Affiliated Research Projects
Cluster MINT-Fit
MINT-Fit – Inspiring STEM in Southwestern Saxony
The MINT-Fit project targets primary school children growing up under challenging circumstances—such as in educationally disadvantaged families, with refugee experience, or in structurally weak regions. Its aim is to provide continuous, age-appropriate access to STEM subjects (Mathematics, Computer Science, Natural Sciences, and Technology), opening up new educational opportunities and supporting their holistic personal development.
The project connects institutions that either already work with children from these target groups or possess recognized STEM expertise. Together, they develop low-threshold, hands-on programs designed to spark curiosity, strengthen skills, and foster long-term enthusiasm for scientific questions.
MINT-Fit is led by Chemnitz University of Technology in close cooperation with regional partners: Don Bosco Sachsen, KINDERVEREINIGUNG Chemnitz e. V., the Johanneum Children and Youth Foundation of the City of Chemnitz, and Phänomenia Stollberg. The project is funded over three years with more than €500,000 by the Federal Ministry of Education and Research.
Planned activities include STEM clubs, holiday camps, experiment days, and science shows, all pedagogically tailored to the needs of primary school children. A special highlight is the MINT-Mobile—a mobile learning space that travels throughout southwestern Saxony as a rolling discovery workshop, reaching children in remote locations and inspiring their interest in science and technology.
Contact: Prof. Dr. Leena Bröll
Chair: Primary Education – Science Teaching
Funding: Federal Ministry of Education and Research
Duration: Until June 2027
PREDIR
Systems, Algorithms, and Cognitive Models for Predicting Individual Human Reasoning Processes (PREDIR)
This research project addresses a central question in cognitive artificial intelligence: How can we predict the conclusions an individual will draw based on only a few observed pieces of information? Unlike classical approaches in cognitive psychology, which primarily focus on average behavior, this project specifically targets individual thinking. The goal is to develop computational models that adapt to individual persons and can predict their future cognitive decisions—even with limited data.
The project integrates perspectives from cognitive science, psychology, and data-driven methods such as recommender systems. It analyzes existing approaches, identifies their limitations, and develops improved models based on these insights. The focus is on both the performance of predictive systems and a deeper understanding of the cognitive mechanisms underlying human thought.
The long-term aim is to create adaptive, learning-capable AI systems that can not only interpret human thinking but also anticipate it on an individual level.
Contact: Prof. Dr. Marco Ragni
Chair: Predictive Analytics
Funding: German Research Foundation (DFG)
Duration: 01 July 2024 – 30 June 2027 (36 months)
Automatische Prozessmodellgenerierung für Kognitive Modellierung
Automatic Process Model Generation for Cognitive Modeling
This project aims at a paradigm shift in cognitive modeling: Instead of using entire theories to describe group behavior, the smallest building blocks of existing theories—so-called atomic cognitive processes—are identified, combined, and individually adapted. Using a formalized space of cognitive operations and accounting for individual differences, the project seeks to automatically generate cognitive models that are highly accurate, individually customizable, and powerful in prediction. This innovative methodology prioritizes predictive capability over theoretical assumptions, opening new avenues for cognitive research on human reasoning.
Contact: Prof. Dr. Marco Ragni
Chair: Predictive Analytics
Funding: German Research Foundation (DFG)
Duration: 01 January 2024 – 31 December 2025 (24 months)
HYDRA
Hybride Entscheidungsunterstützung für Computer Aided Design
HYDRA – Hybrid Decision Support for Computer-Aided Design
Mechanical design places high demands on engineers: in addition to mechanical stability and safety, aspects such as aesthetics, environmental impact, maintenance effort, costs, and recyclability must also be considered. Many of these criteria are difficult to formalize, which is why human intuition and experience continue to play a central role.
The goal of the HYDRA project is to enhance the design process through intelligent, hybrid decision support. Classical CAD systems are combined with an AI-based assistance system, the so-called Completer. Trained using real-world design data, the Completer proposes step-by-step new design options based on the current geometry and individual inputs from the engineer.
Through an interactive overlay, targeted feedback can be provided and specific constraints set—for example, to keep certain parts of the design unchanged or to suggest modifications. Humans remain at the center of the process, retaining full control and decision-making authority, while being effectively relieved of routine tasks.
This type of support is particularly valuable for less experienced designers, helping them achieve high-quality design solutions more quickly and efficiently.
Contacts:
Prof. Dr. Fred Hamker – Chair of Artificial Intelligence
Prof. Dr. Alexander Hasse – Chair of Machine Elements and Product Development
Funding: German Research Foundation (DFG, SPP 2443)
Duration: 01 January 2025 – 31 December 2027 (36 months)
