only moderate declines, non-English-speaking regions exhibit substantially larger reductions in traditional, search-based information-seeking activity. Despite the overall decrease in volume, the seasonal structure characteristic of academic activity remains robust. Taken together, these findings indicate a redistribution of physics-related information-seeking behavior in academic contexts where generative tools are increasingly available.

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References (54)

  1. S. Stephens-Davidowitz, Everybody Lies: What the Internet Can Tell Us about who We Really Are (Bloomsbury Publishing, London 2017).
  2. J. Ginsberg, M. H. Mohebbi, R. S. Patel, L. Brammer, M. S. Smolinski, and L. Brilliant, Detecting influenza epidemics using search engine query data, Nature (London) 457, 1012 (2009).
  3. T. Preis, H. S. Moat, and H. E. Stanley, Quantifying trading behavior in financial markets using google trends, Sci. Rep. 3, 1684 (2013).
  4. Z. Da, J. Engelberg, and P. Gao, In search of attention, J. Finance 66, 1461 (2011).
  5. H. Choi and H. Varian, Predicting the present with google trends, Econ. Rec. 88, 2 (2012).
  6. I. Rowlands, D. Nicholas, P. Williams, P. Huntington, M. Fieldhouse, B. Gunter, R. Withey, H. R. Jamali, T. Dobrowolski, and C. Tenopir, The google generation: The information behaviour of the researcher of the future, in Aslib Proceedings (Emerald Group Publishing Limited, Bingley, England, 2008), Vol. 60, pp. 290–310.
  7. Google, Google trends (2026). https://www.google.com/ trends, accessed: January 21, 2026.
  8. T. Gillis and S. Garrison, Confounding effect of undergraduate semester–driven “academic” internet searches on the ability to detect true disease seasonality in google trends data: Fourier filter method development and demonstration, JMIR Infodemiol. 2, e34464 (2022).
  9. S. Arslan, M. Tiwari, and C. Piech, Using google search trends to estimate global patterns in learning, in Proceedings of the Seventh ACM Conference on Learning@ Scale (Association for Computing Machinery (ACM), New York, NY, 2020), pp. 185–195.
  10. E. Bilen and A. Matros, Online cheating amid COVID-19, J. Econ. Behav. Organization 182, 196 (2021).
  11. Microsoft AI Economy Institute, Global AI adoption in 2025: A widening digital divide, Tech. Rep., Microsoft Corporation, 2026.
  12. A. Misra, J. Wang, S. McCullers, K. White, and J. L. Ferres, Measuring AI diffusion: A population-normalized metric for tracking global AI usage, arXiv:2511.02781.
  13. Edelman, 2025 Edelman trust barometer: Insights on the technology sector, Tech. Rep., Edelman, 2025, accessed: January 2026.
  14. OECD, How Do People Experience New Technologies and Generative AI?: Insights From a Few Countries Worldwide, OECD Policy Insights on Well-Being, Inclusion and Equal Opportunity 23 (OECD Publishing, Paris, 2025).
  15. G. Kortemeyer, J. Nöhl, and D. Onishchuk, Grading assistance for a handwritten thermodynamics exam using artificial intelligence: An exploratory study, Phys. Rev. Phys. Educ. Res. 20, 020144 (2024).
  16. Z. Chen and T. Wan, Grading explanations of problem-solving process and generating feedback using large language models at human-level accuracy, Phys. Rev. Phys. Educ. Res. 21, 010126 (2025).
  17. R. K. Fussell, M. Flynn, A. Damle, M. F. J. Fox, and N. G. Holmes, Comparing large language models for supervised analysis of students’ lab notes, Phys. Rev. Phys. Educ. Res. 21, 010128 (2025).
  18. G. Kortemeyer and J. Nöhl, Assessing confidence in AI-assisted grading of physics exams through psychometrics: An exploratory study, Phys. Rev. Phys. Educ. Res. 21, 010136 (2025).
  19. P. Tschisgale, H. Maus, F. Kieser, B. Kroehs, S. Petersen, and P. Wulff, Evaluating GPT- and reasoning-based large language models on physics Olympiad problems: Surpassing human performance and implications for educational assessment, Phys. Rev. Phys. Educ. Res. 21, 020115 (2025).
  20. G. Polverini and B. Gregorcic, Performance of ChatGPT on the test of understanding graphs in kinematics, Phys. Rev. Phys. Educ. Res. 20, 010109 (2024).
  21. G. Polverini, J. Melin, E. Önerud, and B. Gregorcic, Performance of ChatGPT on tasks involving physics visual representations: The case of the brief electricity and magnetism assessment, Phys. Rev. Phys. Educ. Res. 21, 010154 (2025).
  22. J. C. Dunlap, R. Sissons, and R. Widenhorn, Descending an inclined plane with a large language model, Phys. Rev. Phys. Educ. Res. 21, 010153 (2025).
  23. D. Tong, B. Jin, Y. Tao, H. Ren, A. Y. M. Atiquil Islam, and L. Bao, Exploring the role of human-AI collaboration in solving scientific problems, Phys. Rev. Phys. Educ. Res. 21, 010149 (2025).
  24. S. Kilde-Westberg, A. Johansson, and J. Enger, Generative AI as a lab partner: A case study, Phys. Rev. Phys. Educ. Res. 21, 020119 (2025).
  25. J. Lademann, J. Henze, and S. Becker-Genschow, Augmenting learning environments using AI custom chatbots: Effects on learning performance, cognitive load, and affective variables, Phys. Rev. Phys. Educ. Res. 21, 010147 (2025).
  26. Y. Ben-Zion, R. E. Zarzecki, J. Glazer, and N. D. Finkelstein, Leveraging AI for rapid generation of physics simulations in education: Building your own virtual lab, Phys. Teach. 63, 424 (2025).
  27. P. Wattanakasiwich, K. Kaewkhong, and D. Katwibun, Physics instructors’ acceptance and implementation of generative AI, Phys. Rev. Phys. Educ. Res. 21, 010155 (2025).
  28. S. Küchemann, S. Steinert, N. Revenga, M. Schweinberger, Y. Dinc, K. E. Avila, and J. Kuhn, Can ChatGPT support prospective teachers in physics task development?, Phys. Rev. Phys. Educ. Res. 19, 020128 (2023).
  29. F. Kieser, P. Wulff, J. Kuhn, and S. Küchemann, Educational data augmentation in physics education research using ChatGPT, Phys. Rev. Phys. Educ. Res. 19, 020150 (2023).
  30. R. Shankar, Fundamentals of Physics II: Electromagnetism, Optics, and Quantum Mechanics (Yale University Press, New Haven, CT, 2016).
  31. R. Shankar, Fundamentals of Physics I: Mechanics, Relativity, and Thermodynamics (Yale University Press, New Haven, CT, 2019).
  32. E. Hecht, Optics (Pearson Education India, Noida, India, 2012).
  33. United Nations, Department of Economic and Social Affairs, Population Division, World population prospects 2024, online edition (2024), https://population.un.org/wpp/downloads, data tables for total population by country (estimates up to 2023).
  34. A. A. diSessa, Knowledge in pieces, in Constructivism in the Computer Age, edited by G. Forman and P. B. Pufall (Lawrence Erlbaum Associates, Inc., Hillsdale, NJ, 1988), pp. 49–70.
  35. A. A. diSessa, Toward an epistemology of physics, Cognit. Instr. 10, 105 (1993).
  36. D. Hammer, Student resources for learning introductory physics, Am. J. Phys. 68, S52 (2000).
  37. D. P. Maloney, T. L. O’Kuma, C. J. Hieggelke, and A. Van Heuvelen, Surveying students’ conceptual knowledge of electricity and magnetism, Am. J. Phys. 69, S12 (2001).
  38. M. T. H. Chi, J. D. Slotta, and N. De Leeuw, From things to processes: A theory of conceptual change for learning science concepts, Learn. Instr. 4, 27 (1994).
  39. P. B. Kohl and N. D. Finkelstein, Effects of representation on students solving physics problems: A fine-grained characterization, Phys. Rev. ST Phys. Educ. Res. 2, 010106 (2006).
  40. G. Kortemeyer, M. Babayeva, G. Polverini, R. Widenhorn, and B. Gregorcic, Multilingual performance of a multimodal artificial intelligence system on multisubject physics concept inventories, Phys. Rev. Phys. Educ. Res. 21, 020101 (2025).
  41. R. Yu, Z. Xu, S. CH-Wang, and R. Arum, Whose ChatGPT? unveiling real-world educational inequalities introduced by large language models, arXiv:2410.22282.
  42. D. Filimonovic, C. Rutzer, J. Macher, and R. Weder, Does Scientific Writing Converge to U.S. English? Evidence from Generative AI-Assisted Publications, arXiv:2511.11687.
  43. D. Lin, N. Zhao, D. Tian, and J. Li, Large language models reshape the language of science, arXiv:2504.12317.
  44. E. Zuckerman, Rewire: Digital Cosmopolitans in the Age of Connection (WW Norton & Company, New York, NY, 2013).
  45. OpenAI, ChatGPT supported countries (2026). https://help.openai.com/articles/7947663-chatgpt-supported-countries, accessed: February 8, 2026. Iran is excluded from the list of supported jurisdictions.
  46. M. Caulfield, Web Literacy for Student Fact-Checkers (Pressbooks, Montreal, Canada, 2017). open-access textbook.
  47. E. Kasneci, K. Seßler, S. Küchemann, M. Bannert, D. Dementieva, F. Fischer, U. Gasser, G. Groh, S. Günnemann, E. Hüllermeier et al., ChatGPT for good? on opportunities and challenges of large language models for education, Learn. Individ. Differ. 103, 102274 (2023).
  48. J. Reich, Stop Pretending You Know How to Teach AI (The Chronicle of Higher Education, Washington, DC, 2025).
  49. C. Wieman, Improving How Universities Teach Science: Lessons from the Science Education Initiative (Harvard University Press, Cambridge, MA, 2017).
  50. National Academies of Sciences, Engineering, and Medicine, Transforming Undergraduate STEM Education: Supporting Equitable and Effective Teaching (The National Academies Press, Washington, DC, 2025).
  51. E. Brewe, Modeling theory applied: Modeling instruction in introductory physics, Am. J. Phys. 76, 1155 (2008).
  52. T. O. B. Odden and R. S. Russ, Defining sensemaking: Bringing clarity to a fragmented theoretical construct, Sci. Educ. 103, 187 (2019).
  53. A. Sirnoorkar, P. D. O. Bergeron, and J. T. Laverty, Sensemaking and scientific modeling: Intertwined processes analyzed in the context of physics problem solving, Phys. Rev. Phys. Educ. Res. 19, 010118 (2023).
  54. Y. Ben-Zion, O. Michaeli, and N. D. Finkelstein, From search to GenAI queries: Global trends in physics information-seeking across topics and regions, 10.5281/zenodo.21131978, (2026).

Outline

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Phys. Rev. Phys. Educ. Res. 22, 020120– Published 17 August, 2026
  • Received 17 February 2026
  • Accepted 29 June 2026
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Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article’s title, journal citation, and DOI.

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