When people discuss artificial intelligence in science, they typically envision physicists, biologists, or engineers. Humanities scholars – historians, literary critics, philosophers – rarely feature in this representation. Yet, it is within their domain that one of the most intriguing experiments with AI is currently unfolding. Princeton University has found itself at the epicenter of this process.
Where You Least Expect It
Princeton has long been among the world's leading centers for AI research. However, while the primary activity used to be concentrated in the technical departments, this wave has now reached the humanities faculties – not as a gentle ripple, but as a tangible movement.
This isn't about historians suddenly starting to code. Rather, a culture of collaboration is emerging at the university: technical specialists and humanities scholars are working on common problems that neither side could solve alone.
Simply put: AI provides the tools, and humanities scholars ask the questions. This combination is proving to be surprisingly productive.
What Exactly Is Happening in Classrooms and Labs
The humanities work with vast arrays of texts, archives, and historical documents. In the past, a researcher might spend years manually sifting through sources. Now, AI helps manage these volumes more quickly – finding patterns, comparing texts, and identifying connections that would otherwise go unnoticed.
However, this doesn't mean the machine is drawing conclusions for the scholar. Instead, it takes on the routine work, freeing up the human for interpretation. And interpretation is precisely what has been taught in the humanities for decades.
At Princeton, such projects span a wide range of fields: from analyzing historical texts to studying how language and culture influence our understanding of the world. Researchers are asking questions that once seemed too vast for a single project – and AI is helping to at least roughly outline them.
Why Humanities Scholars Are More Than Just «Users»» of AI
There's an important nuance here that's easy to miss. In this collaboration, humanities researchers are not just applying ready-made tools. They are formulating problems that compel AI developers to think differently.
When a historian asks, «Can you trace how the meaning of a single word has changed over three centuries?»» it's a completely different challenge than image recognition or playing chess. Such tasks require AI to work with ambiguity, context, and cultural layers. And it is precisely here that humanities expertise becomes not auxiliary, but guiding.
In a way, humanities scholars are helping AI become a little more human – not in the sense of emotions, but in the sense of understanding how human knowledge is structured.
The Era of Collaboration – It Sounds Grand, But It's True
The word «era»» in this context can easily be seen as an exaggeration. But if you look at what's happening at Princeton – and more broadly, in academia as a whole – we are truly talking about a structural shift.
Previously, the humanities and technical sciences existed in rather parallel worlds. There were few joint projects, the language was different, and interests rarely overlapped. That is changing now. Not because someone decided, «this is how it should be»», but because a common object of interest has emerged – along with tools that allow them to work on it together.
In this case, AI acts not just as a technology, but as a kind of common language – flexible enough for both an engineer and a philosopher to speak it.
What This Means Outside the University
If this collaboration takes root – and at Princeton, it seems it already is – it could change how we think about the application of AI in general.
Right now, most conversations about AI revolve around automation, productivity, and business tasks. That's important. But there is another dimension: AI as a tool for cognition. Not just for completing tasks, but for understanding history, culture, language, and meaning.
And it is the humanities, with their centuries of experience working with these questions, that could become a surprisingly important partner in the development of AI. Not as a consumer of the technology, but as its co-author.
For now, this is more of a beginning than a result. But the fact that such processes are unfolding at one of the world's leading research universities is, in itself, significant.
