In an era where artificial intelligence appears poised to reshape every facet of academic life, historians are confronted with both unprecedented opportunities and pressing challenges. Yet, precisely because of the scale of the potential disruption (as well as the attendant capital-driven hype around it) working to develop nuanced, technologically-savvy and situationally specific response is of more importance than ever. This session—featuring roundtable participants Louis Hyman, Jacob Bruggeman, Samuel Backer, Steven Mintz, and Jo Guldi—attempts to do just, avoiding large-scale pronouncements in favor of a critical examination of the possibilities and limitations of AI in the history classroom. Drawing from candid reflections on pedagogical experience, the history of computing, and the theory and practice of machine learning, our panelists explore how AI tools can simultaneously illuminate and complicate students’ relationship to the study of the past.

The panel opens with an exploration of AI’s transformative potential: How can digital tools such as large language models, coding-based statistical analysis, or data visualization platforms augment our pedagogical practices? Panelists discuss the promise of these technologies to democratize access to archival materials, facilitate research-as-teaching methods, and foster dynamic classroom experiences that engage students in critical thinking about both the historical record and the technological present. This discussion aims to blend theory with a pragmatic analysis of disciplinary limitations—how much do historians need to learn about these technologies in order to use them effective? How can they most effectively react to what often feels like a dizzying rate of change?

Crucially, this panel will suggest that AI is not a silver bullet—its adoption demands rigorous scrutiny, transparency, and a commitment to methodological soundness. Balancing enthusiasm with caution, our speakers will highlight the inherent tensions between traditional historiographical rigor and the algorithmic shortcuts offered by AI. They probe questions of ethical responsibility: How do we navigate issues of bias, accuracy, and context when historical narratives are increasingly mediated by machine-generated interpretations? Rather than succumbing to a passive reliance on technological tools, our panelists advocate for an active, reflective engagement with machine learning. They will discuss their experiences of challenging students to use AI without succumbing to the “just-press-play” logics of its most popular incarnations and share their approaches to building courses around the forms of technical instruction necessary to do so. Ultimately, such historical work pushes students to better understand how AI can (and can’t) work for them, both in the classroom and the world beyond it.

Such a discussion challenges us to reimagine the role of historical education in the digital age. It proposes a model where historians not only leverage AI to uncover hidden patterns and voices in the past but also interrogate its limitations, not merely preserving the craft of historical interpretation in a rapidly changing academic landscape, but actively advocating for its continued intellectual importance. Join us as we navigate this frontier—balancing innovation with introspection, and ensuring that the tools we adopt serve to enrich, rather than dilute, our collective pursuit of historical truth.