What is the relationship between generative artificial intelligence (AI) and the anthropology classroom? How are anthropologists using, refusing, or shaping AI in their teaching? We shared the call for this series in hopes of answering tackling these questions. We wrote the call equals parts frustrated, irritated, and even bored by the mainstream discourse surrounding generative AI and higher education. We hope this series is a collaborative space of knowledge making which moves beyond deciding whether AI is good or bad, to give educators some practical tools with which to confront its forceful ubiquity within higher education and beyond.

We particularly wanted to find a way out of the AI double bind: On one hand, AI boosters claim that AI is an almost utopian inevitability, revolutionizing all aspects of life and fundamentally transforming higher education. From this side, we felt the pressure of a near apocalyptic discourse which suggested any refusal of AI would render the discipline into irrelevance. In this vein any attempt to refuse to use AI was akin to holding back students by denying them the tools of the future. Yet, on the other hand were the critics of AI who seemed to see in students’ booming uptake of generative AI to write papers and complete assignments a kind of death knell of education. The subtext seemed to be that resistance was futile as the end was already here. We understand both “AI good” and “AI bad” views as a version of what technology scholar Lee Vinsel calls “criti-hype”: rhetoric which distracts us from actually existing problems and usages of technologies by instead focusing our attention on problems that might someday arrive. Both these positive and negative renderings of AI felt unsatisfying and did not match our actual experiences, where generative AI was usually more banal or boring than either discourse captures. In other words, we suspected AI to be more mundane tool than revolution.

In contrast to the speculative imaginings of criti-hype, we were hungry to hear what anthropologists were actually doing in the classroom. We had a feeling (and a hope) that the daily practice was nowhere near as utopian or catastrophic as both critiques alleged; moreover, as anthropologists, we were curious about what that daily practice actually was. Perhaps AI was fundamentally less iconoclastic than either its supporters and detractors thought. We drew inspiration from thinkers such as Theodore Kalaitzidis who frame AI not as an outsider destabilizing higher education but rather as revealing pre-existing fault lines: “GenAI does not problematize higher education so much as expose its long-standing distortion of knowing and learning” (Kalaitzidis 2026, 895). We were inspired to approach generative AI not as a threat but instead as an opportunity to refocus on the question of what it means to learn, especially in anthropology. 

Further, these criti-hype approaches didn’t give us the tools we needed to speak honestly about the impact of generative AI on our teaching and our pedagogies in ways which did not shame others. For example, one of the authors had been at a pedagogy seminar where one of the participants spoke up and said they thought AI made them a better writer. The room was silent and awkward in response; everyone avoided eye contact as if an invisible wall had been breached between the majority of the room who had political stakes opposing AI and the participant’s comment. The moment was a reminder to meet students where they are, including in contexts which are overwhelmingly pro-AI, so that restricting or refusing AI use in the classroom is explained and does not implicitly shame them but rather provides an opening into an honest conversation on the limitations, harms, or grounded, realistic possibilities of AI.

The series contributions below thus offer a range of approaches anthropology instructors can use, both engaging with generative AI but also offering strategies for refusing the naturalization of AI within higher education. Even when appearing to have divergent practices and policies towards AI, such as banning it versus using it for an in-class activity, contributors draw on similar tenets of anthropological scholarship; in this sense, we hope the series facilitates instructors’ critical approach to AI, modeling for students how anthropologists study and think critically about any other device in daily life.

The first part of the series shares critical anthropological approaches to AI that go beyond the good/bad binary. We include here two syllabi by instructors who have developed courses on the anthropology of AI. Ted Maclin shows how AI is not an aberration or exceptional product but rather fits into preexisting narratives about technology in U.S. society. Samuel Collins similarly proposes that engaging AI requires engaging complexity rather than dichotomous thinking. Klāvs Sedlenieks discusses how the conflicts generated by the adoption of AI technology mirror earlier debates about plagiarism and academic integrity in Latvian higher education and suggests AI offers an opportunity to move toward new forms of assessment, such as oral exams, even as this also shifts labor burden onto instructors. 

Part two offers a range of ideas for classroom activities using AI. Here, instructors can guide students to learn from AI interaction in perhaps unexpected ways. Amirpouyan Shiva’s classroom activity enables students to engage with generative AI to critique how anthropology has been constructed as a field. Saad Quasem’s contribution adapts a previous activity shaped around Raymond Williams’s 1976 text Keywords, showing a continuum in pedagogical approaches in both AI and non-AI assignments. Sita Venkateswar’s adaptation of AI disclosure forms shows how instructors are rapidly adapting within a shifting educational landscape. Tiatemsu Longkumer has embraced AI in assignments, arguing it allows students to consider more carefully how knowledge is constructed and finding that allowing AI has improved debate in the classroom.

However, we would be remiss to not point to the much broader questions that this collection of entries brings us to. We as editors remain critical of universities’ embrace of generative AI, especially given the costs and environmental harms; we note that several of the series contributions reflect that, despite the fantasy of labor-saving, generative AI, much like many other technologies, merely redistributes and re-arranges labor. We continue to question the opportunity costs of integrating AI and are troubled by the ways generative AI generates suspicion and doubt in what should be a relationship of trust between teachers and students. We reaffirm the importance of anthropology classrooms for inspiring students to learn to think and write critically. It is our ambition that some of these exercises of using AI with students can open students to the limitations of AI and perhaps defang its novelty, cultural power, and supposed efficacy. Maybe then we can talk about something more interesting than AI.