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An Oct. 19 LSE Impact essay by Helen Choi argues that academics have an ethical responsibility to support Wikipedia as AI-generated answers may reduce visits to the encyclopedia. Choi suggests scholars contribute expertise, cite Wikipedia when they use it and teach students to edit it; the essay does not establish how much AI products have affected Wikipedia traffic.

Helen Choi argues that academics should help sustain Wikipedia, warning that AI products which provide direct answers may reduce visits to the volunteer-run encyclopedia and weaken the community that maintains it. In an essay published Oct. 19 by LSE Impact, Choi calls on scholars to contribute expertise, cite Wikipedia when they rely on it and teach students how to edit it.

Choi’s argument links Wikipedia’s readership to its capacity to maintain its articles. The site relies on volunteers not only to write entries, but also to code, administer and correct them. If fewer readers go on to become contributors, she says, the encyclopedia could lose people who update pages and fix errors. The essay presents that outcome as a risk, not as an established forecast.

AI assistants such as ChatGPT and Google Gemini can return answers directly, sometimes with links to Wikipedia. Choi says many users accept generated responses rather than checking the linked sources. She argues that this pattern could draw attention away from Wikipedia while AI companies continue to rely on large bodies of existing material, including Wikipedia’s content.

Her proposed responses are practical: academics can improve entries with subject knowledge and reliable sources, cite Wikipedia transparently when they use it, and design assignments that let students contribute to articles. Choi says student editing can teach research, source evaluation, copyright and attribution. The essay cites Wiki Education Foundation figures: since 2010, more than 152,000 students at over 8,000 colleges and universities in the United States and Canada have contributed more than 125 million words to Wikipedia.

At a glance
analysisWhen: Published Oct. 19, 2026
The developmentHelen Choi argues in an LSE Impact essay that academics should help defend Wikipedia as AI-generated answers may draw users away from the volunteer-run encyclopedia.

Academic Work Can Support Wikipedia

The essay matters because it frames Wikipedia’s future as more than a question of whether AI companies credit or pay for content. Choi emphasizes that an encyclopedia depends on an active community able to review, update and expand its material. If the audience and pool of potential volunteers shrink, she argues, the effect could reach the quality and continuity of public reference information.

For researchers and teachers, the proposal connects professional practice to a widely used public knowledge resource. Academics can bring subject expertise and access to scholarly sources to pages that need stronger evidence or clearer explanations. Teaching students to edit can also make information literacy concrete: learners must assess sources, follow citation rules and write for readers beyond a classroom.

Choi grounds the duty claim in Iris Marion Young’s Social Connection Model, which holds that responsibility for addressing injustice can arise from participation in the structures that produce it. She applies that framework to academia’s role in knowledge systems. This is the essay’s ethical argument, rather than a finding that all scholars accept or an institutional policy announced by LSE.

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Why Wikipedia Depends on Readers

Wikipedia is maintained by a volunteer community and is available as an open reference resource. Choi describes its value as both its content and its self-governing community, which produces and shares knowledge outside direct commercial or government control. She argues that academics, as researchers and teachers, are connected to the broader processes through which knowledge is produced and circulated.

The essay distinguishes between different ways AI firms obtain access to Wikipedia material. Choi says some companies scrape content without authorization, while others, including Microsoft, Amazon and Meta, pay for high-speed API access to pages. The source does not provide a company-by-company account of access arrangements or quantify what share of AI training material comes from Wikipedia.

Choi also notes longstanding skepticism of Wikipedia in academic settings, while saying citations to it in academic journals appear to be increasing, including in high-impact health-sciences publications. The essay offers no citation dataset or time series for that claim. Its student-program figures refer specifically to Wiki Education Foundation-supported work in the United States and Canada since 2010, not to every student contribution worldwide.

“academics have an ethical duty to defend and support open knowledge communities such as Wikipedia”

— Helen Choi, writing for LSE Impact

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Traffic Effects Remain Unquantified

The essay does not provide traffic measurements showing how much visits to Wikipedia have changed because of AI answer products, nor does it isolate AI use from other possible causes of changes in readership. The claim that users often do not click through to linked sources is presented by Choi without figures in the supplied report.

It is also unclear how any change in visits would translate into fewer editors, slower corrections or a decline in article quality. Choi describes these as possible consequences; the essay does not establish that Wikipedia is already on a path to becoming inactive or defunct. The supplied material also ends partway through its discussion of student benefits, so further details in the original argument are not available here.

Choi’s account of companies’ use of Wikipedia content raises questions about authorization and commercial benefit, but the material does not document specific cases of unauthorized scraping or quantify financial effects. Those claims should be understood as the essay’s account, not as independently established findings in the supplied source.

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More Evidence and Contributions Needed

Choi’s recommendations depend on individual and institutional action rather than a single announced policy. Academics can add or improve article content, make transparent citations when Wikipedia informs their work and create assignments in which students contribute under appropriate guidance. The essay points to existing Wiki Education Foundation projects as an example of teaching-based contributions.

The broader questions it raises will require evidence beyond the essay: sustained data on referral traffic and editing activity, clearer information about how AI services access Wikipedia content, and assessments of whether educational contributions remain accurate and useful over time. No formal response from Wikipedia, AI companies or universities is included in the supplied report, and no policy change or follow-up date is announced.

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Key Questions

What is the news in Helen Choi’s essay?

Choi argues that academics have an ethical responsibility to support Wikipedia because AI-generated answers may divert readers from the volunteer-run site. The essay appeared on LSE Impact on Oct. 19, 2026.

How does Choi say academics can support Wikipedia?

She recommends using subject expertise and reliable sources to improve articles, citing Wikipedia transparently when it informs scholarly work, and teaching students to edit entries.

Does the essay prove that AI has reduced Wikipedia traffic?

No. It argues that direct AI answers may discourage users from clicking through to Wikipedia, but the supplied essay material gives no traffic data that measures the effect.

How many students have contributed through Wiki Education Foundation?

The essay says more than 152,000 students at over 8,000 colleges and universities in the United States and Canada have contributed more than 125 million words since 2010.

Is Choi’s call an official academic policy?

No. It is an ethical argument by Choi in an LSE Impact essay. The supplied source does not report a new policy adopted by LSE, universities or academic associations.

Source: hn

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