AI and Anthropological Pedagogies Symposium

4 and 5 June 2026

The aim of this online symposium is to provide a space for teachers and students of social anthropology to share their experiences with regards their practical pedagogic engagement with AI.

While universities worldwide scramble to catch up with developments in AI, what is happening in anthropology classrooms? How are teachers and students of social and cultural anthropology discussing, engaging with, or boycotting AI agents? What, if anything, are anthropology departments doing differently in the wake of generative AI? Are they preparing their students for a radically altered workplace and if so, how? And how might anthropologists contribute to broader emerging debates on AI in higher education?

Addressing these urgent questions will illuminate diverse anthropological pedagogies, particularly around how to balance the critical and intellectual value of anthropology with its potential practical and public contributions. As teachers, we may want to help our students be effective critical contributors to public debate about AI. But should we also give them tools to engage AI in practical, reflexive and anthropologically relevant ways? How and where exactly – in workplaces, activist organising, or policymaking and regulatory initiatives?

The need to assess the state of play and share best practice has become urgent. To do our best by our students, we need to draw on the collective wisdom and experience of the global anthropological community.

Contributors will debate how best to teach anthropology post-AI in light of students’ diverse trajectories and professional lives. They will explore how they are identifying and communicating anthropologically specific AI skills in concrete ways, and they will reflect on the distinct challenges posed to anthropology teaching and learning around AI in different national, regional and local contexts.

(All timing in British Summer Time, UTC+1)

Day 1 – 4 June 2026

9-9.30am: Introduction

Dr Lenia Kouneni, Associate Dean Education, Teodor Zidaru, Paloma Gay Blasco and the AnthroAI Working Group

Department of Social Anthropology, University of St Andrews

9.30-11am Session 1: Ethical dilemmas

Convener: Dr Mattia Fumanti, Department of Social Anthropology, University of St Andrews

1. Everyday Ethics and Epistemic Drift: a reflective content analysis of 1400 student declarations of ethical AI use in a high-stakes first year health sciences paper.

Ruth Fitzgerald, Professor of Social Anthropology, University of Otago, Dunedin, New Zealand.

Eléna Chetland de Vries, Geddes Summer Student, Social Anthropology Programme, University of Otago, Dunedin, New Zealand.

This paper draws on self-reflections of a teacher and research assistant on the thematic analysis of anonymised responses from 1400 students on their views of the ethical use of generative AI in their creation of a medical anthropology-based assignment. The data stems from their mandatory 300-word declarations explaining their learning practices for the essay development.

The course is one of several papers offered at the university in a suite of introductory health science papers which are both high stakes and highly competitive, serving as the gateway for students for later entry into coveted professional programmes such as dentistry and medicine.  Medical anthropology taught in this heavily regulated and highly competitive environment encounters significant epistemological tensions – particularly its emphasis on the development of student’s critical thinking rather than solely rote learning (the more customary approach in first year health science papers).

The essay itself thus becomes one of these points of epistemological tension as this assignment is ‘ripe’ for students to game the most effective way to achieve the desired outcome of high marks by making unethical uses of AI.

As teachers, we have chosen to embrace this complexity, knowing that students will progress into professions such as medicine where AIs are routinely being used.  We thus encourage our students to strengthen their capacities for self-reflection and self-education in their ethical uses of AI. This approach is not without dilemmas. For example, we have uncovered deficits in understanding of the meaning of ethical practice in academic life beyond its strategically calculated ‘performance’ in a declarative statement and a lack of provision by the university on any bridging material that teaches the elements of moral reasoning and its lived experiences.  There is also the issue of understanding intentions when the reflective statement is itself perhaps AI generated.

2. Cripping Anthropology in a time of Artificial Intelligence

Sage Wilson, Undergraduate Student, Department of Social Anthropology, University of St Andrews.

Alyssa Morgan, Undergraduate Student, Department of Social Anthropology, University of St Andrews.

Bridget Bradley, Lecturer in Social Anthropology, Department of Social Anthropology, University of St Andrews.

This paper explores the relationship between AI technologies and disability access within and beyond the anthropology classroom. Written from the perspective of both teacher (Bradley) and student (Wilson), we use our recent experiences from the module ‘Crip Anthropology: Disability and Difference’ as the starting point to reflect on the process of navigating AI in the classroom. We describe the process of turning an institutional requirement – the class AI statement – into a pedagogical opportunity to debate the benefits and risks of AI in our teaching and learning. The outcome was a collaborative AI statement, written as a class to reflect the different needs and views of students. This exercise recognised students’ agency in their own learning, and aimed to create an environment based on trust, transparency and interdependence. The activity included a lively debate about how AI is shaping our experiences of learning, and how these technologies are affecting disabled (and other marginalised) people around the world. Our paper situates this unique teaching example within wider scholarship on crip authorship, anti-ableist methods and disabled activism within anthropology to show the importance of involving diverse perspectives in discussions and decisions relating to AI and disability.

Break: 11.00-11.30pm

11.30-1.30 Session 2: AI in the classroom: grasping the nettle

Convener: Dr Paloma Gay Blasco, Department of Social Anthropology, University of St Andrews

3. Designing AI Pedagogies: Student-Led Experiments in Anthropological Teaching

Suzana Jovicic, Post-Doc, University of Vienna, Department of Social and Cultural Anthropology.

Sofie Kronberger, Junior Research Fellow, University of Arts.

Michael Anranter, University Lecturer, University of Vienna, Department of Social and Cultural Anthropology.

With new digital tools emerging almost daily and reshaping how we conduct research and teaching, teachers and students increasingly find themselves navigating the same shifting terrain. This raises two key questions: (1) how can we make sense of this “mess” not only in theoretical and methodological terms but also in pragmatic ones, and (2) how might we engage with these unavoidable and disruptive developments to address long-standing challenges in anthropological teaching (Pfeifer & Jovicic)?

To explore these questions, we – three anthropologists based at the University of Vienna – designed and co-taught a course on AI in anthropological research and teaching. The course unfolded along two main dimensions. The first engaged with AI theoretically, discussing ethical and environmental considerations, various aspects of friction in AI-assisted research, and the broader implications of AI’s growing use. This strand was paired with an explicit pedagogical commitment to open, eye-level discussion and mutual learning between instructors and students. The second dimension emphasized student engagement through practice: participants developed didactic modules exploring how AI can be used in relation to various aspects of anthropological inquiry, from literature review to research design. Over the course of one semester, students worked in design-thinking-inspired teams to create innovative exercises that placed student perspectives at the centre of rethinking anthropological pedagogy in the age of generative AI. The exercises did not aim to legitimize AI as a default tool; instead they created structured occasions for judgement-provoking critical reflection on when, why, and how AI might be appropriate – or not – to use in research contexts. The key outcome of the course was not a single “best practice,” but a portfolio of situated pedagogical experiments that render the terms of AI use – its limits, frictions, and responsibilities – explicit, contestable, and teachable in the classroom.

References

Pfeifer, S., Jovicic, S. (2023). Co-Teaching Post digital Ethnography, Working Paper Series

Media of Cooperation. Collaborative Research Centre Media of Cooperation.

4. From Clinical to Synthetic Gaze: Experiential Learning and the Anthropological Witness in the AI Era

Begüm Ergün, PhD Candidate, Boston University Department of Anthropology

As AI models become capable of generating images of human suffering, anthropology instructors face an urgent epistemic challenge: What is the distinct value of the human ethnographic eye? This presentation details a pedagogical strategy that amplifies the necessity of human experience and the situated nature of ethnographic fieldwork by putting the “synthetic gaze” of AI in conversation with the “clinical gaze” of medicine.

The pedagogy centres on a group assignment in an undergraduate medical anthropology course where students produce photo journals documenting local realities of health inequality and illness. These human-authored visual data are then contrasted with AI generated imagery prompted on identical topics during a class workshop. This friction between the “lived reality” captured by the student and the “probabilistic output” of the AI serves as a practical laboratory for theoretical analysis. This approach moves beyond simple prompt engineering; it equips students with the ability to articulate the “epistemological gaps” in AI systems.

Crucially, this exercise anchors the critique of AI in foundational medical anthropology theory. We draw parallels between the “synthetic gaze” of AI and the “clinical gaze”—an ostensibly neutral observational mode that often obscures structural violence and social reality (Beagan 2000; Davenport 2000; Holmes 2012). By connecting algorithmic outputs to literature on how the “neutral” gaze in medicine causes harm (Martin 1991), students learn that AI is not a neutral tool but a biased observer similar to the biomedical institution itself. The “synthetic gaze,” like the clinical one, sees the condition but misses the person. This pedagogy demonstrates that in an era of automated knowledge, the anthropologist’s distinct skill is not merely data collection, but the ability to bear witness to the emotional nuance that the synthetic gaze inevitably overlooks, and to challenge the “language of neutrality” by validating the messy and embodied realities that algorithms sanitize.

5. Cultivating Anthropological Thinking in the Age of Generative AI: An Inquiry-Based Teaching Experiment

Bixie Lacoste, PhD student, Lecturer, Université de Montréal

This presentation reports on a teaching experiment in two first-year undergraduates anthropology courses (Major Ethnological Theories and Kinship and Neo kinship) structured using an Inquiry-Based Learning (IBL) approach. Students were positioned as novice researchers developing the capacity to ask questions, mobilize theoretical frameworks, and analyse ethnographic and conceptual material through guided inquiry. Generative AI was integrated as a scaffolded support rather than an epistemic authority or writing engine.

Students were trained on research-oriented AI tools, including Consensus and NotebookLM, to clarify concepts, map authors and debates, and structure notes. Instruction emphasized epistemic vigilance, hallucination detection, verification practices, and recognition of AI’s limits in interpreting socially and culturally embedded meanings.

To ensure authentic learning and maintain intellectual authorship, key assessments—including reflective journals and exam essays—were completed by hand, in class, with no grading penalties for spelling or grammar. Peer-evaluation mechanisms reinforced accountability and critical engagement, requiring students to assess each other’s reasoning, participation, and interpretive accuracy.

In the Kinship and Neo kinship course, an autoethnographic kinship exercise served as an anchor against invented or AI-fabricated narratives. Students worked from their own family structures and lived relational contexts, ensuring that inquiry remained grounded, reflexive, and ethically situated. This design prevented content fabrication while deepening awareness of cultural norms, embodied histories, and variation in kinship systems.

Across both courses, the combination of IBL, structured AI literacy, handwritten assessments, peer-evaluation, and autoethnographic grounding fostered epistemic independence, theoretical comprehension, and critical reflexivity. This case suggests that pairing inquiry pedagogy with guided AI use and embodied assessment practices offers a strong model for cultivating rigorous, autonomous thinkers in the age of generative systems.

Break: 1.30-3.00pm

3-5pm Session 3: Learning anthropologically

Convener: Prof Adam Reed, Department of Social Anthropology, University of St Andrews

6. Learning with and learning about ChatGPT: legitimate peripheral participation and meta personal ethnography

Benjamin Knight, Undergraduate Student, Department of Social Anthropology, University of Cambridge

Matei Candea, Professor of Social Anthropology, University of Cambridge

What or whom are we talking to when we talk to a chatbot? At what point does a conversation with a Large Language Model become an ethnographic encounter, and with whom or what? This paper reflects on two years of a multi-stranded engagement with llms as both objects of study and interlocutors, even ‘collaborators’ of a sort. Through a combination of a personal interest in open-source software and an institutional role demanding a regulatory engagement with ai in my university, I have spent the past two years delving deeper and deeper into the world of large language models. Rather than simply learning about llms as a technical object, I found myself learning about, with and through chatGPT and other large language models as conversational partners – idiosyncratic, problematic, tricksterish, and engaging. Retrospectively recasting this experience in ethnographic terms, the paper probes the boundaries between online ethnography and conversations with a metapersonal entity like chatGPT. How does learning with and from chatGPT resemble or differ from learning with and through online communities, forums and blogs? Revisiting Lave and Wenger’s anthropological theories of ‘legitimate peripheral participation’, the paper also asks how the increasingly ubiquitous LLMs we are all learning with and learning through transform, reconfigure or dissolve other pedagogical communities of practice. While the risk of LLMs ‘deskilling’ their users is already widely acknowledged, this paper spotlights a slightly different risk: the gradual dissolution of human communities of practice (learning peers and teachers) who provide, not only access to transferable skills, but also a disciplinary identity and a collective.

7. Help, Anthropology Needs Somebody: Critiquing AI Pedagogies and Transforming Anthropological Curricula.

Maximiliano Albornoz Torres, PhD researcher at Eötvös Loránd University and University of Buenos Aires (TáTK-ELTE/FFyL-UBA)

In an era where artificial intelligence reshapes human experience, anthropological education must evolve beyond traditional boundaries to critically engage with technological disruption. This presentation analyzes curricula at the University of Buenos Aires (UBA), my alma mater, and my current PhD programme at Eötvös Loránd University (ELTE) in Hungary, benchmarking them against the top 50 QS-ranked anthropology programs worldwide, such as Oxford, Harvard, and ANU. A preliminary data exercise reveals stark disparities: UBA’s Anthropological Sciences degree mandates broad foundational courses across social/cultural anthropology, archaeology, biological anthropology, and linguistics, but dedicates under 5% of credits to digital or AI-related topics (2 out of 40 courses). ELTE’s Cultural Anthropology MA prioritizes critical thinking, interpretative methods, and ethical fieldwork for intercultural contexts, yet similarly allocates minimal space (1 out of 12 courses) to tech integration. Top QS programs, by contrast, embed interdisciplinary modules on digital ethnography and AI ethics, averaging 15-20% of curricula.

This gap exemplifies Tim Ingold’s critique in Anthropology: Why It Matters, urging anthropology to transcend ethnography as mere practical documentation or an “encyclopaedia of cultures.” The discipline’s four fields offer a macro framework to dissect AI’s profound shifts, drawing on Yuk Hui’s cosmotechnics and Eric Sadin’s analyses of AI’s power-truth regimes that automate vital, labour, and intimate functions, eroding conventional notions of the “human.” Algorithmic biases, intertwined with burgeoning behavioural sciences, deepen structural inequalities within digital ecosystems that induce and transform conduct. Echoing my research on erotic content consumers, platforms’ algorithmic curation subtly moulds intimate desires, normalizing automated behavioural nudges much like those in AI pedagogies highlighted by the symposium. To counter this, I advocate revitalizing ethnography not as neutral chronicle, but as situated, emotionally embedded writing that denaturalizes our entanglements with technology critical awareness of how automatisms reshape us. Anthropology must humanize AI while rigorously critiquing it, refusing to merely train LLMs but instead transforming their biases, and imparting this critical capacity to students for more equitable technological futures.

8. What are we using when we use generative AI? Anthropological pedagogies for generative technologies

Heikki Wilenius, Postdoctoral Researcher, University of Helsinki

This contribution draws on a spring 2026 seminar in which a group of doctoral students and I explored the use of large language models in ethnographic research. I will explore three interconnected themes: the political economy of generative AI, the ethics of deploying AI tools for data collection and analysis, and the new commodity chains that anthropological work becomes entangled with when researchers adopt these technologies.

Drawing on research into “ferality” in computing and landscapes, and studies of frontier dynamics, I argue that anthropology offers students tools for understanding large language models not as bounded technical objects but as enmeshed in labour relations, data extraction regimes, and contested sovereignties. A central capability we cultivated in the seminar was distinguishing between open and closed large language models, and how that distinction shapes the ways in which models can and should be used for anthropological work. We also traced the hidden material and labour infrastructures behind the production of generative AI and examined the “weedy proliferation” of open-weight models through frameworks of gift exchange and commoditisation. Finally, the practical part of the seminar involved the students working with my personal GenAI research assistant – a system conversant in anthropological literature and capable of accessing online spaces. Through this exercise, they gained first-hand experience in assessing the ethics and practicalities of conducting AI-assisted ethnographic research.

The presentation reflects on what happened when these analytical frameworks were combined with the practical exercise and students’ assessment of it. I discuss the pedagogical insights that emerged as participants navigated questions of data sovereignty, algorithmic opacity, and the ethical responsibilities that attach to new technological dependencies, in relation to the conventional positionality of a human ethnographer. Based on my previous teaching experiments, I suggest that equipping students to engage large language models requires sustained, collective experimentation with the tools themselves alongside critical analysis of their conditions of production.

Day 2 – 5 June 2026

9-11am Session 4: Student engagements with/around AI

Convener: Dr Huon Wardle, Department of Social Anthropology, University of St Andrews

9. Authority Without Evidence? Teaching Knowledge Production Through GenAI in Anthropology

Dr Tiffany Cone, Assistant Professor of Social Sciences, University of Doha for Science and Technology

AI generated text has a tendency to produce generalised, decontextualised and authoritative claims without valid (or even real) evidence. Anthropological texts, on the other hand, emphasise contextual specificity, reflexivity and interpretive ambivalence. These differences present a unique case for students to examine knowledge production and implicit ontological and epistemological assumptions. This paper discusses a re-designed assessment in an undergraduate anthropology course in which a traditional research paper assignment was replaced with a literature critique. Students were given two sample texts on a specific topic. One text was entirely AI-generated, and the other was an excerpt from a peer reviewed journal article published in an anthropology journal. Students did not have access to any AI tools and could only input a response in a locked text book on their devices. Students were asked a series of prompts related to the texts: 1) How do the texts construct social reality? 2) How do the texts assert epistemic authority? 3) How do the texts use evidence? 4) How do the texts manage uncertainty? In this paper, I elaborate on patterns in students’ responses and consider what they reveal about teaching epistemology, authority, and generative AI in anthropology.

10. Uncertain Experiments in the Age of AI: Teaching-Led-Research Collaborations

AnthroAI Team

Universities are abuzz with talk about generative AI technologies in education and research. So much so that AI has arguably become a master symbol in higher education institutions. Universities seek to market themselves as providing AI-responsive education.

Educators disagree over the specifics of post-AI teaching, learning and assessment. Students worry about AI’s environmental impact, academic misconduct and their future employment in the AI-dominated workplace.

Many of these concerns are couched in the register of certainty: certainties about AI’s negative cognitive, ecological or employment consequences; or certainties about AI as unlocking opportunities for innovation. In contrast, we emphasise the generative value of uncertainty as a recognition of future contingencies and indeterminacies that not only side-steps narrow, normative conceptions of inevitable futures but also galvanises new forms of collaborative action between unlikely parties.

Our broad argument emerges from our own unlikely collaboration as undergraduate students and educators. Out of this collaboration emerged a gamut of experiments in post-AI anthropological pedagogy as well as a broader student-staff working group (AnthroAI) currently undertaking research on student use of AI and post-AI employability strategies for anthropology students.

11. Anthropology Teaching in the Context of Generative AI: Classroom Experiences

Miklós Szabó, Associate Professor, ELTE Eötvös Loránd University, Faculty of Social Sciences, Department of Minority Studies

While universities worldwide are revising policies and assessment practices in response to generative AI, relatively little is known about how AI is discussed, experienced, and pedagogically incorporated in everyday anthropology teaching. This presentation draws on three consecutive semesters of AI-assisted teaching in social and cultural anthropology, based on the use of a course-specific retrieval-augmented generation (RAG) AI tutor in a BA level introductory Cultural Anthropology course (over three semesters) and in MA-level anthropology seminars.

Rather than treating generative AI primarily as a problem of academic integrity or efficiency, the teaching experiment approached AI as both a pedagogical tool and an object of anthropological inquiry. The AI tutor was embedded in curated course materials and used as part of routine teaching practice, allowing students to engage with anthropological texts through dialogue. Anonymised interaction logs, classroom observation, and student focus groups were used to explore how students and instructors interpreted and negotiated issues of trust, authority, and interpretation when working with an institutionally provided AI system.

Across undergraduate and graduate contexts, the use of the AI tutor did not replace reading, interpretation, or classroom discussion. Instead, it appeared to support particular forms of engagement with anthropological theory. Many students used the system as a conversational aid for clarification, reinterpretation, and comparison, rather than as a simple reference source. These practices point to a set of anthropologically relevant skills exercised in AI supported learning, such as reflexive questioning, interpretive comparison, critical engagement with sources, and situated assessments of credibility. The presentation also reflects on practical constraints encountered during the teaching process, including uneven AI literacy among students, institutional uncertainty, and disciplinary concerns related to authorship and originality. Rather than offering a model or set of best practices, the contribution aims to share situated experiences from anthropology teaching, contributing empirically grounded observations to ongoing discussions about AI in higher education.

Break: 11-11:30am

11.30-1.30 Session 5: Institutions, value, values

Convener: Dr Bridget Bradley, Department of Social Anthropology, University of St Andrews

12. AI and Anthropological Pedagogies in the Italian Context: Teaching Critical Engagement Across Educational Levels

Elisabetta Di Giovanni, Associate Professor of Anthropology, University of Palermo (Italy) Antonio Fundarò, Teacher School, Italy

This contribution examines the challenges and opportunities of teaching AI-informed anthropology in the Italian education system, based on a collaborative experience between a university anthropologist and a school teacher expert in AI. Our collaboration addresses a critical gap: while Italian institutions are rapidly implementing AI systems for citizen services, migration management, and social welfare, there is minimal anthropological engagement with these transformations. This pedagogical intervention responds to both educational demands and ethical imperatives, drawing on Pink et al.’s (2022) framework for digital anthropological methods and Eubanks’ (2018) critical analysis of automated decision-making in welfare systems. The Italian context presents distinct pedagogical opportunities: bridging university and secondary education allows students at different levels to develop anthropologically-informed AI competencies that could meaningfully shape their future engagement with vulnerable populations and institutional practices. The Italian context presents distinct pedagogical opportunities: bridging university and secondary education allows students at different levels to develop anthropologically-informed AI competencies that could meaningfully shape their future engagement with vulnerable populations and institutional practices, particularly within European frameworks of migration governance (Ponzanesi 2019; Leurs & Ponzanesi 2024) and algorithmic profiling (Barassi 2020, 2022).

Our pedagogical approach balances three dimensions informed by critical algorithm studies (Noble 2018; Benjamin 2019): (1) critical analysis of algorithmic bias in institutional services, particularly regarding migrants and marginalized communities; (2) practical skills in ethnographic prompt engineering and qualitative data analysis using AI tools, following Boyer’s (2023) call for “infrastructural literacy”; (3) reflexive understanding of how AI reshapes anthropological research and teaching practices across university and secondary school contexts.

Students at both educational levels learn to identify how AI systems reproduce structural inequalities, to design culturally sensitive AI applications, and to conduct AI-assisted qualitative research while maintaining ethical rigor, positioned as critical mediators between technological implementation and social justice. This cross-level collaboration develops what de Sousa Santos (2014) terms “epistemologies of the South” for the algorithmic age, centring marginalized knowledges in technological contexts within Italy’s stratified educational landscape.

13. AI and the pedagogy of care in anthropology: notes on the subsumption of academic labour 

Mariya Ivancheva, Senior Lecturer, Strathclyde Institute of Education, Scotland.

This paper examines the integration of artificial intelligence (AI) into academic labour, addressing the challenges within the higher education sector subsumed under capital (in general) and anthropological research and teaching (in specific). Under the claim of enhanced efficiency, academic institutions increasingly deploy machine learning and natural language processing-generated AI technologies to streamline recruitment, admission, and performance evaluation processes, as well as to customize teaching materials and assessments. AI’s (costly) ‘purchase’ comes both as a tool for administrative convenience and an instrument of surveillance and standardization, but as other recently embraced new technologies, it introduces serious concerns regarding discipline, control, and transformations of academic labour.  The potential for AI to infringe on academic freedom, intellectual property, and worker rights through intensified monitoring and profiling activities reflecting and reinforcing intersectional inequalities are all critical concern. Yet, employing a materialist lens, the paper shows venues overlooked by most emergent academic literature on the subject. On the one hand, the enormous surplus extraction and value capture in higher education through formal and real subsumption of academic labour into segmented tasks that can be easily automated and outsourced, casualised, deprofessionalised and disembodied. This becomes especially problematic in cases of disciplines as anthropology where teaching and research both happen through longer-term, non-linear, and deeply embodied practices of learning and knowledge transmission. On the other hand, I discuss how traditional hierarchies, rituals, temporal and spatial dimensions of scholarly learning and research, to which anthropology is no stranger, make it particularly susceptible to such changes. I thus outline potential venues for research and resistance, with focus on anthropological training and research, taking into account new vantage points of capitalist capture and their strategic choke points. 

14. Ethnography of an anthropology classroom: Ai, teaching relationships and hierarchies

Mateo Forestier, Université libre de Bruxelles

This contribution investigates the ways in which the recent deployment of artificial intelligence and various forms of virtuality within universities participates in the reconfiguration of the social and symbolic structures of higher education institutions. How, indeed, is knowledge distributed when it becomes an exchange value in the form of attentional capital, and when virtuality and its artificial intelligences are engaged in an overt struggle on this academic “attention market”?

Drawing on two years of ethnographic fieldwork conducted in humanities and social science courses at the ULB, this paper analyses the implications of the relationships forged between humans and machines that carry virtual spaces, such as video projectors, computers, smartphones, and related devices. Conducted from a dual positionality, as both a student enrolled in an anthropology programme and an ethnographer undertaking a master’s research project, the fieldwork consisted of participant observation in classrooms, as well as semi-structured interviews and informal discussions with students, lecturers, and, more occasionally, administrative staff, both during and outside class time.

What becomes of the value or the feeling of student work, or of teaching itself, when artificial intelligence, sometimes promoted through internal lobbying, can claim to produce work of equivalent or even superior quality to that of humans? It is here that the limits of the academic form, as well as the limits of ethnographic practice itself, begin to shift, caught in a seismic upheaval whose contours I seek to trace as they are continually reshaped by software updates, collective awareness, legal frameworks, and the very computational codes of artificial intelligence

Break: 1:30-3:00pm

3-4.30pm: Open discussion