xiaozhi-teach-student-intake

Structured student intake for independent teachers works by collecting minimal anonymized data, conducting 5W needs interviews, establishing diagnostic baselines, and designing trial lessons to generate formal archives. This systematic approach ensures complete profiles and consistent follow-up.

Privacy-driven data minimization for student onboarding involves collecting only essential anonymized aliases and schedule preferences instead of extensive personal records. This enforces privacy protection while still enabling effective diagnostic baselines and trial-lesson design.

To run a 5W needs interview and diagnostic assessment, initiate an audition flow that systematically captures student needs, establishes diagnostic baselines, and designs tailored trial lessons. The process automatically generates a formal student archive for ongoing supervision.

Yes, you can integrate student intake archives with solo-dashboard and analytics tools. The skill automatically interfaces with these platforms during archive creation, forming a complete teaching starting point for ongoing supervision and data analysis.

The best way to manage auditions while protecting privacy is using a data-minimization approach that collects anonymized aliases and essential contact information only. This converts auditions into structured profiles while enforcing privacy-driven data handling throughout the intake process.