KIDaS

In Germany, a total of almost €49 billion was spent on integration and social assistance in 2024. Of this, €28.7 billion was spent on integration assistance services for over one million people. Nevertheless, finding suitable services for people seeking help is often a lengthy and inefficient process. Social workers mainly research available services manually, existing platforms require a great deal of maintenance and are not sufficiently widespread in the social sector. There is a lack of data-driven approaches to recognize needs at an early stage, identify gaps in services and develop new services in a targeted manner. People seeking help usually only have access to information via service providers, which severely restricts their independent participation. Digital offers of help such as health or inclusion apps are hardly taken into account by existing platforms. All of this means that available services often remain unused despite high demand and people seeking help have to wait a long time for suitable support.

KIDaS is developing an AI-supported data platform that mediates, analyzes and systematically promotes the innovation of social services. The platform brings together service seekers, providers and suppliers and enables the targeted exchange of data on supply, needs and demand in order to better balance supply and demand. AI agents based on generative AI automate the collection and maintenance of supply data, significantly reducing manual effort. An innovative service sonar uses the analysis of publicly accessible data sources to identify trends and unmet needs and generates suggestions for new social services. A matching service with an offer configurator brings people seeking help together with suitable services in a targeted manner. Scientifically, the project is developing design principles for the use of agentic AI on platforms, a method for data-driven innovation of social services and a transferable process model for establishing digital service platforms. In the long term, the platform is intended to act as a public welfare-oriented hub to promote the transfer of knowledge between stakeholders and establish a competence network of supra-local social welfare providers.

Four partners are working together in an iterative and practical manner: FAU Erlangen-Nuremberg and the University of Kassel as scientific partners and ANLEI-Service GmbH and PRODATO Integration Technology GmbH as implementation partners. The consortium is complemented by associated practice partners: LWV Hessen, Bathildisheim e.V., Landschaftsverband Rheinland and Landschaftsverband Westfalen-Lippe. The research approach combines action design research with value-sensitive design and ensures that technical innovation and ethical principles are given equal consideration. During so-called e-Van tours, the project team visits local social institutions to gather requirements, obtain feedback and establish a competence network. The development takes place step by step: from requirements analysis and technology preparation to the development of the platform and AI-based data crawlers through to the central services and the opening strategy.

The project provides an expandable data platform with integrated AI components that significantly reduce the manual maintenance effort, precisely match people seeking help with services and support decision-makers with data-based suggestions for new services. In addition, transferable scientific methods for the use of agentic AI, for data-driven service innovation and for the sustainable establishment of digital service platforms are being developed. A targeted opening strategy and an incentive system will position the platform as an incubator for social tech innovations and, not least, strengthen digitalization in the social sector.

http://kidas-projekt.de/

KIDaS is funded as part of the funding measure "Nutzen in Daten-Ökosystemen: Competition - Communication - Cooperation (DigiNutzenDat)".

The research project has a duration of three years (01.01.2026 - 31.12.2028).

• Prof. Dr. Jan Marco Leimeister
• Dr. Mahei Li
• M.Sc. Antonia Tolzin
• M.Sc. Eileen Feind