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Use Cases

In Teaching​

DashAI enables instructors of non-computational courses (general education, bridge courses, diploma programs) to incorporate ML as a practical tool:

  • Students experiment with real data without any programming barrier
  • Every action produces visible evidence — charts, metrics, and comparisons
  • Experiments are reproducible: one student can replicate another's work
  • Instructors can design activities where the goal is to compare and justify decisions, not implement algorithms
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DashAI is being adopted in university courses in Chile as a support tool for applied ML teaching.

In Applied Research​

For researchers in non-computational fields (social sciences, health, management) who need ML as a methodological tool:

  • Visual EDA to understand data structure and quality
  • Comparative experimentation with full traceability
  • Downloadable results for reports and publications
  • No dependency on programmers for exploratory analysis

Self-Directed Learning​

For motivated individuals who want to learn ML hands-on:

  • The Workbench loop teaches the real flow of an ML project
  • Errors are instructive — low metrics tell you what to adjust
  • The interface doesn't hide decisions: you always see which models, which parameters, which splits
  • You can iterate freely: each experiment is independent