Experimental Evidence on the Learning Impact of Generative AI

A randomized experiment posted to arXiv on 9 July 2026 found that undergraduates given access to off-the-shelf generative AI during proctored, in-person essay-writing sessions scored 0.27 standard deviations higher on immediate knowledge tests, with the gains persisting one week later. Essay quality changed little while AI access was available but improved in style and relevance one week later when students wrote unaided, with the delayed gains larger among "augmentation users" who used AI to explain concepts rather than generate text, while automation users' short-run quality gains vanished once AI was removed. The authors attribute the learning gains to students shifting time away from drafting text toward reading and searching for information and reporting greater learning enjoyment.

Economics General Economics Submitted on 9 Jul 2026 Title:Experimental Evidence on the Learning Impact of Generative AI View PDF https://arxiv.org/pdf/2607.08849 HTML experimental https://arxiv.org/html/2607.08849v1 Abstract:We study how generative AI affects student learning in a randomized experiment. In proctored, in-person sessions, undergraduates learn about an unfamiliar topic and write an analytical essay with or without access to off-the-shelf generative AI, then complete unaided assessments immediately and one week later. We measure learning with knowledge tests factual and conceptual understanding and open-ended essays higher-order skills . AI access raises immediate test scores by 0.27 standard deviations. These gains persist one week later. Essay quality, by contrast, changes little while students have AI access but improves in style and relevance one week later, when students write unaided. These delayed gains are larger among augmentation users-who use AI to explain concepts rather than generate text-whereas automation users' short-run quality gains vanish once AI is removed. We find evidence for two mechanisms behind the learning gains: students shift time away from drafting text and toward reading and searching for information, and they report greater learning enjoyment. Current browse context: econ.GN References & Citations Loading... Bibliographic and Citation Tools Bibliographic Explorer What is the Explorer? https://info.arxiv.org/labs/showcase.html arxiv-bibliographic-explorer Connected Papers What is Connected Papers? https://www.connectedpapers.com/about Litmaps What is Litmaps? https://www.litmaps.co/ scite Smart Citations What are Smart Citations? https://www.scite.ai/ Code, Data and Media Associated with this Article alphaXiv What is alphaXiv? https://alphaxiv.org/ CatalyzeX Code Finder for Papers What is CatalyzeX? https://www.catalyzex.com DagsHub What is DagsHub? https://dagshub.com/ Gotit.pub What is GotitPub? http://gotit.pub/faq Hugging Face What is Huggingface? https://huggingface.co/huggingface ScienceCast What is ScienceCast? https://sciencecast.org/welcome Demos Recommenders and Search Tools Influence Flower What are Influence Flowers? https://influencemap.cmlab.dev/ CORE Recommender What is CORE? https://core.ac.uk/services/recommender arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs https://info.arxiv.org/labs/index.html .