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View corpus contextThe neoliberal playbook for global education is fraying: platform power, datafication and geopolitical and cultural shifts undermine transnational policy convergence and demand new, political-economy-informed approaches to AI in education. Economists should model platform governance, data as an economic asset, and distributional effects rather than relying on narrow efficiency narratives.
Reading the Global and the Emerging Challenges Facing Comparative Education
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The neoliberal framework that long shaped global education policy is increasingly strained by platformisation, rising inequality, geopolitical fragmentation and post-truth politics, and AI economists should adopt political-economy-aware, distributional, and governance-focused approaches when studying educational technologies.
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In this essay honouring Bob Cowen, we address the contemporary challenges facing comparative education in responding to his call to “read the global”. We argue that the neoliberal imaginary, which has long shaped understandings of global educational policy and governance, is increasingly marked by contradiction and exhaustion. Contemporary geopolitical, economic and cultural shifts are reconfiguring the global order and demanding new approaches to educational purposes and governance. Rising inequalities, populism, anti-globalisation sentiment, post-truth politics and the platformisation of society have made the task of reading the global increasingly difficult. We analyse the implications of these developments for comparative education today.
Summary
Main Finding
The essay argues that the neoliberal imaginary that long structured global educational policy is now contradictory and exhausted. Contemporary geopolitical, economic and cultural shifts — including rising inequality, populism and anti-globalisation, post-truth politics, and the platformisation/datafication of society — are reconfiguring the global order and complicating the project of "reading the global." Comparative education must develop new conceptual tools and governance approaches to respond to these changes.
Key Points
- Neoliberal imaginary: For decades, market-oriented norms (competition, accountability through metrics, privatization, and transnational policy transfer) shaped global educational governance and comparative frameworks.
- Signs of exhaustion and contradiction: Tensions between market logic and public needs, uneven policy outcomes, legitimacy crises in international assessments and policy prescriptions.
- Geopolitical shifts: Resurgent state power, techno-nationalism, and anti-globalisation politics undermine transnational policy convergence and complicate comparative inference.
- Cultural and informational changes: Populism, disinformation, and "post-truth" dynamics erode shared epistemic foundations for policy debate and make comparative claims more contestable.
- Platformisation and datafication: EdTech and platform actors are reshaping educational markets, data governance, and pedagogical practices, introducing new power asymmetries and economic logics (surveillance, monetization of data).
- Inequality and distributional effects: Global and within-country inequalities are intensifying, affecting access to education, the benefits of digitalization, and the social legitimacy of policy tools tied to markets and metrics.
- Methodological implications: Comparative education needs to broaden methods (beyond straightforward policy transfer and test-score comparison) to include political economy, critical discourse, and attention to power and local context.
Data & Methods
- Type of work: Conceptual/theoretical essay and critical synthesis rather than original empirical measurement.
- Methods used or implied: critical literature review, historical/contextual analysis of policy trends, discourse and ideological critique (examining the normative content of the "neoliberal imaginary"), and interpretive analysis of contemporary socio-political phenomena (platformisation, populism, geopolitical shifts).
- Evidence base: Synthesis of existing scholarship and contemporary developments in politics, economics, and technology; no new quantitative dataset reported.
Implications for AI Economics
- Market structure and platform power: The platformisation of education highlights asymmetric market power (network effects, data monopolies). AI economists should model platform governance, multi-sided markets, and the implications of data concentration for competition, innovation, and welfare.
- Distributional impacts and inequality: Economic analyses must incorporate distributional outcomes of AI in education — who gains/loses from automation, adaptive learning, and data-driven personalization — and design redistributive or compensatory policies.
- Data as an economic asset and externalities: Treat educational data as an asset with value and potential negative externalities (privacy harms, surveillance). AI economists should include data governance, property-rights regimes, and externalities in models and cost–benefit frameworks.
- Measurement and metrics: The critique of test-driven, metricized governance calls for new outcome measures in AI economics (well-being, civic capacities, equity-adjusted learning gains) and caution about gaming and perverse incentives generated by algorithmic evaluation.
- Political economy constraints: Geopolitical fragmentation and anti-globalisation sentiment imply that transnational coordination on AI policy is fragile. Models and policy prescriptions must account for techno-nationalism, strategic trade and regulation, and differing institutional capacities across countries.
- Information quality and trust: Post-truth and misinformation dynamics affect how AI-driven educational content and recommendations are received. Economists should incorporate information frictions, trust, and signaling into analyses of AI adoption and welfare.
- Interdisciplinary methods and governance experiments: AI economists should collaborate with political scientists, ethicists and education scholars to design regulatory experiments (platform regulation, data trusts, algorithmic audits) and evaluate their economic effects.
- Research agenda suggestions:
- Empirically estimate distributional effects of AI-powered educational technologies across socio-economic groups.
- Model competition and market power in educational platforms, including implications of vertical integration and data lock-in.
- Evaluate policy instruments: data portability, interoperability mandates, privacy regulation, subsidies for equitable access, and public provision alternatives.
- Develop and test alternative outcome metrics that capture civic, social and long-term learning, and analyze how AI systems perform against these broader goals.
- Study international policy divergence and its economic consequences for cross-border platform firms and global knowledge flows.
Short takeaway: The essay calls for AI economists to move beyond narrow efficiency and innovation narratives and to build political-economy-aware, distributionally sensitive models and policy evaluations that reckon with platform power, data governance, mistrust, and geopolitics in the changing global educational landscape.
Assessment
Paper Typetheoretical
Evidence Strengthn/a — This is a conceptual/theoretical essay and critical synthesis without original empirical identification or causal tests, so standard evidence strength metrics for causal inference do not apply.
Methods Rigormedium — The paper uses a critical literature synthesis, historical/contextual analysis, and interpretive critique which are appropriate for theoretical work, but it lacks a transparent, systematic review protocol, explicit criteria for selecting evidence, or empirical validation of claims.
SampleNo original empirical sample; the paper is a conceptual essay synthesizing existing scholarship, policy documents, and contemporary socio-political developments (platformisation, populism, geopolitics) relevant to global education and EdTech.
Themesgovernance skills_training inequality human_ai_collab adoption
GeneralizabilityNo empirical estimates or data-driven results — claims are interpretive and may not hold uniformly across countries, education systems, or platform markets., Argument focuses on education and EdTech; implications for other sectors (health, labor platforms) are suggestive but not demonstrated., Broad geopolitical and cultural claims may overlook local institutional variation and counterexamples., Lacks systematic cross-country comparison or representative sampling of policy contexts, limiting external validity.
Claims (10)
| Claim | Direction | Outcome | Confidence & Evidence | Details |
|---|---|---|---|---|
| The neoliberal imaginary that structured global educational policy is now contradictory and exhausted. Governance And Regulation | negative | Viability and coherence of the dominant global educational policy framework |
Reading fidelity
high
Study strength
low
|
not reported
|
| Market-oriented norms, including competition, metric-based accountability, privatization, and transnational policy transfer, have shaped global educational governance and comparative education frameworks for decades. Governance And Regulation | positive | Influence of market-oriented norms on educational governance |
Reading fidelity
high
Study strength
low
|
not reported
|
| Tensions between market logic and public needs, uneven policy outcomes, and legitimacy crises in international assessments and policy prescriptions indicate the exhaustion or contradiction of the neoliberal educational policy framework. Governance And Regulation | negative | Legitimacy and effectiveness of market-oriented educational policies |
Reading fidelity
high
Study strength
low
|
not reported
|
| Resurgent state power, techno-nationalism, and anti-globalisation politics undermine transnational policy convergence and complicate comparative inference in education. Governance And Regulation | negative | Transnational policy convergence and comparability of educational policy evidence |
Reading fidelity
high
Study strength
low
|
not reported
|
| Populism, disinformation, and post-truth dynamics erode shared epistemic foundations for educational policy debate and make comparative claims more contestable. Governance And Regulation | negative | Trustworthiness and contestability of comparative educational knowledge and policy debate |
Reading fidelity
high
Study strength
low
|
not reported
|
| The platformisation and datafication of education are reshaping educational markets, data governance, and pedagogical practices while introducing new power asymmetries based on surveillance and data monetization. Market Structure | mixed | Market power, data governance, and organization of educational practices |
Reading fidelity
high
Study strength
low
|
not reported
|
| Global and within-country inequalities are intensifying, affecting access to education, the distribution of benefits from digitalization, and the legitimacy of market- and metric-based policy tools. Inequality | negative | Educational access, distribution of digitalization benefits, and policy legitimacy across social groups |
Reading fidelity
high
Study strength
low
|
not reported
|
| Comparative education needs to move beyond straightforward policy transfer and test-score comparison by incorporating political economy, critical discourse, power, and local context. Research Productivity | positive | Methodological adequacy and explanatory scope of comparative education research |
Reading fidelity
high
Study strength
low
|
not reported
|
| Educational data should be treated as an economic asset with potential negative externalities, including privacy harms and surveillance, requiring data-governance and property-rights analysis. Ai Safety And Ethics | mixed | Economic value and social costs of educational data |
Reading fidelity
high
Study strength
speculative
|
not reported
|
| Geopolitical fragmentation and anti-globalisation sentiment make transnational coordination on AI policy fragile and require policy analysis to account for techno-nationalism, strategic trade, regulation, and differing institutional capacities across countries. Governance And Regulation | negative | Feasibility of transnational AI-policy coordination |
Reading fidelity
high
Study strength
speculative
|
not reported
|