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AI-powered Profile Analysis according to Griesshaber for German at Berufswahlschule Uster

Evoya AI has developed a modern web application for the Berufswahlschule Uster that automates the entire process of profile analysis according to Griesshaber.

Process Automation

Process Automation Overview
Project Overview

Evoya AI has developed a modern web application for the Berufswahlschule Uster that combines several AI-supported steps: digitization of handwritten texts, automatic division into minimal sentence-worthy units (MSE), analysis according to the Griesshaber method, and export of results to an Excel file. Teachers can test and continuously optimize the process via chat agent.

30–60 Min
Time savings per profile analysis

2 Weeks
Project duration

Initial Situation and Challenge

The previous process for profile analysis according to Griesshaber was time-consuming and manual. Handwritten texts had to be transferred to Excel, divided into MSE and categorized – an error-prone and lengthy process. Different handwritings and varying language proficiency made the use of classical OCR technologies difficult.

In addition, the interpretation of the texts required a high degree of sensitivity on the part of the teachers.

Solution and Features

  • Handwriting Digitization: AI models recognize and interpret handwritten texts.
  • Division into MSE: Automatic segmentation of texts into minimal sentence-worthy units.
  • Analysis Creation: Categorization and analysis according to the Griesshaber method.
  • Output to Excel File: Automatic export of results.
  • Continuous Optimization: Centrally managed AI instructions and chat agent for teachers.
Handwriting Digitization (Page 1 of 2)
STEP 1

Handwriting Digitization (Page 1 of 2)

Division into Minimal Sentence-worthy Units (MSE)
STEP 2

Division into Minimal Sentence-worthy Units (MSE)

Analysis Creation
STEP 3

Analysis Creation

Output to Excel File
STEP 4

Output to Excel File

Continuous Optimization
STEP 5

Continuous Optimization

Centrally managed AI instructions that can be easily adapted by teachers, as well as a chat agent for direct use and testing of the application.

Added Value

  • Time Savings: 30–60 minutes per test.
  • Accuracy: Higher consistency thanks to AI analysis.
  • Scalability: Analysis of a large number of students without additional effort.
  • Flexibility: Teachers adapt AI instructions themselves.
  • Future-proofing: Basis for development plans, exercises and interactive tutors.

Outlook

The process does not end with the analysis. The next steps include the development of individual development plans, personalized exercises, and an interactive tutor that accompanies students based on the development plan.

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