See "Quantitative Economics with Julia" lecture notes updated to Julia 1.6, VS Code, and GitHub Actions for a summary of the new QuantEcon julia lecture notes release.
A lot of effort was put into software engineering tooling, workflows, etc. in this revision.
My personal feeling is that changing people’s workflows to support reproducibility is our most important goal, and the tools can help. To me, the eventual nail in the coffin of matlab is not its speed (it is actually pretty fast in practice since so many algorithms are dominated by linear algebra) nor its clunky syntax (far superior to python in some cases), but rather that it cannot support modern software engineering.
Julia is a great language for reproducibility and collaboration partially because it is new enough that these things (e.g. reproducible environments, CI, unit testing, collaboration workflows, package discovery) were designed in from its inception. So if a student learns these things correctly with julia, they can apply them with R, Python, Stan, etc.