One institute announces many AI projects and is considered more advanced than another. I propose to observe objectives, accessibility and documented results.
In the example, a small project allows a more verifiable learning than a very large trial. The number of applications does not alone measure educational quality.
The comparison should describe conditions and limits, avoiding rankings built only on visibility. Innovation becomes useful when it improves concrete possibilities for students and professionals.