What Are AI‑Generated Learning Materials?

As large language models have improved, some publishers, edtech startups, and even some individual teachers have begun using AI to generate or heavily draft textbooks, revision guides, and course packs. Instead of writing every chapter from scratch, they can prompt a system to produce explanations, examples, and practice questions for a given syllabus, then edit and assemble those outputs into a cohesive resource. Some platforms go further, offering “dynamic textbooks” which adjust explanations and practice items to a learner’s level in real time, or that can regenerate sections in simpler language or another language on demand.

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On the surface, this looks like a win: faster production, easier localisation, and the promise of more affordable or even free materials. For niche subjects or rapidly evolving fields, AI‑assisted authoring might make it viable to keep content up to date where traditional publishing cycles lag. Teachers can also use the same tools to produce custom course packs, mixing AI‑generated explanations with curated readings and their own commentary. But underneath sit fundamental questions about accuracy, depth, authorship, and what it means to “trust” a text.​

Potential, Pitfalls, and How to Read Critically

There are real advantages to AI‑supported materials when humans are in the loop. Teachers can quickly draft multiple versions of an explanation – for different ages, backgrounds, or language proficiencies – and then refine the best one, saving precious time. Practice questions and worked examples can be tailored to local curricula and contexts rather than imported wholesale from overseas exam systems. For learners with disabilities or those studying in a second language, the ability to adjust reading level or generate alternative formats (summaries, glossaries, visual outlines) can make content more accessible without waiting for a special edition.​ Here at Turtle and elephant, one way we’ve tried this recently together with a student who is learning Spanish is to create texts specific to their level and the grammar and vocabulary they are focusing on. For example using a prompt such as ‘please write a 150-200 word story about ordering food in a restaurant using present simple and continuous tenses’.

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Such examples are contained and short and together with the student we discuss nuances of the text and why specific phrases and grammar were used. The dangers emerge when AI outputs are insufficiently checked or when cost pressures incentivise replacing expert writers and editors entirely. Currently Large Language Models (LLMs) are known to “hallucinate” plausible but incorrect facts, invent references, and smooth over controversy in ways that can mislead learners. Subtle errors in maths, science, or history may go unnoticed by non‑specialist reviewers, yet still end up being propagated widely. There are also ethical problems with  bypassing and ignoring existing Intellectual Property (IP) laws and using existing textbooks, articles, and teacher materials as training data without clear credit or compensation., as an ongoing case between the NY Times and Open AI demonstrates (to learn more you can refer to this Reuters article).

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For parents and students, one pragmatic stance is to treat AI‑generated or AI‑assisted materials as they would any new or unfamiliar textbook:

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• Check who is named as the author or editor. Are subject experts clearly involved, and is there transparency about how AI was used in the process?​
• Sample explanations, dates & key facts against a second source. If a key concept appears only in one resource and nowhere else reputable, be cautious.
• Notice the texture of examples and voice. Overly generic, culture‑free writing with slightly off idioms can be a hint that little human craft has gone into the text.

For educators, being upfront with students about when and how AI has been used in course materials can itself be a teachable moment. It opens up rich questions: How do we know if an explanation is good? What counts as a trustworthy source? Who gets to “author” knowledge? Those questions can sometimes matter just as much as the content of any single chapter – perhaps even more in a world where explanations are cheap to produce but costly to understand.

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Note: the first draft of this article was done by AI Chatbot Claude with the support of Max Capacity. The text was then edited and adapted by Jaye Sergeant of Turtle & elephant, who is responsible for the published version.

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