Kate facilitates online courses in our Scholars & Scientists Program, teaching professionals about the culture, policies, and community of Wikipedia and how to make high-quality contributions. This includes our new Documenting America initiative, focused on improving Wikipedia’s coverage of topics related to U.S. history, civics, democracy, and the 2026 midterm elections. (Explore all upcoming editing courses here.)

Kate is a medieval art historian with a PhD and MA from the University of Pittsburgh. She has taught at the University of San Diego, Wells College, and the New York State College of Ceramics at Alfred University in New York. She has also worked at the Metropolitan Museum of Art in New York and the J. Paul Getty Research Institute in Los Angeles. 

Her publications include a co-edited book with Margaret Goehring, Dressing the Part: Textiles as Propaganda in the Middle Ages (Brepols, 2014) and a forthcoming Festschrift, entitled Tributes to Alison Stones—Reading beyond the Page: Medieval Manuscript Imagination through Word and Image (Harvey Miller, expected 2027). Kate’s research has been supported by a Fulbright Fellowship in Brussels and a Kress Fellowship at the Institut national d’histoire de l’art in Paris.

You might already know Kate from her engagement with Wiki Education programs over the past four years.

In 2022, she launched her first Wikipedia Assignment in her course “The Year 1500: A Global History of Art & Architecture” with support from our Wikipedia Student Program, and has since run several successful assignments. She also participated in our Art History Wiki Scholars course and has presented at conferences to share Wiki Education’s programs with other faculty and subject matter experts.

Based in San Diego, Kate and her husband spend time together hiking trails from the coast to the desert or playing fetch at the park with their beloved labrador retriever, Maisie.

And until recently, Rachel appeared as only an unlinked name in Wikipedia’s article for Paul Revere — one of countless women throughout history whose presence on Wikipedia, if it exists at all, is filtered through the men in their lives. But thanks to the expertise and efforts of Mehitabel Glenhaber at the Paul Revere House in Boston, Rachel has a biography of her own on the world’s go-to encyclopedia. 

Glenhaber’s work is part of Wiki Education’s “250 by 2026” initiative, a partnership with the American Association of State and Local History. Together, we brought 259 cultural heritage professionals from around the country into the world of Wikipedia, empowering them to contribute their expertise and collections. (Read more about the 250 by 2026 initiative.)

Across thirteen editing courses offered by Wiki Education, participants added more than 250,000 words to the encyclopedia, improving coverage of history topics, including notable women of U.S. history who either had no Wikipedia article at all or only a short stub.

Rachel Walker Revere was one of these missing women.

For new Wikipedia editor Glenhaber, the appeal of the initiative and its goal directly aligned with the everyday mission of museum work: getting history out of the place-bound building and into the hands of people who may never get the chance to walk through the door.

“I am always excited about ways that museums can share information with audiences who may not be able to visit the museum in person,” said Glenhaber. “At the Paul Revere House, we have such a wealth of knowledge about the life of Paul Revere, his relatives, and his neighbors, and we want to make sure that this information is for everyone, not just people who can make a trip to Boston.”

Before joining the course, Glenhaber knew just how impactful enhancements to Wikipedia can be, not only for the general public but also for historians and museum professionals like them.

“Wikipedia is an incredible resource which reaches so many people,” explained Glenhaber of their motivation to join the course. “I’ve always been grateful to people who contribute to it and help me with my own historical research, so I wanted to help give back to that community, too.”

Inspired by conversations with their course instructor and fellow editors about underrepresentation of women on Wikipedia, Glenhaber decided to add Rachel Walker Revere to the site.

But as with any new Wikipedia article, that meant building a case for their inclusion. Glenhaber had to prove through their sources that Rachel met Wikipedia’s notability threshold and deserved her own standalone biography.

“Writing about Rachel Revere and making the case that she is a ‘notable figure’ was an interesting challenge to me,” said Glenhaber. “Something that’s always interested me about Paul Revere is the fact that he’s actually a very ordinary and unglamorous participant in the American Revolution: he’s a person who delivered messages, illustrated political cartoons, and organized military supply chains.”

Rachel’s story followed the same trajectory, Glenhaber noted.

“Similarly, Rachel Walker Revere was an ordinary and unglamorous participant in the American Revolution: she fed, clothed, and provided childcare for two Revolutionary War soldiers (Paul Revere and his son, Paul Jr.) and helped evacuate her family from a city under siege,” said Glenhaber. “We are lucky to know so much about the lives of these two 18th-century people.”

And it’s this reframing that gets at something bigger than just one person’s biography: The American Revolution is often remembered in speeches and signatures, but was sustained by the quieter, essential labor of figures like Rachel.

“Making sure that her story gets told as well is very important to me,” said Glenhaber. “I didn’t want her to just come up as an unlinked name on Paul Revere’s page, but as an agent of her own.”

Interested in learning how to add your expertise to Wikipedia? Explore Wiki Education’s upcoming editing courses.

Join Wiki Education for the return of our Speaker Series, Wikipedia in the Age of AI, on Wednesday, September 2, at 10 am Pacific / 1 pm Eastern. Panelists Whitney Lew James (University of Notre Dame), Sage Ross (Wiki Education), Max Spero (Pangram AI Detector), and Oliver Wunsch (Boston College) will join moderator LiAnna Davis to discuss what’s at stake as the AI landscape shifts, how detection tools actually work, and what it means for the future of open, reliable knowledge. It’s a timely conversation for anyone heading into a term of teaching, editing, or simply trying to figure out where to find information you can trust. The session includes a live Q&A, so bring the questions you’re already turning over as the term begins. Register for the Zoom here.

Several of our panelists have already written about this topic on the Wiki Education blog:

• LiAnna Davis reflected on the last six months of generative AI and Wikipedia, including how Wiki Education uses AI-detection tools like Pangram to flag content that needs extra scrutiny for verifiability.
• Oliver Wunsch shared a firsthand account of teaching students to spot AI-generated content, including some telling AI-generated campus photos.
• Whitney Lew James discussed why the Wikipedia Assignment is more relevant than ever as generative AI use grows.

There’s no better way to start the academic year than with a frank discussion about the tools already shaping how students read, write, and research. Register today, or sign up for notifications about future editions so you never miss a conversation. We hope to see you there as the new term begins!

Each term, Wiki Education shepherds thousands of postsecondary students from hundreds of courses across the U.S. and Canada through their Wikipedia journey. With over a decade of doing this under our belt, it might be tempting to assume that each term is much like another. While long experience affords us a certain amount of predictive ability, there’s an amazing amount of variation each Fall and Spring. Neither Wikipedia nor higher education are static, and our Wikipedia Student Program reflects the changing nature of these two landscapes.

A given term can be defined in the ways it stood out qualitatively as well as quantitatively. The impact our students have on Wikipedia and the impact Wikipedia has on our students can hardly be summed up in a few numbers, but these figures are impressive nonetheless. This spring, roughly 6,300 students across 344 courses added 4.75 million words and over 50,000 references to English Wikipedia. Their contributions were dispersed over 7,000 different articles, and they collectively created 401 entirely new entries. Their expertise ranged from plant ecology to global poverty studies. 

The above numbers capture the impact our students have on Wikipedia, but the real beauty of this project is that this impact cuts both ways. In examining student and faculty feedback from spring 2026, three words popped up over and over again – responsibility, ownership, and community.

As one student wrote, “I found it interesting how much responsibility I felt while editing the page. It felt as though I was participating in something that could survive me, a team effort that could live for hundreds of years. In other words, there’s a certain sense of community that comes with entering the ‘Wikipedia World.’” 

The Wikipedia Assignment is at once an exercise in individual as well as collective responsibility. It’s a way for students to situate themselves in today’s knowledge landscape.

As one professor remarked, “It taught my students firsthand how to ‘narrate history’ themselves and that anyone can contribute to knowledge production. It gave them a sense of responsibility that extended beyond the limits of our classroom.” 

Despite the fact that no one truly owns a Wikipedia contribution, the notion of ownership came up over and over again in student and faculty feedback. “Students took ownership of this assignment much more than any other assignment I have ever given. They felt like it meant something, and knowing that other people might read it made them feel a responsibility to do it well,” said one professor. 

“I am always amazed how, despite some technical issues that arise each semester I am teaching this class and assigned this project, students develop such a sense of ownership for ‘their’ article. It’s a perfect outward and community-facing assignment,” another faculty member noted. 

Ownership in this context is not about the content itself, but about its quality and caliber. Our students really get it. They know that they don’t own the content they’re adding to Wikipedia. As one student wrote, “Ultimately, editing Wikipedia is a lesson in humility. You write for a platform where the individual author disappears, leaving only the information to stand on its own.” 

Another word that showed up repeatedly was credibility. “I received a great deal of positive feedback from my students,” noted a professor. Over 90% said that the assignment helped them to think more carefully about the credibility and biases of the information they encounter online.

Another instructor wrote, “My students saw themselves as part of a global community of scholars and researchers. The assignment heightened their awareness of how to find credible sources and increased their information literacy skills.”

Information has always been at the heart of the Wikipedia Assignment, but its importance has grown exponentially with the advent of AI. Accurate information is an increasingly scarce commodity, and faculty are seeking ways to help their students peek beyond the curtain. 

In writing about why they decided to adopt the Wikipedia Assignment, one professor wrote that they are trying to “help students to see that knowledge doesn’t fall from heaven or come from an algorithm.” 

The fact that we’re living through an age of information crisis is not lost on our students either. In the words of one student, “This project reminded me of the importance of my role within today’s information landscape. At a time when misinformation and disinformation spread rapidly, this project reaffirmed my belief that I can make an actual impact by bringing factual information to audiences. Whether that is through Wikipedia or journalism, the value of true, factual information has never been greater because it is less available than ever.”

Whether our students add 50 or 500 words to Wikipedia, they leave their mark, and this project leaves its mark on them. As always, our students put it best: “I have made a permanent contribution to human knowledge, and when you think about it that way, it’s really quite profound.” 

Thank you to all of the faculty and students who participated in the Student Program in Spring 2026 — we can’t wait to see what comes next this fall!

Interested in incorporating a Wikipedia Assignment into your course? Visit teach.wikiedu.org to learn more about the free resources, digital tools, and staff support that Wiki Education offers to postsecondary instructors in the United States and Canada. 

But thanks to new support from Every Page Foundation, hundreds of postsecondary student editors working on Wikipedia assignments in fall 2026 and spring 2027 terms will lend their efforts to closing this ongoing gender gap. 

“Significant contributions to the arts and sciences by women span every era and geography, yet are still often deliberately missing or carelessly overlooked in the spaces where people go to learn,” said Dr. Breea Govenar, Executive Director of Every Page Foundation. “EPF is happy to partner with Wiki Education to support students in making a widely accessible online resource more balanced, while also forging valuable research and writing skills that they’ll be able to put to use in any endeavor, even after they leave the classroom.”

Every Page Foundation, a nonprofit organization dedicated to serving as a resource and strategic partner for social and environmental justice by advancing gender equity, has partnered with Wiki Education through a one-year grant to help ensure women’s representation on Wikipedia.

Guided by our resources and staff, higher education students across the country will examine knowledge gaps and biases on Wikipedia, then research and write new content documenting women’s achievements in the arts and sciences as an integrated part of their coursework.

Students will develop lasting skills in research, source evaluation, and writing for a public audience while gaining hands-on experience in knowledge production that extends far beyond the classroom.

We’re grateful to Every Page Foundation for their commitment to this critical work and excited for the impact ahead.

Interested in incorporating a Wikipedia Assignment into your course? Visit teach.wikiedu.org to learn more about the free resources, digital tools, and staff support that Wiki Education offers to postsecondary instructors in the United States and Canada. 

This is one of the many reasons why Wiki Education’s Wikipedia editing courses in collaboration with Rare As One (RAO) are so important. By bringing together experts in rare diseases, we can empower those with the knowledge to fill the related Wikipedia articles with the most exhaustive list of up-to-date sources available.

Our most recent editing course, which ran from October through December 2025, gave more than twenty subject area experts the opportunity to create and expand Wikipedia’s coverage of rare diseases.

This cohort of course participants dazzled during our eight weeks together. Nearly everyone in the course was completely new to editing Wikipedia, and yet this group was able to produce five new articles that did not exist previously. This is a remarkable statistic!  Prior to their contributions, Wikipedia did not offer articles about SCN2A-related disorders, Okur–Chung neurodevelopmental syndrome, and KCNA2-related disorders.

It sounds simple, but just by virtue of having these articles exist on Wikipedia, readers can find reliable sources faster, understand the topic better, and other editors can continue to update the content as new research is published.

And it’s not just about creating new articles. Course participants also improved 40 existing Wikipedia articles, adding high-quality sources to bring additional information to readers.

Edits to the Alström syndrome article, for example, highlight how a subject matter expert can spot content gaps in an existing page. Prior to the editing course, the language of the article wasn’t as sharp, and the sections on “Research” and “Signs and symptoms” did not exist. You can see similar outcomes in the article about Multisystem proteinopathy, CACNA1C (a protein), and Casein kinase 2, alpha 1 (an enzyme). Thanks to the efforts of the brand-new editors, now these vital sections exist, and are backed up by high-quality, reliable sources.

Speaking of sources, our course participants kept busy adding 479 references to rare disease articles across Wikipedia. In a time of AI hallucinations and inaccurate answers, readers can take comfort in the fact that these references are reliably sourced, and these sources back up the facts with precision. In total, the cohort made 685 edits, adding 45.6K words to Wikipedia.

Although these are all impressive numbers, what towers above the rest is the number of views on these pages since the course began: 819,000 page views since the start of the course in October 2025! 

Every little bit of work on these rare disease articles counts in a big way for readers. The cumulative effect of the participants’ efforts shines through when you consider the number of readers on Wikipedia, how LLMs heavily rely on Wikipedia to produce their answers, and how this information can be easily translated into other languages since it is now on Wikipedia. 

Course experiences like this emphasize just how much work is left to do on Wikipedia for rare diseases and beyond. But while there will always be more to contribute, we’re beyond proud to celebrate this successful course and the incredibly meaningful work of its participants.

Teaching is about knowledge acquisition.  AI is a tool for knowledge acquisition. So, why is generative AI the subject of so much angst for me and my students? 

In my undergraduate upper division history classes, the Wikipedia Assignment is a forum to explore how, why, and where we acquire knowledge and, hopefully, to contribute to expanding knowledge available to the public.  It has also become a launch pad for discussions about new and ever-expanding generative AI.  AI is a tool. Wikipedia is an encyclopedia. AI emphasizes process. Wikipedia focuses on content. At their core, both AI and Wikipedia disseminate information and expand access to knowledge. 

Two student training modules “Using AI tools with Wikipedia” and “Large Language Models” on the Wiki Education dashboard address generative AI, explaining Wikipedia’s ban on using AI-generated content, but also opening the door to use AI for inspiration. The materials emphasize partnership, encouraging students to follow instructors’ guidelines and institutional policies to work effectively and productively to successfully publish their work to Wikipedia.  

The two modules quickly became an AI life raft for me and an inspiration for discussions of sources, research, writing, and research ethics in the classroom.  Wiki Education provides ethical guidelines and technical assistance that helped me to ground my thoughts as, along with my colleagues, I struggled to craft AI strategies and adjust assignments and teaching.  This guidance and support also helped me to navigate the waters of new institutional policies, procedures, and guidelines to encourage appropriate uses of AI.  Wiki Education’s videos, examples, policies, and review systems provide guardrails in a supported environment where professional assistance alleviates worry about lack of familiarity or discomfort with emerging technologies.  

This support system allowed me to focus on the aspects of my assignment important to classroom goals and learning objectives rather than on my own anxieties about unpreparedness or uncertainty in detecting or assessing the role played by generative AI in producing specific content. The materials offer simple, but not simplistic, discussions of AI chatbots and explain how Wiki Education uses an AI detector called  Pangram.  The automatic check on students’ work saved me from having to assess and learn how to use detection software.  It allowed me to focus on how, rather than when, to respond to potentially inappropriate use of AI.  Wiki Education communicated potential problems to me and to students, taking away the guesswork.  

While I recognized the piece of mind the Wiki Education resources gave me, I was not immediately aware of how the Wiki Education approach helped forge a community in the classroom, uniting me and the students as fellow travelers exploring the new technology rather than pitting us against each other trying to suss out how the technology could be deployed and what uses of it were appropriate. Headlines decry AI’s “invasion,” blaming the ready availability of generative AI for an increase in cheating, decrease in critical thinking, and cognitive off-load, conjuring dystopian visions of “professor” bots grading work generated by “student” bots.  Classroom discussions revealed that students were often as uncertain, insecure, and anxious as instructors about generative AI’s use.  What I found in the Wikipedia Assignment was a foundation for guided discussions of the use, abuse, or uncertainties of AI usage and practical suggestions for meeting the challenges it posed, not condemnation or unbridled praise of the technology.  

The explanation of LLM’s as “pattern completion programs” explains replications of information and errors in an accessible way, spurring discussion of “loops” of suspect information and engaging students in thinking about how to “set things right” through careful work in original sources and publication in relevant Wikipedia articles. In one class, the examples of AI “Hallucinations” led students into an impromptu competition to find and correct hallucinated content on the internet, learning along the way how information was processed and distorted.  Discussion of LLM’s lack of transparency related to proprietary technology and algorithms encouraged consideration of bias as well as authority and power in the dissemination of information.

Wiki Education’s fact verification messages, sent to both the student and the instructor, allowed us to figure out problems, inconsistencies, and errors in their work together, based not on my critical assessment, but on an “outside” query, presenting things to be explored and explained, not defended.  The process seemed less accusatory and more collaborative.  Why was the writing flagged?  What about the content triggered the review?  As use of the sandbox was optional for my students, in several cases the content reflected traces of use of Grammarly and other writing aids.  In cases where it signaled inappropriate attribution or generated content, the discussion of the “flag” focused on the use of sources and the processes of citation and attribution, rather than on an explanation of what made me suspect the use of AI.  The tools therefore provide greater accountability and verifiability, while at the same time decreasing potential for interpersonal tensions or misunderstanding.

The messages also provide concrete evidence to allow students to voice their anxieties, to share their interactions with generative AI and Wiki Education, and to discuss the benefits and limitations of technology in completing their assignments.  “Hey dude, chill out, I got the same message,” followed by some version of “and this is how Dr. H and I addressed it,” prompted several discussions about how to cite sources, use direct quotes, or incorporate new material.

In my classroom, the Wiki Education materials on AI helped students to collect their thoughts and approach generative AI use from an analytical, rather than emotional, perspective.  Using the materials as a foundation, they acquired a common language to discuss the advantages and limitations of AI and to share how they had used it successfully or unsuccessfully in other classes and in their daily lives.

Interested in incorporating a Wikipedia Assignment into your course? Visit teach.wikiedu.org to learn more about the free resources, digital tools, and staff support that Wiki Education offers to postsecondary instructors in the United States and Canada. 

A few months ago, a student stopped by my office at Boston College to discuss the sources for her Wikipedia Assignment research project. She was enrolled in one of the art history courses in which I ask students to revise and expand a Wikipedia article on an artwork related to our course’s themes. She had taken seriously my requirement that she base most of her research on physical books from the Boston College library, and she proved the point by stacking them on my desk when she arrived to talk. I asked whether the trip to the library had gone smoothly. She replied that it had gone well, even though it was the first time she had checked out any books during her time at college. She then said something that I found puzzling: “I felt like I was in a movie about college.” I asked what she meant, and she pointed out that nobody carried books around campus in real life, at least not anymore. She had only seen it in movies, presumably old or inaccurate ones.

Why do I make my students engage in this antiquated ritual of taking out books from the library? In part, I insist on it because many of the most important art historical sources remain books that, for the time being, are not fully accessible online. But in recent years, with the rise of artificial intelligence, I have come to see this practical necessity as secondary to a deeper goal: to show students that the process of acquiring a piece of information changes its value and meaning; knowledge gleaned from a scholarly book, full of footnotes and supporting evidence, carries a different significance from something generated by ChatGPT.  

It might seem paradoxical that I seek to impart this message while asking my students to write articles for Wikipedia, which has historically served as the preferred information source of underprepared students across the world. When I ask my students at the beginning of the semester what first comes to mind when I say Wikipedia, many of them recall teachers in high school who told them not to use it. Yet Wikipedia’s traditional association with poorly researched papers belies the platform’s sophisticated policies around citation practices and reliable sources. It is these policies that have turned Wikipedia into a powerful ally in my effort to demonstrate to students not only that books still matter in the age of AI, but also that how you learn a fact matters as much as the fact itself.

Among the Wikipedia policies that I have found useful in conversations with students about AI, the most salient one has been the requirement of verifiability. The policy is simple enough to explain to students: a reader should be able to verify that the facts and claims in a Wikipedia article come from reliable sources. Wikipedia editors therefore need to include inline citations to their sources, and when citing books or long articles, they should point readers to the specific page numbers where the information appears. I sometimes expand on the principle by saying to students that every Wikipedia article needs to be easy to reverse engineer. Anyone should be able to figure out how the editors of the article put it together from its source material. Such a principle, of course, runs directly counter to the nature of large language models, whose processes of text generation are hardly transparent. The power and peril of generative AI lie precisely in the fact that, unless explicitly connected to a retrieval system or source database, it does not typically answer questions by gathering information from a specific source; instead, it generates text by predicting likely sequences of words based on patterns in its training data and the prompt itself. This design makes LLMs excellent at producing plausible replies that are very often true yet impossible to verify by tracking information back to its source. For this reason, the logic of generative AI runs counter to the principle of verifiability, and it makes good sense that Wikipedia’s current policies prohibit using LLMs to generate or rewrite article content, apart from limited uses such as copyediting one’s own writing. 

Policies, of course, only get you so far. If students don’t understand the reason for a rule, or if they don’t believe you can enforce it, they are unlikely to take it too seriously. I therefore spend substantial time early in the semester showing students some of the limits of LLMs in generating verifiable research. For example, I recently demonstrated to students how the current version of ChatGPT would respond if I enlisted its help for research on Michelangelo’s Dying Slave, the topic of one student’s Wikipedia article. While ChatGPT provided a competent overview of the sculpture and an impressive bibliography composed mostly of real sources, it faltered when I asked it to connect information about the sculpture to specific sources. Rather than cite the most important books in the bibliography that it had just produced, it instead provided links to various websites, some more credible than others. When I then asked why it had not cited any of the authoritative books on the topic, which offer much more extensive analysis of the sculpture than the online sources, it acknowledged a limitation: “most major Michelangelo books… are not fully readable online, so I can’t responsibly ‘cite’ them.”

I wish I could say that these demonstrations have completely dissuaded students from using AI on the assignment. In truth, I still have several students each semester who turn to LLMs when drafting their articles. Here, I am grateful that Wikipedia allows students to work on drafts in “sandboxes,” where I can typically identify issues before students publish anything to Wikipedia’s public-facing “mainspace.” I also appreciate the Pangram AI detector built into the Wiki Education dashboard, which scans these sandbox drafts and notifies both the student and me of any suspicious text. When I follow up with students about these issues, I do not generally make direct accusations about AI use or seek confessions, especially since no AI detector is 100% accurate. Instead, I focus on the question of verifiability. In my experience, almost all the passages flagged by the AI detector fail the verifiability standard. When I check the sources that the student has cited, I usually cannot find the information in the source, at least not on the cited page. In the few cases where the cited source does correspond to the flagged text, then the source is almost always a website and not one of the more authoritative publications on the topic. I can then remind the student of our conversations earlier in the semester about the importance of verifiability and reliable sources for maintaining Wikipedia’s credibility. This approach serves several purposes. First, it allows me to avoid lengthy, antagonistic debates about whether the student used AI. Second, it ensures that we concentrate on ways to remedy the problem rather than on culpability. And most importantly, it shifts the focus to the underlying issue of what makes writing trustworthy. 

Working through these questions of evidence and credibility with students has had unexpected benefits beyond the Wikipedia Assignment itself. Students often draw upon our Wikipedia conversations about AI later in the semester when we discuss trust and truth in visual representation. Students made those connections this past spring toward the end of my course The Medium Shapes the Message: Materials and Technologies of Visual Communication. In the final unit, we turned to digital images in the age of AI, seeking to establish some techniques for evaluating the veracity of images today. One day, I provided my students with a group of about twenty pictures of Boston College students around campus, half of which came from our campus photographer and the other half of which I generated using AI. When students discussed how they might authenticate the images, one student made the connection to the principle of verifiability: just as we check the credibility of a Wikipedia article by tracing information back to its source, we might verify a photograph by comparing inconspicuous background elements against architectural details or landscape features actually present on campus. 

In that moment, it became clear that the conversation about AI use in the Wikipedia project amounted to something bigger than enforcing an anti-cheating policy. It pushed the class to think much more deeply about the relationship between knowledge, representation, and authenticity. Another student, I should note, drew upon the lessons of the Wikipedia project to arrive at an even simpler means of separating the real photographs from the fake ones: only in the AI-generated images of Boston College students did we see anyone carrying any books, presumably because the AI model had been trained on stock imagery from an earlier era. I imagine the models will catch up soon. Or perhaps, with renewed interest in verifiable sources, we may see a few students walking across campus with books in hand once again.

Interested in incorporating a Wikipedia Assignment into your course? Visit teach.wikiedu.org to learn more about the free resources, digital tools, and staff support that Wiki Education offers to postsecondary instructors in the United States and Canada. Priority deadline for fall 2026 courses: Wednesday, August 12, 2026

Looking Back: 2025–2026

Fiscal year 2025–2026 was a year of navigating profound shifts in the external environment while continuing to deliver strong results across our priority areas. AI’s growing role in how knowledge is produced, interpreted, and shared made this a pivotal year for our mission.

As more students turned to AI tools to draft and revise their coursework, we saw a real risk of hallucinated or unverifiable content making its way onto Wikipedia from our program participants. In response, we built and refined new systems to scan student contributions for likely AI use, developed new training modules and resources to support both students and instructors, and launched a partnership with Princeton University’s Humans and Machines Lab to study how students use AI in educational settings. (Read more about our approach to AI in this recent blog post.)

The focus of our Scholars & Scientists work this year was our 250 by 2026 initiative in collaboration with the American Association for State and Local History, which brought more than 250 historians and cultural heritage professionals to Wikipedia to strengthen coverage of American history ahead of the nation’s semiquincentennial. We’re absolutely thrilled with the impact of this effort. Our Scholars & Scientists Program also continued to support subject-matter experts in improving Wikipedia on topics ranging from disability and environmental justice to rare diseases and public health.

Looking Ahead: 2026–2027

In the coming fiscal year, we’ll focus on strengthening our impact while continuing to navigate an information ecosystem being reshaped by artificial intelligence. That means maintaining rigorous quality standards to protect Wikipedia’s reliability while also exploring constructive uses of AI, including tools that could help identify inconsistencies between article content and sources.

A flagship initiative for our year will be Documenting America, a new collaboration with the American Association for State and Local History that builds on the success of the 250 by 2026 initiative. The project will engage subject-matter experts to strengthen Wikipedia’s coverage of state history, expand general American history content, increase the number of historical images on Wikimedia Commons, and address gaps in content related to civics and democracy.

In the Wikipedia Student Program, we’ll continue our Knowledge Equity initiative, including expanding outreach to humanities instructors. We’ll also keep working to improve Wikipedia’s coverage of women and people of color in STEM, and roll out new programmatic support, including a streamlined assignment option and expanded workshops for faculty new to the project.

On the technology side, we’ll deepen our collaboration with academic researchers studying AI’s effects on Wikipedia content, launch our first Canvas integration for the Dashboard, and continue exploring how AI coding tools can help us build and maintain our technical infrastructure.

The Wiki Education Board of Trustees approved our new annual plan during their June meeting. 

We thank our Board, program participants, funders, partners, and global community for their continued support of Wiki Education and our shared goals. Here’s to the year ahead!

For the complete 2026–2027 Annual Plan, please visit wikiedu.org/annual-plan