Yes, the AI is jammed in there. 

This is the May 2026 edition. It is interesting to see how our views develop on these matters. I know mine have shifted.

Frontier AI’s has created a great deal of cultural instability, particularly in relation to higher education. Gen AI fundamentally undermined how students learn and faculty understand their students’ learning. Gen AI also undermined the viability of academic research across disciplines. Nevertheless, the push to incorporate AI into education is undeniable. The assertion is that AI outputs will be/are “best” communications. That computational methods will be how we know, and their operational capacities will expand, but also determine, what can be known. 

It’s a fundamental epistemological problem. If we cannot accept AI output as accurate or true, then how do we deal with its convincing verisimilitude swamping human communication? if we must accept AI output as true, then whose truth is it? How is that acceptance realized? Through what institutional means?

This is in turn a communicational/pedagogical problem. If we accept AI output as accurate or true, then is interaction with an AI sufficient for learning? What if we told this story? Once upon a time, students (humans) needed to learn to think by interacting with text, then later they needed to learn to think by interacting with machines (computers) and now they just need to learn to think by interacting with AI? If so, can we turn over all communication to machines? Can stop fussing about our communicating human to human? If we reject these conclusions, do we do so from irrational anthropocentrism? Or can we recognize a communicational or epistemological insufficiency in machine-human communication and interaction?

Though I know many of my colleagues across disciplines wouldn’t use these precise terms, I think we all recognize that our disciplines are always in negotiation—both internally and in relation—over what can be known, how it can be known, and how that knowledge can be best communicated and/or taught (not an AI em-dash by the way, too inelegant). These are not questions that began with the arrival of frontier AI. These are the questions whose considerations shape disciplinary practice. This is a conventional, Kuhnian understanding. 

On university campuses like mine, rather than leaving these questions among disciplines, there is an institutional-administrative effort to impose answers. As with the corporate owners of frontier AI and nation-states, universities understand the power, prestige, and wealth that might accrue from answering these questions in particular ways to the diminishment, if not exclusion, of others. I can’t imagine anyone who works at a university would be surprised by such behavior. 

Despite such pressures, questions remain; they may even harden. So what occurs beyond the computer scientists and mathematicians building a truth machine and the social scientists and other human managers developing policies to put those truths to work on society?

Often these faculty are scattered among other existing departments, which is the case at my own institution at least on the humanities side. Media Study is one locale but most of the faculty doing this work are in other arts and humanities departments. “AI arts and humanities” is already digital humanities, media studies, digital/media arts, etc. 

So what kinds of this might we discover outside this space? Many, many things. Here is a list of some of them. I should note that the list is not , nor do I mean to suggest any sense of ownership or territorial claim over these contributions these disciplines, fields, and methods. My point is additive. 

Humanities disciplines

Discipline	How it contributes to AI studies
Philosophy	Ethics of AI, philosophy of mind, agency, personhood, consciousness, epistemology, logic, explanation, responsibility, autonomy, and machine intelligence.
History	Histories of computation, cybernetics, automation, information theory, labor-saving technologies, bureaucracy, surveillance, scientific instruments, and technical imaginaries.
Literary Studies	AI-generated text, authorship, style, narrative, creativity, interpretation, reading practices, computational poetics, and the cultural meaning of “machine writing.”
Rhetoric and Composition	AI writing tools, persuasion, authorship, audience, human-machine communication, prompt practices, academic integrity, and the changing conditions of writing instruction.
Classics	Long histories of automata, artificial life, myths of animated objects, ancient computation, rhetoric, classification, and textual transmission.
Art History	Machine vision, generative art, image databases, visual culture, aesthetics, authorship, style transfer, and the politics of classification in visual systems.
Media Studies	Platforms, interfaces, algorithms, attention, mediation, digital infrastructures, generative media, social media, recommendation systems, and computational culture.
Film and Screen Studies	Representations of AI, robots, automation, synthetic actors, CGI, virtual production, deepfakes, and the history of machine vision in screen culture.
Musicology and Sound Studies	AI music generation, algorithmic composition, voice synthesis, sonic archives, listening technologies, authorship, performance, and authenticity.
Theatre and Performance Studies	Human-machine performance, avatars, virtual bodies, scripted interaction, automation, liveness, embodiment, and AI as performer or collaborator.
Cultural Studies	AI as cultural formation; ideology, power, identity, labor, representation, consumer culture, and everyday life under algorithmic systems.
Gender and Sexuality Studies	Bias, embodiment, care work, feminized digital labor, voice assistants, intimate technologies, harassment, classification, and gendered assumptions in AI systems.
Ethnic Studies	Racialization in algorithmic systems, surveillance, predictive policing, biometric classification, data colonialism, and histories of technological inequality.
Postcolonial Studies	AI and empire, extraction, global data labor, linguistic dominance, infrastructure, development discourse, colonial knowledge systems, and technological dependency.
Indigenous Studies	Data sovereignty, Indigenous AI, knowledge governance, relational ethics, refusal, archival justice, and alternatives to extractive data regimes.
Disability Studies	Assistive AI, accessibility, normativity, classification, embodiment, dependency, automation, care, and the design of systems around imagined “normal” users.

Arts disciplines

Discipline	How it contributes to AI studies
Studio Art	Treats AI as medium, collaborator, tool, constraint, and object of critique through image generation, installation, sculpture, drawing, painting, and mixed media.
Digital Art	Studies and produces computer-based artworks, generative systems, interactive media, algorithmic aesthetics, digital materiality, and networked culture.
Performance Art	Investigates embodiment, liveness, automation, avatars, robotics, synthetic performers, human-machine interaction, and AI-mediated presence.
Theatre	Studies AI as playwright, performer, dramaturgical device, scenographic system, audience interface, and representation of nonhuman agency.
Dance	Explores motion capture, gesture recognition, choreographic algorithms, human-robot movement, embodied computation, and AI-assisted choreography.
Music Composition	Studies algorithmic composition, AI-assisted composing, style imitation, authorship, improvisation, creativity, and musical form.
Sound Art	Engages synthetic voice, audio recognition, listening machines, sonic surveillance, spatial sound, and machine-generated soundscapes.
Film Production	Addresses AI in editing, visual effects, animation, synthetic actors, script development, virtual production, deepfakes, and automated post-production.
Animation	Studies procedural animation, motion synthesis, generative character design, synthetic movement, simulation, and AI-assisted visual storytelling.
Photography	Examines computational photography, image manipulation, machine vision, synthetic images, authenticity, indexicality, surveillance, and dataset aesthetics.
Video Art	Uses moving-image technologies, feedback systems, algorithmic editing, generative video, surveillance footage, and AI image synthesis.
Graphic Design	Studies AI-assisted visual communication, typography, branding, layout, interface aesthetics, design labor, and automated visual production.
Industrial Design	Examines AI-enabled products, smart objects, robotics, usability, human-machine interaction, and automation in design workflows.
Interaction Design	Studies interfaces for AI systems, chatbots, recommendation systems, conversational agents, trust, explainability, and user experience.
Game Design	Studies procedural generation, non-player characters, adaptive systems, AI agents, simulation, game worlds, and player-model interaction.
Fashion Design	Addresses generative design, body data, virtual clothing, recommendation systems, supply chains, wearable technology, and algorithmic style.
Textile Arts	Studies smart textiles, computational weaving, e-textiles, sensors, craft automation, pattern generation, and material computation.
Craft Studies	Examines making, skill, tacit knowledge, automation, repair, fabrication, material intelligence, and the politics of machine-assisted production.
Creative Writing	Studies AI co-writing, authorship, voice, style, narrative generation, literary experimentation, and the changing conditions of creative labor.
Comics and Sequential Art	Explores AI-generated imagery and narrative, visual style, authorship, panel composition, fandom, and platform circulation.
Curatorial Practice	Addresses AI in exhibition design, collection interpretation, recommendation, provenance, metadata, audience analytics, and algorithmic curation.
Museum Practice	Studies AI-mediated access, digital collections, virtual exhibitions, preservation, interpretation, restitution, and cultural heritage data.
Art Conservation	Uses imaging, pattern recognition, reconstruction, provenance analysis, and predictive modeling while raising questions of authenticity and intervention.

Interdisciplinary fields (arts and humanities)

Field	How it contributes to AI studies
Digital Humanities	Computational analysis of cultural materials, text mining, digital archives, data visualization, critical tool-building, and reflection on computation as scholarly method.
Critical AI Studies	AI as a social, cultural, political, and epistemic phenomenon; combines humanities, social sciences, STS, law, design, and computer science.
Science and Technology Studies	Sociotechnical systems, expert knowledge, infrastructures, laboratories, classification, standards, users, institutions, and the co-production of technology and society.
Critical Data Studies	Datafication, datasets, metadata, database politics, quantification, classification, data labor, surveillance, and the limits of data-driven knowledge.
Platform Studies	The technical, economic, and cultural logics of platforms, including recommendation algorithms, app ecosystems, APIs, moderation, and platform governance.
Software Studies	Code, interfaces, operating systems, protocols, software architectures, and the cultural assumptions embedded in computational systems.
Code Studies / Critical Code Studies	Source code as a cultural text; examines code’s rhetoric, style, ideology, authorship, and interpretive significance.
Algorithm Studies	Algorithms as cultural, institutional, and political actors; focuses on opacity, ranking, classification, recommendation, and automation.
Surveillance Studies	AI in monitoring, prediction, policing, biometrics, workplace analytics, behavioral tracking, and state or corporate power.
Infrastructure Studies	Data centers, cloud computing, cables, energy systems, logistics, standards, maintenance, and the material infrastructures that make AI possible.
Internet Studies	Online communities, digital publics, memes, social media, identity, governance, moderation, virality, and algorithmically mediated communication.
Game Studies	AI agents, procedural generation, simulation, player behavior modeling, virtual worlds, non-player characters, and games as experimental AI environments.
Human-Computer Interaction	Human-AI interaction, usability, trust, explainability, design, user experience, interface ethics, and participatory design.
Environmental Humanities	Energy use, data centers, extraction, e-waste, climate modeling, planetary computation, and ecological consequences of AI infrastructure.
Medical Humanities	AI in diagnosis, care, triage, health records, bioethics, patient narratives, disability, clinical authority, and medical decision-making.
Visual Culture Studies	Machine vision, facial recognition, image generation, visual datasets, memes, aesthetics, and the politics of seeing.
Sound Studies	Voice assistants, synthetic speech, audio surveillance, sonic interfaces, listening machines, and the cultural politics of sound recognition.
Museum Studies	AI curation, digital exhibits, provenance, collections data, cultural heritage digitization, access, restitution, and algorithmic interpretation.
Public Humanities	AI literacy, public debate, community engagement, civic education, cultural institutions, and democratic deliberation about technology.
AI Ethics	Bias, fairness, accountability, transparency, harm, governance, dignity, human rights, and normative evaluation of AI systems.
Responsible AI / AI Governance	Institutional design, standards, auditing, policy, risk management, procurement, public accountability, and sociotechnical oversight.
Translation Studies	Machine translation, multilingual AI, linguistic hierarchy, untranslatability, cultural context, translation labor, and the politics of language technologies.
Book History and Textual Studies	AI and textual production, editing, authorship, archives, metadata, scholarly editions, publishing infrastructures, and the history of inscription technologies.
Archival Studies	Dataset construction, preservation, metadata, provenance, memory institutions, digitization, algorithmic access, and the ethics of cultural data.
Critical Theory	AI, capitalism, subjectivity, ideology, technocracy, rationalization, reification, instrumental reason, automation, and the critique of technological modernity.
Art and Technology	Studies the mutual formation of artistic practice and technological systems, including AI as medium, infrastructure, and cultural force.
Creative AI	Focuses on AI systems used for artistic production, creativity support, co-creation, generative media, and computational aesthetics.
Human-Computer Interaction	Contributes methods for studying usability, creativity support tools, trust, interface design, explainability, and human-AI interaction.
Design Studies	Examines design processes, user imaginaries, speculative futures, design justice, automation, and AI-enabled products and services.
Critical Design	Uses designed artifacts to critique assumptions about intelligence, optimization, efficiency, creativity, and technological progress.
Design Justice	Studies how AI design affects marginalized communities and how participatory methods can redistribute design power.
Research-Creation	Uses artistic making as a mode of scholarly inquiry into AI, computation, digital culture, and sociotechnical systems.
Practice-Based Research	Develops knowledge through creative practice, including experiments with AI tools, generative systems, robotics, or interactive installations.
Practice-Led Research	Begins from artistic practice to generate theoretical, methodological, or critical insight about AI and digital culture.
Media Arts and Sciences	Integrates art, design, engineering, computation, sensing, robotics, AI, and human experience.
Digital Heritage / Cultural Heritage Informatics	Studies digitization, reconstruction, metadata, 3D modeling, preservation, provenance, and AI-mediated access to cultural collections.
Computational Aesthetics	Uses computational models to analyze, generate, or evaluate aesthetic form, style, perception, and artistic judgment.
Affective Computing	Studies computational sensing and modeling of emotion, relevant to performance, interaction, sound, games, and responsive environments.
Embodied Interaction	Examines gesture, movement, touch, bodies, sensors, robotics, spatial computing, and human-machine performance.
Robotics and Art	Investigates robots as performers, sculptural agents, companions, fabrication systems, and embodiments of nonhuman agency.
Social Robotics	Studies expressive, affective, assistive, and performative robots in relation to social interaction, care, design, and embodiment.
Interactive Media	Covers responsive environments, installations, games, interfaces, sensors, immersive media, and AI-driven interaction.
Immersive Media / XR Studies	Studies virtual reality, augmented reality, mixed reality, spatial computing, synthetic environments, avatars, and AI-generated worlds.
Virtual Production Studies	Examines real-time rendering, motion capture, AI-assisted effects, synthetic actors, and hybrid cinematic production.
Procedural Content Generation Studies	Focuses on algorithmic production of game levels, characters, narratives, environments, and assets.
Data Visualization	Treats visualization as analytic, aesthetic, rhetorical, and political practice; relevant to making AI systems legible.
Information Design	Studies how complex data, systems, and AI outputs are communicated visually and interactively.
Interface Studies	Examines interfaces as aesthetic, cultural, political, and epistemic forms that structure human-AI interaction.
Performance Studies	Examines liveness, embodiment, repetition, scripted behavior, avatars, automation, machinic performance, and audience interaction.
Movement Studies	Connects dance, biomechanics, motion capture, robotics, gesture recognition, and embodied AI.
Fashion-Tech / Wearable Media	Studies smart garments, body sensors, recommendation systems, virtual try-on, digital fashion, and algorithmic style.
Digital Fabrication	Studies 3D printing, CNC, robotic fabrication, parametric design, craft automation, and material computation.
Maker Studies	Examines DIY technology, creative coding, open-source hardware, repair cultures, fabrication labs, and accessible AI experimentation.
Creative Coding	Uses programming as artistic practice, including generative visuals, interactive systems, live coding, and AI-enabled artworks.
Live Coding Studies	Studies real-time programming as performance, improvisation, notation, and human-machine collaboration.
Electronic Literature	Studies born-digital writing, hypertext, interactive fiction, generative literature, chatbots, and AI-mediated textuality.
Digital Poetics	Examines computational language, generative text, procedural writing, AI poetry, constraint, and machine authorship.
Expanded Cinema	Studies moving-image work beyond conventional film, including installation, projection mapping, real-time systems, and generative video.
Bioart	Uses living systems, biotechnology, simulation, and artificial life to examine intelligence, agency, life, and technological intervention.
Artificial Life Art	Explores emergence, evolution, agents, swarms, adaptive systems, and synthetic life as artistic and theoretical problems.
Posthumanist Art Studies	Examines nonhuman agency, human-machine relations, distributed cognition, embodiment, ecology, and challenges to human exceptionalism.
Environmental Media / Eco-Art	Studies AI’s material infrastructures, energy use, climate visualization, sensing, planetary computation, and ecological aesthetics.
Surveillance Art / Sousveillance Studies	Uses artistic practice to expose, critique, or reverse surveillance systems, including facial recognition and predictive analytics.
Forensic Architecture / Investigative Aesthetics	Uses spatial analysis, visualization, modeling, media verification, and sometimes machine learning to investigate violence, rights, and state power.
Arts Entrepreneurship and Creative Industries Studies	Studies how AI changes creative labor, copyright, markets, platforms, patronage, branding, and cultural production.
Arts Management and Cultural Policy	Examines AI governance in cultural institutions, funding, copyright, labor policy, access, equity, and public culture.

(yes this list was created with AI assistance)