LEARN BY WORKING ON REAL QUESTIONS
AI research workshops & consulting
Task-based workshops that help research teams understand tools, limitations and practical methods.
How should university AI research training be organized?
Group workshops can focus on a shared task; departmental training accounts for disciplines and experience levels. Continuing education requires agreed teaching roles, exercises and feedback. Start with learning outcomes that participants can demonstrate and review.
Research groups, departments and university continuing education teams exploring courses or training programs.
We start with participant experience and learning outcomes, then select illustration, assisted writing, analysis or workflow exercises that build transferable skills.
What we can deliver together
- An audience-specific course outline
- Demonstrations and practice materials
- Research group workshops or departmental training
- Agreed follow-up discussion and teaching review
What to prepare
- Participant backgrounds, numbers and experience
- Learning goals and available teaching time
- Devices, access, accounts and practice data
How we evaluate completion
Use agreed exercises and learning outcomes to check whether participants can complete core tasks, explain limitations and review results.
Scope of collaboration
Continuing education partnerships begin with course goals, responsibilities and teaching requirements. Accounts, platform costs and certificates are agreed with the institution.
How collaboration works
- 01Define learning outcomes
- 02Design lessons and exercises
- 03Run or deliver the workshop
- 04Review feedback and improve
Common questions
Can workshops suit non-programmers?
Yes. We can select visual tools and practical tasks without assuming coding experience.
Can we co-develop continuing education courses?
We welcome discussions about courses and training programs, including teaching objectives, responsibilities and institutional review.
Further reading
GOOD QUESTIONS START GOOD PROJECTS
Bring a question.
Let’s work on it.
A model, a repetitive workflow, or research that deserves a clearer story. Start there.
Let’s talk research