AI TutorDevelopment
Give every learner a patient practice partner that works from your own curriculum and reports back to teachers, rather than replacing them.
AI Tutor Development: what the work involves
A teacher with forty students cannot answer every why at the moment it arises. Learners get stuck on a step, stay silent, and fall behind quietly. Academies and ed-tech platforms try to cover this with recorded videos and static question banks, but a student who needs a different explanation at ten at night has nobody to ask, and tuition costs push many families out entirely.
DevKey builds tutoring assistants anchored to your syllabus, textbooks and question banks. The tutor retrieves the relevant material, explains concepts at the learner's level, asks guiding questions before giving answers, and generates practice items with worked solutions. Progress is stored per topic, so revision targets weak areas. Teachers and parents can see summaries and flagged confusion, and subject experts review content and tune the tutor's teaching style before launch.
Core features
Curriculum-grounded explanations
The tutor draws on your approved materials, citing the chapter or lesson, so explanations match what the exam board and teacher expect.
Socratic questioning mode
Instead of handing over answers, it can ask the next guiding question and reveal hints step by step, which supports actual learning.
Practice generation with solutions
New questions at a chosen difficulty are produced and checked against your answer keys or a rule validator for subjects like maths.
Learner progress map
Topic-level mastery is tracked from interactions and quizzes, steering revision to weaker areas and avoiding repetition of known material.
Teacher and parent views
Summaries of activity, common misconceptions and flagged questions help teachers adjust lessons and understand who needs attention.
Bilingual support
Explanations can be given in English, Urdu or a blend, with technical terms kept consistent with the textbook.
What we get right before launch
Correctness in STEM
Language models make arithmetic and reasoning errors. We pair them with calculators, symbolic maths tools and answer keys, and review a sample of tutor outputs with subject teachers before release.
Child safety and data protection
Learners may be minors. We apply age-appropriate content filters, restrict off-topic conversation, minimise personal data, and meet the consent requirements of the schools and regions involved.
Dependence and academic honesty
A tutor that always solves the problem encourages copying. We design for hints first, allow teachers to set modes, and keep assessment tools separate from the practice assistant.
Tools and technology
- Anthropic Claude
- OpenAI GPT
- LlamaIndex
- pgvector
- Python
- FastAPI
- SymPy
- React Native
- Next.js
Common questions, answered
Can the tutor follow our exact syllabus?
Yes, if we have the materials. We index your textbooks, notes and question banks and restrict the tutor to them for factual claims. Where content is missing, it says so instead of improvising.
Will it give wrong answers?
Sometimes, as any model can. We reduce this with grounded retrieval, calculation tools and teacher review of samples, and we include an easy way for students to flag an answer they doubt.
Does it replace teachers?
No. It extends practice and support between lessons. Teachers still plan, assess and mentor, and the tutor gives them better visibility into where students struggle.
Is it safe for children?
We add content filters, topic limits and logging, and keep personal data minimal. Safety is never absolute, so a school should still supervise use and review reports of problems.
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Ready to start your AI Tutor Development project?
Tell us what you need and we will come back with a clear scope, timeline and the questions worth answering before any build starts.