Artificial intelligence (AI) is used on a number of Wikipedia and Wikimedia projects. This may be directly involved with creation of text content, or in support roles related to evaluating article quality, adding metadata, or generating images. As with any machine-generated content, care must be used when employing AI at scale or in applying it where the community consensus is to exercise more caution.

When exploring AI techniques and systems, the community consensus is to prefer human decisions over machine-generated outcomes until the implications are better understood.

At the onset of the 2020s AI boom, Wikipedia's existing content policies already addressed many of the emerging AI-related concerns that prompted other platforms and organizations to adopt a dedicated new policy; consequently, Wikipedia has no single all-encompassing, detailed "AI use policy", "AI-generated content policy", "AI content guideline", et cetera. Wikipedia:Large language models § Risks and relevant policies (essay) aims to explain how the broad core content policies and the copyrights policy interact with the use of AI tools, mostly in the domain of text.

A dedicated guideline in this area does exist: Wikipedia:Writing articles with large language models (WP:NEWLLM). It is the closest thing to an explicit "AI policy" page on English Wikipedia, but it is intentionally very spartan, comprising one point: Large language models should not be used to generate new Wikipedia articles from scratch. Still, disparate portions of other policies and guidelines contain certain provisions that are specifically and explicitly about AI-generated content. The most important of these is the speedy deletion criterion Wikipedia:Speedy deletion § G15. LLM-generated pages without human review (WP:G15), which forms the policy basis to speedily delete pages that could only plausibly have been generated by large language models and can be assumed not to have undergone reasonable human review. Seen together, NEWLLM and G15 reflect the project's expectations that large language models are not to be used to originate articles and that the editor who adds LLM-originated text to the site (not limited to articles) should reasonably review it to ensure that it complies with all applicable policies and guidelines.

The rest of those other relevant (albeit non-dedicated) policies and guidelines are listed here as follows (November 2025):

• Wikipedia:Image use policy § AI-generated images (WP:AIIMAGES), a policy section against the use of images wholly generated by AI
  • referred to in Wikipedia:No original research § Original images (WP:IMAGEOR)
• portion of Wikipedia:Biographies of living persons § Images (WP:AIIMGBLP), a policy norm against the use of AI-generated images to depict subjects of BLPs
• portion of Wikipedia:Manual of Style/Images § Editing images (MOS:AIUPSCALE), a MoS norm against the use of AI upscaling software
• portion of Wikipedia:Public domain § Works ineligible for copyright protection (WP:NONCREATIVE), a guideline paragraph recounting the legal principle that works created by machines are not copyrightable (generally valid as of 2025^[1])
• Wikipedia:Reliable sources § Sources produced by machine learning (WP:RSML), a guideline section against citing AI-generated content as sources
  • referred to in Wikipedia:Reliable sources/Perennial sources § Large language models (WP:RSPLLM), an information page
• Wikipedia:Talk page guidelines § LLM-generated (WP:AITALK), a guideline entry allowing the striking or collapsing of comments that are obviously generated by an LLM or similar AI technology

The following are not policies or guidelines, but still have some significance in this context:

• Wikipedia:Translation § Machine translation (WP:MACHINE), an information page section concerning neural machine translation (i.e., "AI translation")
• an entry in Wikipedia:Drafts § Reasons to move an article to draftspace (WP:DRAFTREASON), an explanatory essay about policies on editing and deletion
• portion of Wikipedia:Guide to appealing blocks § Composing your request to be unblocked (WP:NICETRY), an explanatory essay stating that unblock requests that appear to be written using AI are likely to be summarily rejected

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AI-related efforts on Wikipedia include but are not limited to:

The Objective Revision Evaluation Service (ORES) was started in 2015 as a project of the Wikimedia Foundation, and provides a revision score against machine learning models that have been trained in order to report article quality or vandalism. This is used in tools such as ClueBot NG to help immediately revert vandalism, or in evaluation tools like the Program and Events Dashboard to measure the outcomes of classwork, edit-a-thons, or organized editing campaigns.

Guidance can be found at Wikipedia:Translation#Content. There is a Content Translation Tool used across Wikimedia projects that can use the output of machine translation from one Wikipedia article to another, using services like Google Translate. However, on the English Wikipedia, it currently states that "machine translation is disabled for all users and this tool is limited to extended confirmed editors." As a result, only manual translation on the English Wikipedia is supported by the tool, though some users have used translation to Simple English as a workaround. Relatedly, there is a section of the Help:Translation page with the broad advice: "avoid machine translations." As of January 2026, an active Request for Comment exists on machine translation guidance for LLM translations, with community discussion ongoing about appropriate guidelines for this evolving technology.

The explosion of interest in ChatGPT in 2022 has led to increased curiosity in using generative AI to help compose Wikipedia articles. However, current consensus is that "Large language models should not be used to generate new Wikipedia articles from scratch." The status of machine-generated text from tools such as ChatGPT is generally accepted to be public domain, so the copyright issues are not a blocker to using the generated text from a legal standpoint. These issues are generally governed by Help:Adding open license text to Wikipedia#Converting and adding open license text to Wikipedia, which advises to make sure content is adjusted for style and that reliable sources are used.

Image metadata – There have been efforts from GLAM institutions to help supplement image keyword data with machine learning efforts. Among them include:

• Computer aided tagging Started in 2019, "The computer-aided tagging tool is a feature in development by the Structured Data on Commons team to assist community members in identifying and labeling depicts statements for Commons files." See: c:Commons:Structured data/Computer-aided tagging
• Metropolitan Museum of Art Tagging - This project used Met Museum tagging info to train a machine learning system to help predict new "depiction" recommendations for Wikidata. This resulted in a new Wikidata Game that helped add more than 4,000 new depiction (P180) statements to Wikidata. See the Met Museum blog post by Andrew Lih: "Combining AI and Human Judgment to Build Knowledge about Art on a Global Scale," March 4, 2019, [1]

Image generation

• Wikimedia Commons and AI generated media
• AI images and German Wikipedia, results of a meeting
• A Battle for Reality, video essay on AI images and Wikipedia
• Wikimedia Commons AI, a rejected proposal for a new Wikimedia sister project aimed at establishing a clear distinction between human-generated content and content produced by artificial intelligence
  • The four categories, an idea about dividing all images uploaded to Wikimedia Commons in one of four categories

• Artificial intelligence in Wikimedia projects
• Wikipedia:Large language models, an essay on using LLMs (textual generative AI) to produce or modify content on Wikipedia
• Wikipedia:Computer-generated content, a draft of a proposed policy on using computer-generated content in general on Wikipedia
• Wikipedia:WikiProject AI Cleanup, a group of editors focusing on the issue of non-policy-compliant LLM-originated content
• Wikipedia:WikiProject AI Tools, a group of editors collaborating on tools using AI to improve Wikipedia
• Wikipedia:WikiProject Wikipedia spoken by AI voice, proposed project to make natural-sounding audios available for articles at scale
• Wikipedia:Bot
• m:Research:Implications of ChatGPT for knowledge integrity on Wikipedia, Wikimedia research project
• m:Wikilegal/Copyright Analysis of ChatGPT
• m:Category:Artificial intelligence

• Lih, Andrew (March 4, 2019). "Combining AI and Human Judgment to Build Knowledge about Art on a Global Scale". Metropolitan Museum of Art.

• Morgan, Jonathan T. (18 July 2019). "Designing ethically with AI: How Wikimedia can harness machine learning in a responsible and human-centered way". WIkimedia Foundation.
• Redi, Miriam (14 March 2018). "How we're using machine learning to visually enrich Wikidata". Wikimedia Foundation.
• meta:Research:Ethical and human-centered AI

• Signpost/2025-12-01 experiment (identifying and correcting errors in 90% of 31 recent English Wikipedia 'Today's featured articles')

• User:WeatherWriter/LLM Experiment 2 (identifying sourced and unsourced information, including a non-English source)
• User:WeatherWriter/LLM Experiment 3 (identifying sourced and unsourced information, only six of seven tests successful)
• Wikipedia:Articles for deletion/ChatGPT and Wikipedia:Articles for deletion/Planet of the Apes (humorous April Fools' nominations generated almost entirely by large language models).

• User:JPxG/LLM demonstration 2 (suggestions for article improvement, explanations of unclear maintenance templates based on article text)
• Artwork title, a surviving article initially developed from raw LLM output (before this page had been developed)
• User:Fuzheado/ChatGPT (PyWikiBot code, writing from scratch, Wikidata parsing, CSV parsing)
• User:DraconicDark/ChatGPT (lead expansion)
• Wikipedia:Using neural network language models on Wikipedia/Transcripts (showcases actual mainspace LLM-assisted copyedits)
• User:WeatherWriter/LLM Experiment 1 (identifying sourced and unsourced information)

• User:JPxG/LLM demonstration (wikitext markup, table rotation, reference analysis, article improvement suggestions, plot summarization, reference- and infobox-based expansion, proseline repair, uncited text tagging, table formatting and color schemes)

• Shit flow diagram, an experiment in using a constrained set of data to write an article^{[clarification needed]}
• User:BrokenSegue - Wikidata:Wwwyzzerdd and Psychiq Wikidata game that uses distilBERT and ML, analyzing Wikipedia categories.

• Wikipedia:Artificial intelligence resources