A proposed ten-week course on the people, institutions, and material systems behind generative media.

A generated image may appear on a screen without a camera ever recording the scene. But it does not arrive without a production history. Someone develops and finances a model, supplies or licenses data, performs visible and invisible work, chooses the output, distributes it, and decides how—if at all—its making will be disclosed. The image may be synthetic; the industry around it is not.

That distinction is the starting point for this open syllabus. Debates about AI in media frequently begin with the finished artifact: Does it look convincing? Is it original? Could it replace a filmmaker, performer, writer, or designer? These are legitimate questions, but the artifact alone cannot answer them. To understand the stakes, students also have to ask who owns the infrastructure, whose labor is made visible or invisible, how creative decisions move between people and platforms, and whose voices and images become easier—or harder—to represent.

I designed Media Industries and AI: Labor, Platforms, and Cultural Production as a proposed upper-division lecture course in critical media studies. It treats generative AI neither as a miracle nor as a single catastrophe. Instead, it places emerging tools in the longer histories of film, television, music, advertising, distribution, and digital platforms. The emphasis is not on teaching students to operate a particular product. It is on giving them the historical knowledge and investigative methods to examine changing systems of cultural production.

Why Teach AI Through Media Industries?

A media-industries approach keeps aesthetics, authorship, and technological practice in view while examining the arrangements that make them possible: contracts, training-data practices, labor hierarchies, platform governance, financing, distribution, and environmental costs. It also allows comparisons across media and across national contexts. A production decision that promises efficiency to one institution may shift costs or risks elsewhere.

The course is designed for a large lecture without reducing learning to passive listening. Students build an infrastructure map, compare documented production cases, and develop a final research dossier. Short screenings and guided discussions connect conceptual readings with observable media practices. Students can complete every assignment without purchasing an AI subscription or generating synthetic media themselves. Critical understanding should not depend on access to a commercial tool.

This course also complements, rather than duplicates, my AI Cinematic Realism (AICR) open syllabus. The AICR seminar asks how realism, meaning, and accountable authorship operate in synthetic cinema. This broader lecture asks how media industries organize the conditions under which such work is made, circulated, valued, and contested. In Week 7, my Study 33: First Lesson provides one documented creative case to examine alongside other production practices.

What follows is a proposed teaching resource, not a claim that this course has already been offered or evaluated. Its ten-week plan, reading selections, and assessments are intended to be adapted to local program requirements and revised as media practices change.

Proposed Open Syllabus

Course title: Media Industries and AI: Labor, Platforms, and Cultural Production

Level: Upper-division undergraduate

Format: Large lecture with guided screenings and discussion or annotation activities

Duration: Ten-week quarter; adaptable to other academic calendars

Credits and course number: To be established by the adopting institution

Prerequisites: No background in programming or generative media production required

Course Description

Artificial intelligence is reshaping media not only through spectacular images but through ordinary industrial decisions: who supplies training data, who performs creative and hidden labor, who owns the infrastructure, who can authorize likeness and style, and who bears the costs of production. This upper-division lecture course places generative systems within longer histories of film, television, music, advertising, and platform media. Using production studies, political economy, critical technology studies, and transnational case studies, students examine competing claims about efficiency, creativity, representation, intellectual property, and public value. The course does not require students to generate AI content or hold paid platform accounts. Students learn to analyze the human and institutional systems behind seemingly automated media.

Learning Outcomes

By the end of the course, students will be able to:

• Explain how industrial organization and historical media transitions shape the adoption of generative AI.
• Map a synthetic-media production chain, identifying platform ownership, creative and data labor, financial incentives, and environmental costs.
• Compare claims about automation and creativity using peer-reviewed scholarship and documented production cases.
• Analyze the effects of AI systems on representation, consent, authorship, and cultural power across different media contexts.
• Develop a sourced, historically grounded argument about a media-industry controversy and communicate it in an accessible format.

Teaching Format and Assessment

Illustrative format: two interactive lectures per week, with guided screenings and discussion-section activities where available. Designed for a large undergraduate enrollment; no prerequisite in programming or AI image generation.

The following assessment model is illustrative and totals 100%:

• Reading and screening responses — 20%: Eight short analytical responses; students may drop their lowest two of ten opportunities. Each identifies a claim, tests it against a case, and cites evidence.
• Structured discussion and annotation — 10%: Individual contribution through section discussion, written annotation, or an accessible asynchronous equivalent; evaluated for evidence and engagement rather than speaking frequency.
• Industry infrastructure map and brief — 20%: A sourced visual or linear-text map of one media workflow (e.g., dubbing, animation, editing, advertising), plus a 900–1,100-word memo explaining stakeholders, labor, and points of control.
• Comparative case analysis — 20%: A 1,100–1,400-word comparison of two production cases with different AI uses, including disclosure practices and at least four credible sources.
• Final research dossier — 30%: A 1,600–2,000-word argument or approved equivalent (captioned audio/video essay with transcript), with research question, evidence log, counterargument, and a revision reflection.

Assessment criteria: clarity of question, quality of sources, historical/industrial analysis, consideration of counterarguments, attribution, and effective revision. Rubrics and examples provided at the start of each assignment.

Ten-Week Course Plan

Week 1 — What Is a Media Industry?

Inquiry: Continuity and change in cultural production; distinguishing technologies from the organizations that deploy them.

Read / view: Hesmondhalgh, The Cultural Industries (introductory selections); Jenkins, Convergence Culture (introduction).

In class: Analyze headlines about AI “replacing” a production role; identify absent institutions and workers.

Milestone: Select an industry or production process to investigate.

Week 2 — The Infrastructure Behind a Synthetic Image

Inquiry: Compute, models, licensing, training data, distribution, and access as sites of power.

Read / view: Crawford, Atlas of AI (selected chapters); Hesmondhalgh (selections on ownership).

In class: Build a collaborative infrastructure diagram, tracing a video from inputs to platforms.

Milestone: Map proposal and preliminary evidence list.

Week 3 — Creative Labor and Automation Claims

Inquiry: Deskilling, recasting of roles, artistic collaboration, and bargaining power.

Read / view: Bender, “Generative-AI, the Media Industries, and the Disappearance of Human Creative Labour”; Chow and Celis Bueno, “The Cloak of Creativity.”

In class: Compare opposed practitioner claims with scholarly evidence; practice interview questions.

Milestone: Submit an infrastructure-map draft.

Week 4 — Hidden Workers and Data Supply Chains

Inquiry: Data labeling, moderation, contracted labor, and inequalities behind “automation.”

Read / view: Gray and Suri, Ghost Work (selections); Crawford, Atlas of AI (labor selections).

In class: Map visible and invisible human contributions to an AI-mediated workflow.

Milestone: Industry map and brief due.

Week 5 — Platforms, Audiences, and Gatekeeping

Inquiry: Ranking, discovery, recommender systems, moderation, and concentrated control of attention.

Read / view: Noble, Algorithms of Oppression (selections); Jenkins, Convergence Culture (selections).

In class: Trace how an audiovisual work reaches an audience, noting algorithmic and editorial gatekeepers.

Milestone: Comparative case selection.

Week 6 — Representation and Global Media Asymmetries

Inquiry: Whose language, likeness, labor, archives, and cultural forms are included or excluded?

Read / view: Noble (selected chapters); Hesmondhalgh (internationalization and diversity selections).

In class: Compare a domestic and a transnational synthetic-media case with attention to language and power.

Milestone: Source-critique workshop.

Week 7 — Film Form, Synthetic Production, and Authorship

Inquiry: Separate visual plausibility from narrative judgment and production accountability.

Read / view: Prince, “True Lies”; Gutierrez, Study 33: First Lesson and its production account.

In class: Close-read a short synthetic film and compare its disclosed workflow with finished scenes.

Milestone: Comparative case analysis due.

Week 8 — Consent, Copyright, and Regulation

Inquiry: Distinguish legal questions, ethical commitments, contractual claims, and artistic practice.

Read / view: Cohen, Between Truth and Power (selections); Bender (revisit).

In class: Structured case discussion: proposed policy responses to a fictional likeness/voice dispute.

Milestone: Final dossier thesis and outline due.

Week 9 — Environmental and Material Costs

Inquiry: Energy, extraction, supply chains, labor, and the scale of model deployment.

Read / view: Crawford, Atlas of AI (environmental selections); Hesmondhalgh (political economy selections).

In class: Test sustainability claims against their scopes, omissions, and evidence.

Milestone: Peer review of dossier drafts.

Week 10 — Possible Futures of Cultural Production

Inquiry: Alternative production arrangements, governance, accountability, and collective agency.

Read / view: Hesmondhalgh (conclusion); Chow and Celis Bueno (revisit).

In class: Dossier research forum; students defend a position and respond to counterevidence.

Milestone: Final dossier submitted during designated assessment window.

Principal Readings and Source Links

Core readings are listed below for transparency and ease of adaptation. In an offered course, instructors should confirm access through library collections or legally available course links, and update current production examples each term.

• David Hesmondhalgh (2026). The Cultural Industries (5th ed.). SAGE; selected chapters
• Henry Jenkins (2006). Convergence Culture: Where Old and New Media Collide. NYU Press
• Kate Crawford (2021). Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence. Yale University Press
• Stuart Bender (2025). Generative-AI, the Media Industries, and the Disappearance of Human Creative Labour. Media Practice and Education
• Pei-Sze Chow and Claudio Celis Bueno (2025). The Cloak of Creativity: AI Imaginaries and Creative Labour in Media Production. European Journal of Cultural Studies
• Mary L. Gray and Siddharth Suri (2019). Ghost Work: How to Stop Silicon Valley from Building a New Global Underclass. Houghton Mifflin Harcourt
• Safiya Umoja Noble (2018). Algorithms of Oppression: How Search Engines Reinforce Racism. NYU Press
• Stephen Prince (1996). True Lies: Perceptual Realism, Digital Images, and Film Theory. Film Quarterly 49(3): 27–37
• Julie E. Cohen (2019). Between Truth and Power: The Legal Constructions of Informational Capitalism. Oxford University Press
• Joni Gutierrez (2026). Study 33, First Lesson: An Experiment in AI Cinematic Realism (AICR). Creative work and documented production study

Access, AI Use, and Research Ethics

Access and participation: Readings will be provided in accessible formats whenever available; screened media will have captions and, when appropriate, transcripts or descriptive alternatives. Slides will use structured headings and accessible links. Students can demonstrate learning through multiple approved formats without losing scholarly expectations.

Responsible AI use: Optional AI-assisted workflows must be disclosed with tool, purpose, source materials, and human revisions. Students may complete every assignment without using generative AI. Fabricated citations or representations of generated text or media as unassisted work are not acceptable.

Research ethics and respectful inquiry: Students should avoid uploading classmates’ work, personal data, copyrighted materials, or unconsented likenesses to external systems. Critical disagreement is welcome; argumentation must be evidence-based. Deadlines, accommodations, and attendance expectations would follow published university policies and individually approved arrangements.

Adapting the Syllabus

This ten-week outline is a model, not a fixed prescription. A longer semester could make more space for production-history case studies, regional comparisons, and student research workshops. A shorter course could use the infrastructure map and comparative case analysis as its central sequence. In every version, the key is to connect claims about what AI can do with evidence about the organizations and people that make media possible.

Instructors should select local and transnational cases appropriate to their students, review the availability of assigned sources, and align assessment and policy language with their institutions. The reading list is a starting point for critical inquiry, not a closed canon. Students should encounter competing perspectives and learn to investigate the evidence behind them.

A Closing Thought

The question is not only whether AI can make an image, an advertisement, a voice, or a film. It is also who can make decisions about that work, who benefits from its circulation, whose contributions disappear from view, and who is accountable for the result. Those are media-industry questions. They are also questions that students should be equipped to ask long after today’s tools have changed.

Reuse and Attribution

This original syllabus and accompanying introduction are shared under Creative Commons Attribution 4.0 International (CC BY 4.0). You may adapt the course design with appropriate attribution. Third-party readings, films, and other linked works retain their own copyrights and terms of use.