Network Satellite: AI-FIT<\/h2><\/div>AI-Powered Questionnaire Integration for Transformation<\/h4><\/div><\/div><\/div><\/div><\/div>Summary<\/h3><\/div><\/div><\/div>spexa GmbH implements sustainable health and performance management in companies with the goal of promoting both operational efficiency and employee well-being. This is based on data collected from employees within the company. This data is used to calculate the proprietary, scientifically based Business Health Index (BHI\u00ae), which provides an aggregated and anonymized snapshot of the company\u2019s status. <\/p>\n
The AI-FIT network satellite project uses the HUMAINE Toolbox to analyze whether the questionnaire\u2014which has thus far been administered as an online form\u2014can be effectively supported and improved in terms of user acceptance through AI-based methods, and whether AI-based text analysis of free-text entries can be performed.<\/p>\n<\/div><\/div><\/div>
![](\"https:\/\/humaine.info\/wp-content\/uploads\/2026\/07\/KI-Fit-Abb-scaled.png\" "\\"KI-Fit")
<\/span><\/div><\/div><\/div>Description of the Approach<\/h3><\/div>Data is collected through online surveys with predefined questions about health and the work environment. A comprehensive survey consisting of multiple-choice questions is specifically supplemented by opportunities for free-text responses. <\/p>\n
Data quality and completeness play a crucial role in BHI calculations. This is where AI-based solutions come into play. Using the \u201cPotential Analysis\u201d and \u201cCapturing User-Centered Interfaces\u201d tools, three use cases were identified: <\/p>\n
\n- Chatbot to assist with comprehension issues – Goal: Provides explanations for questions or technical terms, improves understanding, reduces the dropout rate, and improves data quality.<\/li>\n
- Automated anonymization of free-text entries\u2014Goal: removes personally identifiable information, preserves the meaning of the response, builds trust in data protection, and improves the quality of the data.<\/li>\n
- Avatar to Boost Motivation to Participate – Goal: To demonstrate the impact of participation on individual users and the company as a whole, and to provide information about other possible concrete steps the company can take in the area of workplace health management.<\/li>\n<\/ol>\n<\/div><\/div><\/div>
Goals & Results<\/h3><\/div>Focus on Use Case 2 – Pipeline Consisting of a PII Detector and an LLM<\/b><\/p>\n\n- Identification and Masking of Sensitive Information
\n(PII Detector + LLM)<\/li>\n - Converting the masked text into continuous text (LLM)<\/li>\n<\/ol>\n
The older woman in my department is often very unfriendly to me.<\/i><\/p>\n
\u2193<\/p>\n
[WORK_RELATION_ROLE]<\/b> from<\/i> [WORK_RELATION_ROLE]<\/i><\/b> is very often very unfriendly to me.<\/i><\/p>\n
\u2193<\/p>\n
I very often encounter extreme unfriendliness toward me in my work environment.<\/i><\/p>\n<\/div><\/div><\/div><\/div><\/div>
AI-Powered Questionnaire Integration for Transformation<\/h4><\/div><\/div><\/div><\/div><\/div>Summary<\/h3><\/div><\/div><\/div>spexa GmbH implements sustainable health and performance management in companies with the goal of promoting both operational efficiency and employee well-being. This is based on data collected from employees within the company. This data is used to calculate the proprietary, scientifically based Business Health Index (BHI\u00ae), which provides an aggregated and anonymized snapshot of the company\u2019s status. <\/p>\n
The AI-FIT network satellite project uses the HUMAINE Toolbox to analyze whether the questionnaire\u2014which has thus far been administered as an online form\u2014can be effectively supported and improved in terms of user acceptance through AI-based methods, and whether AI-based text analysis of free-text entries can be performed.<\/p>\n<\/div><\/div><\/div>
<\/span><\/div><\/div><\/div>Description of the Approach<\/h3><\/div>Data is collected through online surveys with predefined questions about health and the work environment. A comprehensive survey consisting of multiple-choice questions is specifically supplemented by opportunities for free-text responses. <\/p>\n
Data quality and completeness play a crucial role in BHI calculations. This is where AI-based solutions come into play. Using the \u201cPotential Analysis\u201d and \u201cCapturing User-Centered Interfaces\u201d tools, three use cases were identified: <\/p>\n
\n- Chatbot to assist with comprehension issues – Goal: Provides explanations for questions or technical terms, improves understanding, reduces the dropout rate, and improves data quality.<\/li>\n
- Automated anonymization of free-text entries\u2014Goal: removes personally identifiable information, preserves the meaning of the response, builds trust in data protection, and improves the quality of the data.<\/li>\n
- Avatar to Boost Motivation to Participate – Goal: To demonstrate the impact of participation on individual users and the company as a whole, and to provide information about other possible concrete steps the company can take in the area of workplace health management.<\/li>\n<\/ol>\n<\/div><\/div><\/div>
Goals & Results<\/h3><\/div>Focus on Use Case 2 – Pipeline Consisting of a PII Detector and an LLM<\/b><\/p>\n\n- Identification and Masking of Sensitive Information
\n(PII Detector + LLM)<\/li>\n - Converting the masked text into continuous text (LLM)<\/li>\n<\/ol>\n
The older woman in my department is often very unfriendly to me.<\/i><\/p>\n
\u2193<\/p>\n
[WORK_RELATION_ROLE]<\/b> from<\/i> [WORK_RELATION_ROLE]<\/i><\/b> is very often very unfriendly to me.<\/i><\/p>\n
\u2193<\/p>\n
I very often encounter extreme unfriendliness toward me in my work environment.<\/i><\/p>\n<\/div><\/div><\/div><\/div><\/div>
Summary<\/h3><\/div><\/div><\/div>spexa GmbH implements sustainable health and performance management in companies with the goal of promoting both operational efficiency and employee well-being. This is based on data collected from employees within the company. This data is used to calculate the proprietary, scientifically based Business Health Index (BHI\u00ae), which provides an aggregated and anonymized snapshot of the company\u2019s status. <\/p>\n
The AI-FIT network satellite project uses the HUMAINE Toolbox to analyze whether the questionnaire\u2014which has thus far been administered as an online form\u2014can be effectively supported and improved in terms of user acceptance through AI-based methods, and whether AI-based text analysis of free-text entries can be performed.<\/p>\n<\/div><\/div><\/div>
<\/span><\/div><\/div><\/div>Description of the Approach<\/h3><\/div>Data is collected through online surveys with predefined questions about health and the work environment. A comprehensive survey consisting of multiple-choice questions is specifically supplemented by opportunities for free-text responses. <\/p>\n
Data quality and completeness play a crucial role in BHI calculations. This is where AI-based solutions come into play. Using the \u201cPotential Analysis\u201d and \u201cCapturing User-Centered Interfaces\u201d tools, three use cases were identified: <\/p>\n
\n- Chatbot to assist with comprehension issues – Goal: Provides explanations for questions or technical terms, improves understanding, reduces the dropout rate, and improves data quality.<\/li>\n
- Automated anonymization of free-text entries\u2014Goal: removes personally identifiable information, preserves the meaning of the response, builds trust in data protection, and improves the quality of the data.<\/li>\n
- Avatar to Boost Motivation to Participate – Goal: To demonstrate the impact of participation on individual users and the company as a whole, and to provide information about other possible concrete steps the company can take in the area of workplace health management.<\/li>\n<\/ol>\n<\/div><\/div><\/div>
Goals & Results<\/h3><\/div>Focus on Use Case 2 – Pipeline Consisting of a PII Detector and an LLM<\/b><\/p>\n\n- Identification and Masking of Sensitive Information
\n(PII Detector + LLM)<\/li>\n - Converting the masked text into continuous text (LLM)<\/li>\n<\/ol>\n
The older woman in my department is often very unfriendly to me.<\/i><\/p>\n
\u2193<\/p>\n
[WORK_RELATION_ROLE]<\/b> from<\/i> [WORK_RELATION_ROLE]<\/i><\/b> is very often very unfriendly to me.<\/i><\/p>\n
\u2193<\/p>\n
I very often encounter extreme unfriendliness toward me in my work environment.<\/i><\/p>\n<\/div><\/div><\/div><\/div><\/div>
spexa GmbH implements sustainable health and performance management in companies with the goal of promoting both operational efficiency and employee well-being. This is based on data collected from employees within the company. This data is used to calculate the proprietary, scientifically based Business Health Index (BHI\u00ae), which provides an aggregated and anonymized snapshot of the company\u2019s status. <\/p>\n
The AI-FIT network satellite project uses the HUMAINE Toolbox to analyze whether the questionnaire\u2014which has thus far been administered as an online form\u2014can be effectively supported and improved in terms of user acceptance through AI-based methods, and whether AI-based text analysis of free-text entries can be performed.<\/p>\n<\/div><\/div><\/div>
<\/span><\/div><\/div><\/div>Description of the Approach<\/h3><\/div>Data is collected through online surveys with predefined questions about health and the work environment. A comprehensive survey consisting of multiple-choice questions is specifically supplemented by opportunities for free-text responses. <\/p>\n
Data quality and completeness play a crucial role in BHI calculations. This is where AI-based solutions come into play. Using the \u201cPotential Analysis\u201d and \u201cCapturing User-Centered Interfaces\u201d tools, three use cases were identified: <\/p>\n
\n- Chatbot to assist with comprehension issues – Goal: Provides explanations for questions or technical terms, improves understanding, reduces the dropout rate, and improves data quality.<\/li>\n
- Automated anonymization of free-text entries\u2014Goal: removes personally identifiable information, preserves the meaning of the response, builds trust in data protection, and improves the quality of the data.<\/li>\n
- Avatar to Boost Motivation to Participate – Goal: To demonstrate the impact of participation on individual users and the company as a whole, and to provide information about other possible concrete steps the company can take in the area of workplace health management.<\/li>\n<\/ol>\n<\/div><\/div><\/div>
Goals & Results<\/h3><\/div>Focus on Use Case 2 – Pipeline Consisting of a PII Detector and an LLM<\/b><\/p>\n\n- Identification and Masking of Sensitive Information
\n(PII Detector + LLM)<\/li>\n - Converting the masked text into continuous text (LLM)<\/li>\n<\/ol>\n
The older woman in my department is often very unfriendly to me.<\/i><\/p>\n
\u2193<\/p>\n
[WORK_RELATION_ROLE]<\/b> from<\/i> [WORK_RELATION_ROLE]<\/i><\/b> is very often very unfriendly to me.<\/i><\/p>\n
\u2193<\/p>\n
I very often encounter extreme unfriendliness toward me in my work environment.<\/i><\/p>\n<\/div><\/div><\/div><\/div><\/div>
Data is collected through online surveys with predefined questions about health and the work environment. A comprehensive survey consisting of multiple-choice questions is specifically supplemented by opportunities for free-text responses. <\/p>\n
Data quality and completeness play a crucial role in BHI calculations. This is where AI-based solutions come into play. Using the \u201cPotential Analysis\u201d and \u201cCapturing User-Centered Interfaces\u201d tools, three use cases were identified: <\/p>\n
- \n
- Chatbot to assist with comprehension issues – Goal: Provides explanations for questions or technical terms, improves understanding, reduces the dropout rate, and improves data quality.<\/li>\n
- Automated anonymization of free-text entries\u2014Goal: removes personally identifiable information, preserves the meaning of the response, builds trust in data protection, and improves the quality of the data.<\/li>\n
- Avatar to Boost Motivation to Participate – Goal: To demonstrate the impact of participation on individual users and the company as a whole, and to provide information about other possible concrete steps the company can take in the area of workplace health management.<\/li>\n<\/ol>\n<\/div><\/div><\/div>
Goals & Results<\/h3><\/div>
Focus on Use Case 2 – Pipeline Consisting of a PII Detector and an LLM<\/b><\/p>\n
- \n
- Identification and Masking of Sensitive Information
\n(PII Detector + LLM)<\/li>\n - Converting the masked text into continuous text (LLM)<\/li>\n<\/ol>\n
The older woman in my department is often very unfriendly to me.<\/i><\/p>\n
\u2193<\/p>\n
[WORK_RELATION_ROLE]<\/b> from<\/i> [WORK_RELATION_ROLE]<\/i><\/b> is very often very unfriendly to me.<\/i><\/p>\n
\u2193<\/p>\n
I very often encounter extreme unfriendliness toward me in my work environment.<\/i><\/p>\n<\/div><\/div><\/div><\/div><\/div>
- Identification and Masking of Sensitive Information
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