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Opinion: AI and the Illusion of Effortless Content Optimisation in Publishing and EdTech

The publishing industry’s obsession with “newness”—chasing fresh material and innovative formats—has long been a cornerstone of its strategy. But the LinkedIn prompt above, however simplistic, hits on a crucial question: are publishers, especially in education, truly capitalising on the rich archives they already possess? The suggestion seems logical enough: artificial intelligence can streamline the repurposing, tagging, and modularisation of existing content, transforming dusty repositories into dynamic assets. Yet, beneath this utopian vision lies a more complex reality, one rife with technical challenges, ethical dilemmas, and systemic risks.

The AI Mirage: Modularisation Meets Monetisation

Let’s start with the selling point: AI. The promise of artificial intelligence in content optimisation is seductive. By applying machine learning to vast, unorganised archives, publishers theoretically gain the ability to dissect, repackage, and redistribute material at scale and speed. For education publishers, this could mean modular lesson plans, adaptive learning pathways, or even personalised assessments, all derived from existing content.

But the commercial appeal of modularisation is as much about control as it is about innovation. Breaking content into smaller, reusable chunks gives publishers granular control over monetisation strategies. Instead of selling an entire textbook or curriculum, they can commodify individual pieces—charging institutions or learners for access to a single chapter, a five-minute video, or even an isolated quiz. This microtransaction model, while lucrative for publishers, raises serious questions about affordability and accessibility for schools, educators, and students. Modularisation isn’t just about efficiency—it’s a power play that could deepen inequities in educational access.

Hidden Costs: The Technical and Human Labour Behind AI Optimisation

The notion that AI can effortlessly “repurpose” content glosses over the reality of what these processes entail. AI systems require significant human oversight to ensure accuracy, relevance, and cultural sensitivity in educational materials, particularly when content is extracted and recontextualised. For instance, will an algorithm recognise when a historical event is presented in a problematic or outdated way? Or will it unintentionally perpetuate biases baked into the original material?

Tagging and metadata creation—another supposed AI superpower—often suffers from the same shortfalls. Automated systems might flag content with generic or irrelevant keywords, leading to poor search experiences for end-users. Worse, poorly implemented tagging can skew the discovery of materials toward dominant narratives, sidelining diverse perspectives that might already be underrepresented in educational publishing. These gaps in AI capability are rarely addressed in the marketing hype, leaving institutions to deal with the fallout when “smart systems” fail to deliver.

Privacy and Data: The Unspoken Risks of AI in Content Strategy

Beyond technical limitations, AI-driven content optimisation opens up a Pandora’s box of data privacy concerns. Modularised educational content often relies on detailed user data to deliver personalised experiences. This data includes not only what students access, but often how they interact with materials—how long they spend on a page, what kinds of errors they make on quizzes, and even their emotional engagement with video lessons.

Who controls this data, and how is it being used? If publishers are leveraging AI to mine behavioural insights from user interactions, where does that leave the institution’s ability to protect student privacy? At what point does adaptive education cross the line into surveillance? These are questions the industry seems disinclined to answer, opting instead to tout the “efficiency” of their AI-powered platforms.

The Bigger Picture: Modularisation as a Symptom of Industry Consolidation

The push toward modular content isn’t occurring in a vacuum. It aligns neatly with broader trends in publishing and EdTech, particularly the increasing consolidation of market power among a handful of major players. By modularising content, large publishers can lock institutions into their ecosystems, making it difficult—if not impossible—to switch to alternative providers without losing access to critical materials. This strategy mirrors the tactics used by Big Tech firms to dominate other industries, from software to cloud services.

For schools and universities, the long-term consequence is a reduction in bargaining power and autonomy. Modularisation may offer short-term convenience, but it comes at the cost of institutional resilience. Once locked into a publisher’s fragmented, pay-per-piece model, institutions risk losing the ability to curate their own educational experiences or negotiate fair pricing for holistic access.

Are Institutions Asking the Right Questions?

The LinkedIn post raises a fair point: publishers should indeed consider whether they’re making the most of their existing archives. But institutions need to dig deeper before buying into the AI-driven optimisation narrative. Key questions include:

• Who owns the content once it’s modularised? Does repurposing material give publishers more control over how institutions can use it?
• What safeguards are in place to ensure ethical and unbiased AI tagging? And how much manual oversight will this realistically require?
• How will modularisation impact long-term costs for institutions? Will the piecemeal pricing model erode budgets over time?
• What happens to student privacy in this equation? Are publishers collecting data under the guise of personalisation, only to monetise it down the line?

The Future of Content Repurposing: Efficiency or Exploitation?

AI modularisation has the potential to reshape how educational content is created and consumed, but it’s far from a neutral innovation. As publishers lean into these tools, institutions must recognise the underlying business strategies at play—strategies that prioritise profit and control over educational outcomes.

Efficiency doesn’t always mean progress, and modularisation could ultimately exacerbate the very problems it claims to solve. The smarter question, then, isn’t just whether publishers are making the most of their existing content—but whether they’re doing so in ways that truly serve educators and learners. If the answer is “no,” then perhaps institutions should be asking themselves whether they’re working as hard as they could to hold publishers accountable.