Education
Reference architecture for an AI reading and learning tutor – speech scoring plus curriculum-grounded tutoring with progress tracking, powered by the NAISH Hub.
A learner practises reading aloud or works through lessons on an app or by voice; a teacher watches progress from a dashboard. This template shows a tutor that scores speech, teaches from the curriculum, and tracks each learner.
How it works
- Channels – learners use an app or voice; teachers use a dashboard.
- Gateway & access – handles access and authentication.
- AI tutor agent (+ skills) – calls skills: speech scoring for reading fluency, the model for tutoring dialogue, retrieval over the curriculum for grounding, and a progress service to track mastery.
- Speech scoring (ASR) – reads recorded audio and writes fluency scores to the progress service.
- NAISH Hub – serves the ASR and language models on GPU. Sovereign AI core.
- Data & dependencies – a vector store grounds tutoring, a learner-records store tracks progress, and identity gates access.
Where the Hub adds value
- Local-language literacy – Hub-served ASR and models assess and tutor in the languages learners actually read.
- Grounded tutoring – retrieval keeps the tutor on the official curriculum.
- Sovereignty – learner data and models stay on national infrastructure; see Governance & Risk.
Reusable building blocks
| Layer | This template | Swap in for your context |
|---|---|---|
| Channels | Learner app / voice + teacher dashboard | Any learner or educator client |
| Agent + skills | Speech scoring, retrieval, progress | Subject-specific skills |
| Models (Hub) | ASR + tutoring LLM | Curriculum-tuned models |
| Services | Progress service | Whatever LMS the programme runs |
| Data | Learner records + curriculum | Any governed education source |
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