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) |
Michal Slupczynski |
| Funding | BMBF - Richtlinie zur Förderung von Forschung zur digitalen Hochschulbildung |
| Project Start | March 01, 2021 |
| Project End | July 31, 2024 |
| Status | Finished |
| Research Field | Advanced Community Information Systems (ACIS) |
| Website | https://milki-psy.de |
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.
Related Job offers
HiWi Position in BMBF Project MILKI PSY
- Latency oriented optimization of a sensor based AI infrastructure (Open)
- Mess to Mastery: User-centric redesign of psychomotor teaching experience (Open)
- Need for Speed: Evaluating Feedback Latency in Psychomotor Learning (Finished)
- Rule-Driven Feedback Engine for Psychomotor Learning: From Motion Distance to Error Detection (Finished)
- Enhancing Physical Activity and Social Interaction through Peer-assisted Exergames (Finished)
- Using LLMs to Support Modernization of Legacy Systems (Finished)
- Offloading of IoT Workloads in Peer-to-Peer Networks using WebAssembly (Finished)
- Sensor Based Human Motion Comparison (Finished)
- From Still to Dynamic: A Comparative Analysis of Visualization Techniques for Human Movements (Finished)
- Time-series based Academic Trend and Downtrend Detection (Finished)
- Web Frontend for the Gamification of Habit Forming in Distance Education (Finished)
- Detection of malicious social bots in online social networks using Artificial Immune Systems (Finished)
- Web Frontend for exchange of NFT artwork (Cancelled)