AI learning assistants that answer student questions from your course material and support teachers, with clear limits and human oversight.
About AI Learning Assistants
An AI learning assistant can answer common questions, explain concepts, point to the right lesson and help students practise. When grounded in your course content, it supports learning outside class hours.
Design is critical in education. Assistants should stay on topic, cite course material, avoid doing students' assessed work for them and give teachers visibility into how they are used.
What is included in AI Learning Assistants
The typical building blocks. Scope is agreed with you in writing and tailored to your situation.
Pedagogy and policy review
How the assistant should and should not help.
Content grounding
Answers drawn from your course material.
Guardrails
Topic limits, age-appropriate responses and academic integrity rules.
LMS and portal integration
Access where students already learn.
Teacher dashboard
Insight into common questions.
Evaluation
Testing quality with real course questions.
Why teams choose AI Learning Assistants
Support at any hour
Students get help between classes.
Consistent with your teaching
Answers follow your material.
Insight for teachers
See where students struggle.
This is a good fit if
- Students have questions outside class time.
- Teachers answer the same queries repeatedly.
- You want AI with academic safeguards.
Does this sound familiar?
Admissions and records handled on paper
Online learning that students drop out of
Staff without time to learn new tools
Works well with
Other Education Technology services that often come with AI Learning Assistants.
What this looks like in practice
How it fits together
A typical reference architecture, from your data to the people who use it.
- Layer 1Student and staff identity
- Layer 2Learning platform
- Layer 3Assessment and records
- Layer 4Reports for management
Technology we typically work with
Six stages, the same every time
You always know which stage you are in and what comes next.
- Stage 1
Discover
Workshops and stakeholder interviews to define goals and constraints.
- Stage 2
Assess
Current-state review of systems, data, security and spend.
- Stage 3
Architect
Target design, integration map and phased plan with owners.
- Stage 4
Implement
Build in short releases with demos, tests and staging sign-off.
- Stage 5
Optimize
Measure against agreed KPIs and tune performance and cost.
- Stage 6
Manage
Monitoring, support and a monthly review with an account manager.
Security and governance
How the work is kept safe and accountable.
- Student data minimised and access-scoped
- Parental consent where required
- Backups tested before each term
Industries we have worked in
Ways to work together
Scope and price are confirmed in writing by a consultant after discovery.
Assessment
A fixed-scope review that ends in a written roadmap you can keep, with or without us.
- Current-state review
- Ranked priorities
- Written roadmap
Project
Defined scope, milestones and acceptance criteria for building or migrating something specific.
- Phased plan with owners
- Short releases and demos
- Handover and training
Managed
A monthly retainer once it is live: monitoring, support and a regular review.
- Monitoring and backups
- Named account manager
- Monthly review
What clients say about working with us
Our team had tried to sort out AI Learning Assistants alone. Every step had an owner and a date, so we always knew what came next. It removed a lot of stress for us.
We were comparing options for AI Learning Assistants. The work was split into small steps we could review. It felt like working with our own team.
We asked for help with AI Learning Assistants and expected a sales pitch. They told us when something was not a good fit for us. We would happily work with them again.
Our team had tried to sort out AI Learning Assistants alone. Questions were answered directly, including the ones about trade-offs. The documents they left behind are still useful.
We asked for help with AI Learning Assistants and expected a sales pitch. The plan was clear enough to share with our board. We would happily work with them again.
Our AI Learning Assistants project had stalled before we called Bedre. They told us when something was not a good fit for us. The result was simple to explain to everyone.
It can be configured to guide and explain rather than provide finished assessed work.
Data handling is agreed up front and minimised.
Where policy allows, summaries or logs can be made available.
It depends on scope. The Assess stage produces a timeline you can review before committing.
You do. Domains, cloud accounts and data stay in your name wherever possible.
Usually yes. The Architect stage lists connectors and any limits.
Ready to scope AI Learning Assistants?
Tell us the outcome you want. We will bring the plan.