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On 19 September, the AI4Work Review Meeting took place with the participation of all project partners and the European Commission reviewers. This mid-term review marked a key milestone, providing the opportunity to present the progress achieved so far and to highlight the first tangible results. The meeting was highly successful, with the reviewers giving very positive feedback on the achievements and the overall direction of AI4Work.
The highlight of the day was the presentation of the early prototypes for the five project pilots. These prototypes represent the first integrated solutions developed within AI4Work and offer a clear preview of how the platform can enhance working conditions across different sectors. These include AI-driven decision support, human-centred digital models, and adaptive interaction mechanisms between humans, AI, and robots.
As a technology provider for two of the pilots of the project, BioAssist presented its contribution to the development of these prototypes:
- The Healthcare pilot, implemented in collaboration with HYGEIA Hospital, aims to improve safety, efficiency, and staff support. The prototype focuses on the shift scheduling module, designed to accommodate nursing staff needs, integrating stress monitoring and AI-driven support. It includes data collection via wearables to measure stress levels throughout each shift. A web dashboard for managers and occupational doctors aggregates analytics on individual and group stress metrics, employee satisfaction with scheduling preferences, and compliance with legal and organizational constraints. Occupational doctors have access to aggregated data per shift schedule as well as pseudonymized individual nurse information, including demographics, questionnaire responses, and daily/weekly stress indices. This allows them to monitor the staff well-being and provide tailored support. The system visualizes data in interactive charts and enables occupational doctors to review AI-generated stress mitigation suggestions before these are shared with nurses.
- The Education pilot, coordinated by the University of Piraeus, focuses on academic workload management by integrating stress monitoring to provide informed and adaptive adjustments to optimize study schedules. Students provide demographic information through an initial questionnaire, select their courses, while also using wearables to continuously collect stress-related data. Professors have access to a dashboard that displays aggregated daily and weekly stress metrics for each course, tracks student progress, and visualizes feedback on the perceived difficulty of the material. Based on these insights, they can make informed adjustments to the workload for the following weeks. In parallel, occupational doctors can supervise the process through a dedicated dashboard that presents aggregated analytics, timelines of data collection, questionnaire results, and wearable data visualized in charts. The system also enables doctors to review AI-generated stress management recommendations before these are communicated to students, ensuring that guidance is accurate and tailored to their needs.
In summary, the early prototypes showcased during the review a wide range of functionalities: applications for collecting stress-related data through wearables and questionnaires, AI tools for stress level assessment, prediction, and personalized stress management recommendations, as well as dashboards for visualizing analytics. They also include interfaces that support shift scheduling and academic workload adjustments, combined with data-driven insights for occupational doctors, enabling them to provide targeted support and interventions. These prototypes set the stage for the upcoming pilot studies with real users, during which the solutions will be tested in operational environments to validate their effectiveness and value.
The next steps of the project will focus on improving the prototypes, scaling up the pilot studies, and further strengthening collaboration among partners.
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This project has received funding from the European Union’s Horizon Europe research and innovation programme under Grant Agreement No 101135990. The website reflects only the view of the author(s) and the Commission is not responsible for any use that may be made of the information it contains.