Brainstorming a training programme through case studies

The focus of a CDT should be about the training programme that is provided and the need for a cohort-based approach. Students still undertake a large independent research project as part of the training, but this must be supplemented by additional activities that enhance the training and experience.

To help us think about how we might design our training programme, we have taken a student-oriented approach, where we created a series of case-studies that outline how a hypothetical student has benefited from the training and cohort based approach the our Geospatial AI CDT would provide. These case studies are based around best practice from existing CDTs, the best bits of DTP funding, and unique ideas for how we might create a programme of learning activities that allows our students to flourish as the next leaders in Geospatial AI when they graduate.

One of the big findings from this exercise was the critical nature of the cohort-based approach for our students. Geospatial AI is a big area that involves many different disciplines that would be impossible for an individual to entirely master alone — the cohort-based approach allows our students to learn to work and communicate with others in the same space with vastly different skill-sets and perspectives. Industry/external engagement is also shown to be critical to the successes of our graduates.

If you would like to read about these case studies in more detail, a sample is available here. These draw on students from different backgrounds and show how our CDT trained them to be successful Geospatial AI experts.