Review of Political Economy

AI and the future of the university

Rationale
Generative artificial intelligence is reshaping how universities produce, certify and transmit knowledge, and this special issue asks who gains, who loses, and what kind of institution emerges. Large language models now draft papers, review manuscripts, grade essays and tutor students. Universities and publishers are adopting them faster than they can study their effects.

Public debate tends to treat AI as a technical shock that institutions must simply absorb. We start from a different premise: the adoption of AI in higher education is shaped by existing pressures — marketisation, casualised academic employment, metric-driven research evaluation, the concentrated market power of commercial publishers and platform firms, and chronic public underfunding. AI may intensify these pressures, redirect them, or open space to contest them.

The special issue invites contributions that examine these changes critically and in their institutional, economic and political context. We welcome work on the university as a workplace, as a producer of knowledge, as a site of teaching and learning, and as a public institution whose purpose is now being renegotiated.

Themes
We invite papers on any of the five themes below. The questions under each are indicative, not exhaustive.

1. AI, academic labour and the future of the university

AI changes what academic work is, who does it, and how it is valued. It may automate parts of teaching, research support and administration while creating new tasks of supervision, verification and data work.

• How is AI changing the labour process, workload and autonomy of academic and professional staff?
• Does AI deepen the casualisation of academic employment, or the divide between a secure core and a precarious periphery?
• How are managerial control, performance metrics and surveillance being reorganised around AI tools?
• What forms of collective bargaining, union strategy or professional resistance are
  emerging?
• Who captures the productivity gains from AI in universities: staff, students,
  administrators or technology vendors?

2. AI, the production of knowledge and the future of journals
AI is entering every stage of scholarly communication, from literature search and drafting to peer review, editing and indexing. This raises questions about authorship, credit, quality and the economics of academic publishing.

• How does AI alter what counts as an original contribution, and who counts as an author?
• What happens to peer review when reviewers, authors and editors all use AI — and when submission volumes rise sharply?
• How are commercial publishers using AI and the content they hold, including licensing it for model training?
• Will AI reinforce dominant paradigms, languages and centres of knowledge production, or widen access for scholars in the Global South and in heterodox traditions?
• What editorial policies, governance models and alternatives (diamond open access, scholar-led journals) are adequate to this moment?

3. AI, education and the future of universities
AI raises basic questions about the purpose, organisation and funding of higher education. It may reshape institutional strategy, business models and the relationship between universities, states and technology firms.

• How are universities governing AI adoption, and whose interests shape procurement and partnerships with technology firms?
• Does AI strengthen the case for mass, low-cost online provision, and with what
  consequences for public universities?
• How will AI affect the value of degrees, graduate labour markets and the university's credentialing role?
• What does AI mean for inequality between institutions, countries and groups of students?
• What visions of the public university can be defended in an age of AI?

4. AI and the transformation of pedagogy and curriculum
AI tools are changing how students learn and how teachers teach. They challenge assumptions about which knowledge and skills a curriculum should prioritise.

• What should students learn when AI can write, code, summarise and calculate?
• How does AI change the teacher–student relationship, feedback and the role of tutoring?
• What are the risks of deskilling, homogenised thinking or over-reliance, and how can teaching counter them?
• How should disciplines — including economics and the social sciences — integrate critical AI literacy into their curricula?
• What evidence do we have on learning outcomes, and what does it fail to capture?

5. AI and the redefinition of assessment and academic integrity
AI unsettles the essay, the take-home exam and other established forms of assessment. It also strains existing frameworks of plagiarism and misconduct.

• What forms of assessment remain meaningful when AI can produce competent written work?
• How reliable and fair are AI-detection tools, and who bears the cost of their errors?
• How should academic integrity be redefined: as rule enforcement, as trust, or as a shared scholarly ethic?
• How do assessment reforms interact with workload, class sizes and institutional pressure on pass rates?
• What can be learned from institutions and countries that have already redesigned
  assessment?