Teacher workload cut.
Student outcomes up.
An AI platform reads the books students are studying, generates questions tied to key concepts and characters, and gives instant feedback on their answers — while an AI tutor builds vocabulary and teachers get insights to personalise assignments.
student answers
and adopted in schools
less teacher workload
for every student
Client
An education technology startup building AI-powered learning platforms.
Goal
Build an AI-powered deep-reading platform that generates learning materials and interactive coaching, to improve teaching effectiveness and student learning outcomes.
A wrong answer from an AI tutor is worse than no answer at all.
The startup wanted an LLM that could read a book and teach it — generating questions about its concepts, characters, events and settings, then marking student answers in real time. That only works if the content is right. Every generated question and every piece of feedback had to be factually correct, aligned with the curriculum, and pedagogically sound, not just plausible-sounding text from a model.
On top of that, the system had to work at classroom scale, adapt its feedback to students at different learning levels and styles, and slot into the schools’ existing EdTech tools and workflows rather than sitting apart from them.
Three constraints were non-negotiable:
- Generative AI and LLMs had to handle large student and content volumes without breaking
- Learning materials had to stay accurate and curriculum-aligned, not just fluent
- Feedback had to adapt to each student’s level, and integrate with existing school platforms
An AI that reads the book, then teaches it.
The core of the platform is an LLM that analyses book content and generates questions tied to its key concepts, characters, events and settings — turning any set text into structured teaching material automatically, rather than requiring a teacher to write it by hand.
The platform reads the book and writes the questions, so the teacher’s job starts where it matters — teaching.
A real-time feedback engine, built with LangChain, gives students instant, adaptive responses to their answers, and an interactive AI tutor sits alongside it to build vocabulary and support mastery of Standard English. Natural language understanding makes these interactions conversational rather than menu-driven.
Teachers get their own layer on top: AI-driven insights that help them personalise assignments and plan lessons. The whole system was built on a scalable architecture designed for broad classroom adoption and integration with the EdTech platforms schools already use.
Live in schools, and doing the job.
The result is a working, curriculum-safe AI product the startup could put directly in front of schools — stronger student engagement, better learning outcomes, personalised support for different learners, and less manual load on teachers. It also gave the startup a concrete, differentiated product to point to in a crowded EdTech market.
* Case studies reflect work undertaken by our Heads of AI either during their tenure with Head of AI or in prior roles before they were part of the Head of AI network; they are provided for illustrative purposes only and are based on conversations with our Heads of AI.
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*Case studies reflect work undertaken by our Heads of AI either during their tenure with Head of AI or in prior roles before they were part of the Head of AI network; they are provided for illustrative purposes only and are based on conversations with our Heads of AI.