Course preparation
Draft exercises and explanations from an approved syllabus for an instructor to check.
Book a call about course preparationEU-first AI for higher education & research
Give AI learning a workspace of its own.
Help course teams prepare teaching materials and give learners a bounded environment for hands-on work.
For teaching innovation leads, course directors and research operations managers. Start with approved data and a named reviewer.
Illustrative scenarios for evaluation. Not a Matrix customer story or a preconfigured industry solution.
The recurring work
Courses need repeatable environments and reviewed learning material. Research support teams also organize extensive documentation, while privacy and academic responsibility need clear boundaries.
AI workflows for education
Hands-on AI labs
Prepare repeatable agent and internal-app exercises in separate Matrix computers with a defined brief.
Book a call about hands-on ai labsResearch administration
Build a tracker for proposal milestones and summarize public funding-call requirements for staff review.
Book a call about research administrationIllustrative workflow
Your approved sourcesFiles, records and the task brief
Matrix workspaceAgents, tools and a working draft
Your reviewA repeatable, instructor-reviewed AI lab
Matrix / Teaching workspace / AI lab
Make the exercise repeatable.
Instructor workspace · synthetic course data
Learning goal
Build a small research tracker
Compare an agent’s output with the supplied reference and explain the corrections.
Hands-on exercise
Use the sample CSV to build a tracker. Check the saved data and document the review.
Exercise briefMapped to the approved module objectivesDraft
Instructor checkTest setup and expected output firstReview
Approved syllabus · synthetic lab datasetThe inputs and tools
- Approved syllabus and teaching materials
- Synthetic lab data and exercise briefs
- Public funding calls and an administration checklist
Use approved files and repository examples. LMS and research-system integrations need separate assessment; start without student records or unpublished research.
Every pilot begins with an agreed data sample, agent/provider configuration and a named reviewer. An export-based pilot does not establish a live integration.
A bounded first pilot
One course team, one module, two weeks. Instructors test a small set of AI-and-app exercises using synthetic data before involving learners.
What to measure
Review setup effort, reproducibility, factual errors and whether instructors can verify the expected learning outcomes.
Where a person stays in charge
Educators approve learning materials and retain grading and admissions decisions. Student data and confidential research require a separate review before use.
Questions for your evaluation
Can we start with student records or automatic grading?
The proposed pilot starts with instructors and synthetic data. It excludes grading, admissions and student monitoring decisions. Student records and confidential research require a separate privacy, permission and provider review.
Review European hosting and privacy considerations and self-hosting options before introducing sensitive data.
Why this workflow
UNESCO recommends a human-centred approach with attention to privacy, agency and educational suitability. These are proposed instructor-led pilots.
UNESCO: GenAI in education and research
The source establishes sector context. The use cases, pilot scope and evaluation measures above are Matrix’s proposed starting points; performance and fit must be tested with your team.
The workspace behind the workflow.
Keep the task, your files and the tools you build in the same Matrix computer. Start with course preparation, then shape the workspace around your team.
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