AI from learning to how the university runs.
GoSchool's solutions help in three areas: how students learn, how instructors work, and how the institution runs.
It can be a ready building block, a specific integration, or something built for that university alone.
AI that does more than give an answer.
Students can use AI tied to their own course and university environment — for learning, for practice and for finding information.
AI tutor
Course-bound AI working from the material the instructor supplied.
- ask questions
- ask for an explanation
- clarify concepts
- look for connections
- keep working on what they have not understood yet
Practice
AI can do more than explain: it can help with practice and self-checking.
- matched to the course material
- matched to the student's current questions
- following the instructor's instructions
University knowledge
A student does not necessarily need to know which system or document holds the answer.
- course information
- regulations
- study information
- institutional knowledge
- information about services
Used where it suits them
We do not want to herd every student into yet another separate chat application.
- on the web
- in a mobile app
- in an embedded interface
- from a compatible external AI client
AI for the instructor's own work.
An instructor does not only have to decide whether students may use AI. They can also create AI solutions for their own work that fit their course and their method.
Your own course AI
The AI is then not a general chatbot but part of the course.
- which material it works from
- what role it takes
- how it answers
- what limits it keeps
Course and material development
- structuring material
- producing examples
- developing practice material
- varying assignments
- explanations at several levels
Teaching workflows
Repeating tasks can get their own AI workflows.
- text processing
- preparing course material
- supporting feedback
- administrative preparation
- gathering information
Skills and recipes
What works does not have to be reinvented by every instructor. A shared library can be built inside the institution.
- proven prompts
- agent skills
- workflows
- teaching use cases
AI in how the university runs.
Most university AI opportunities do not begin at the chatbot. The value often appears where AI can reach the right institutional knowledge or work with the systems already running.
Institutional knowledge
Access can be fitted to institutional permissions.
- internal documentation
- regulations
- process descriptions
- organisational knowledge
- frequently sought information
Administrative AI
For tasks that today take a lot of manual searching, copying or repetition.
- gathering information
- supporting administration
- processing documents
- internal assistants
- preparing processes
AI connected to existing systems
There is no need to replace the university's existing systems.
- LMS
- student information system
- document store
- internal application
- data source
- API
Agents and workflows
An agent does not necessarily only answer. With the right permissions it may be able to:
- search for information
- collect data from several systems
- prepare a task
- start the next step of a workflow
- prepare an operation for human approval
Nothing has to move into a new system.
AI becomes genuinely useful when it can reach the systems and functions the university already works in.
For that we use APIs, MCP and custom integrations.
API
We connect existing systems to AI solutions through standard or custom APIs.
MCP
Institutional data and functions can be made available to compatible AI clients and agents. For some tasks a student or a member of staff can reach institutional capabilities from the AI client they already use.
Custom connectors
Where a system has no ready integration, we can build a connector or an MCP server for it.
Access always follows the use case and the permission. MCP does not mean the AI automatically reaches every institutional system.
- Fitted to the institutional environment
- SSOconnecting to the existing identity system.
- Permissionsby role, course or organisational unit.
- White labelthe institution's own brand and domain.
- Web and mobilea dedicated interface where one is needed.
- Model choicematched to the use case and the requirements.
- Deploymentcloud, private, or on-premise where justified.
No ready module does not mean no good solution.
Universities differ in their systems and their processes. We do not try to force every problem into the same product.
Where a use case has no ready component, we design and build what the task needs.
- a custom agent
- an MCP server
- an internal assistant
- a workflow
- a system integration
- a custom web interface
- a white-label application
- an institutional skill
- a specialised knowledge base
First we understand the problem. Only then do we decide whether it needs a ready component, an integration or custom development.
You don't have to solve the whole university at once.
The best first step is usually one well-bounded use case.
- a course
- a department
- an internal knowledge base
- an administrative process
- a system integration
- an agent
- a workshop
Use case
We find where there is a real problem or opportunity.
Pilot
We build something that works at small scale.
Real use
We try it with users.
Measurement
We look at whether it works and whether it creates value.
Scaling
What works can be built on.
We are not starting from a new concept.
GoSchool's first solutions were course-bound AI tutors. The technical, educational and research experience from those is what the wider institutional solutions are built on.
Which problem would be worth trying AI on?
No finished specification needed. Show us the process, the system or the task, and we can look together at whether AI is worth connecting to it, and how.