MILKI-PSY – Multimodales Immersives Lernen mit KI für Psychomotorische Fähigkeiten

December 16th, 2021

MILKI-PSY aims to create AI-powered, data-rich, multimodal, immersive learning environments for independent learning of psychomotor skills. In doing so, a cross-domain approach is emerging that enables multimodal recording of expert activities and using these recordings as blueprints for learners. With the help of AI-supported analyses, learning progress is to be supported through automated error detection and generated, individual feedback. This creates holistic, innovative learning environments for learning psychomotor skills, in which personalised, AI-supported learning support enables individual learning processes based on complex data analyses.

Manager(s) Ralf Klamma
Michal Slupczynski
Funding BMBF - Richtlinie zur Förderung von Forschung zur digitalen Hochschulbildung
Status Running
Research Field Advanced Community Information Systems (ACIS)

The COVID-19 pandemic has shown that many teaching/learning activities can be carried out without physical presence. This is hardly true for psychomotor skills: their development, as they are necessary in many disciplines (e.g. medicine, engineering, chemistry, artistic activities, sports) requires hands-on practice, direct feedback and reflection. In order to achieve the desired learning successes, personnel supervision and material input are therefore indispensable. Both increase costs and limit the scaling possibilities of the study programmes concerned: experts are rare and expensive, and the use of materials causes further costs.

Sub-project of the RWTH: Secure and Scalable Cloud Data Infrastructure

The aim of the sub-project “Secure and Scalable Cloud Data Infrastructure” for MILKY-PSY is to create and permanently operate a platform for the acquisition, storage, processing and presentation of multimodal real-time data. To achieve this in concrete terms, RWTH will work closely with the collaborative partners as part of the project. In the work package, the necessary conditions for the management of an agile development process and the inclusion of all project participants as well as all affected parties will be created. For this purpose, the DevOpsUse methodology developed at the chair will be used, further developed and evaluated. As a technical platform, open source solutions will be merged into a highly scalable data management infrastructure and delivered to the shared cloud data infrastructure under the highest security standards for the operation of the infrastructure using the latest DevOps tools.