IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2605.08812.html

Little Impact of ChatGPT Availability on High School Student Test Score Performance

Author

Listed:
  • Nick Huntington-Klein

Abstract

In educational settings, AI can be used as a learning aid, but can also be used to avoid schoolwork, thereby passing classes while learning little. Many existing studies on the impact of AI on education focus on AI use in controlled settings or with specialized tools. In this paper, the dropoff in ChatGPT activity during non-school summer months in 2023 and 2024 is used to identify areas with heavy educational AI use and thus estimate the educational impact of AI as it is actually used. I find no meaningful impact of AI usage on high school test score averages in either direction. These results imply that, to the extent that high school students use AI to avoid learning, it either does not matter much for their test performance or is cancelled out by positive uses of AI in the aggregate.

Suggested Citation

  • Nick Huntington-Klein, 2026. "Little Impact of ChatGPT Availability on High School Student Test Score Performance," Papers 2605.08812, arXiv.org, revised May 2026.
    • Handle: RePEc:arx:papers:2605.08812
    as

    Download full text from publisher

    File URL: https://arxiv.org/pdf/2605.08812
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Janik Ole Wecks & Johannes Voshaar & Benedikt Jost Plate & Jochen Zimmermann, 2024. "Generative AI Usage and Exam Performance," Papers 2404.19699, arXiv.org, revised Nov 2024.
    2. Aaron Chatterji & Thomas Cunningham & David J. Deming & Zoe Hitzig & Christopher Ong & Carl Yan Shan & Kevin Wadman, 2025. "How People Use ChatGPT," NBER Working Papers 34255, National Bureau of Economic Research, Inc.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Alexander Bick & Adam Blandin & David J. Deming & Nicola Fuchs-Schündeln & Jonas Jessen, 2026. "Mind the Gap: AI Adoption in Europe and the U.S," NBER Working Papers 34995, National Bureau of Economic Research, Inc.
    2. John Conlon & Peter Schwardmann, 2026. "AI Sycophancy and Decisions," CESifo Working Paper Series 12681, CESifo.
    3. Leland D. Crane & Paul E. Soto, 2026. "AI and Coder Employment: Compiling the Evidence," Finance and Economics Discussion Series 2026-018, Board of Governors of the Federal Reserve System (U.S.).
    4. Se Yan & Han Zhong & Zemin & Zhong & Wenyu Zhou, 2026. "Shopping with a Platform AI Assistant: Who Adopts, When in the Journey, and What For," Papers 2603.24947, arXiv.org.
    5. Jonathan Shaki & Eden Hartman & Sarit Kraus & Yonatan Aumann, 2026. "Sustaining Cooperation in Populations Guided by AI: A Folk Theorem for LLMs," Papers 2605.06525, arXiv.org.
    6. Michelle Yin & Burhan Ogut, 2026. "Who Uses AI? Platform Selection and the Measurement of Occupational AI Exposure," Papers 2605.21743, arXiv.org, revised May 2026.
    7. Rok Gabrovšek & David Rihtaršič, 2025. "Custom Generative Artificial Intelligence Tutors in Action: An Experimental Evaluation of Prompt Strategies in STEM Education," Sustainability, MDPI, vol. 17(21), pages 1-20, October.
    8. Anand, Kartik & Kazinnik, Sophia & Leonello, Agnese & Panetti, Ettore, 2025. "Ex Machina: Financial Stability in the Age of Artificial Intelligence," CEPR Discussion Papers 20681, Centre for Economic Policy Research.
    9. Elliott Ash & Francesco Capozza & Sergio Galletta, 2026. "Demand for Conversational AI," CESifo Working Paper Series 12673, CESifo.
    10. Scott Counts & Yan Chen & Jing Dong & Himanshu Sharma & Andrey Zaikin & Rui Hu & Alperen Kok & Gorkem Ozer Yilmaz & Siddharth Suri & Kiran Tomlinson & Sonia Jaffe & Will Wang, 2026. "AI in the Enterprise: How People Use M365 Copilot Chat," Papers 2605.23958, arXiv.org.
    11. Cao, Wenjun, 2025. "Withstand the Fall of Time: Temporality, Market Selection, and Human Learning under Generative Models," SocArXiv ps7ku_v1, Center for Open Science.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:arx:papers:2605.08812. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: arXiv administrators (email available below). General contact details of provider: https://arxiv.org/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.