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KI@MINT

Brandenburgische Technische Universität Cottbus-Senftenberg

KI@MINT

Institution: Brandenburgische Technische Universität Cottbus-Senftenberg Category: Project
Website: https://www.b-tu.de/universitaet/ueber-uns/qualitaet-von-lehre-und-studium/innovative-lehrprojekte/kimint

Short Description

The KI@MINT project at BTU Cottbus-Senftenberg develops practice-oriented teaching offerings for imparting KI competencies to students in the MINT field. Target groups are students, teaching staff, and professionally qualified individuals who are to be capable of acting in the workforce of tomorrow. The main benefit for universities is the creation of sustainable, competence-oriented learning formats supported by teaching lab and learning factory concepts as well as digital platforms.

General Description

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Thematic Classification

Subject Areas

  • Computer Science
  • Mechanical Engineering
  • Civil Engineering
  • Production Economics
  • Applied Mathematics
  • Technology in Science and Philosophy
  • Fluorescence Microscopy
  • Industry 4.0
  • Automation Technology
  • Object Detection
  • Image Analysis
  • Neural Networks
  • Computer Vision
  • 3D Printing
  • Drone Control
  • Sensorics
  • Data Science
  • Algorithmics
  • Artificial Intelligence (AI)
  • Software Development
  • Graphics Card Optimization
  • Neural Network Training
  • Image Processing
  • Computer-Aided Design (CAD)
  • Systematic Investigation of AI Models
  • Processing Large Data Sets
  • Practice-Oriented Teaching Methods
  • Didactics of AI Education
  • Interdisciplinary Research
  • Technology Integration in Teaching
  • Internship Design
  • Production Facilities
  • Object Detection in Industrial Environments
  • Traffic and Rescue Robotics
  • Underwater and Swimming Rescue Systems
  • Text Generators
  • ChatGPT Application
  • Generative AI
  • Institutionalization of AI Modules in Higher Education

Research Fields

  • Artificial Intelligence (AI)
  • Image analysis with weak AI
  • Object detection (e.g., using YOLO)
  • Neural networks
  • AI-based data processing
  • AI in production economics (Industry 4.0)
  • AI in engineering sciences
  • AI-based drone control
  • Sensor platforms and AI
  • AI in automation technology
  • AI in medical imaging (e.g., CT, MRI, X-ray images)
  • AI for search and rescue operations (e.g., in cases of drowning)
  • AI in fluorescence microscopy
  • AI for material recognition (e.g., screws and nuts)
  • AI for facial temperature measurement (e.g., in the context of COVID-19)
  • AI for the detection of skin carcinomas
  • AI for early detection of cataracts

Specializations

  • AI Apprenticeship Lab with drones, sensor platforms, and 3D printers
  • Practice-oriented teaching concept with AI learning factory for production and economic challenges
  • AI competencies in engineering (e.g., civil engineering)
  • Image analysis with weak AI (neural networks, image recognition, data preprocessing)
  • AI-based object detection in industrial and medical applications (e.g., skin detection, eye detection, temperature measurement, material differentiation)
  • Development of teaching and learning formats with didactic support and educational technological assistance
  • Strengthening AI competencies among study applicants, students, and professionally qualified individuals
  • Establishment of a digital AI learning environment (e.g., Jupiter Notebooks, HAWKI access)
  • Self-paced courses on AI and ChatGPT
  • Practical transfer of AI technologies (TensorFlow, YOLO, Transformer Networks)

Keywords

  • KI@MINT - Artificial Intelligence - Apprenticeship Lab - Practice-Oriented - Engineering - Image Analysis - Learning Factory - Didactics - Digital Platform - Self-Learning Course

Funding

Funding Provider: -
Funding Program: Bundesministerium für Bildung und Forschung
Funding Reference: BMBF-01ZZ2201
Funding Period: 01.22 - 12.25
Project Volume: Das Volumen oder "INSUFFICIENT"

Team & Partners

Project Leadership

Prof. Dr. Peer Schmidt (BTU Cottbus-Senftenberg)

Involved Persons

  • Dr. Claudia Börner (Project Management TP 1)
  • Boguslaw Malys (Project Management TP 1)
  • Prof. Michael Breuss (Project Management TP 2)
  • Prof. Douglas Cunningham (Project Management TP 2)
  • Prof. Ulrich Berger (Project Management TP 3)
  • Dr. Marc Gebauer (Project Management TP 3)
  • Prof. Armin Fügenschuh (Project Management TP 4)
  • Prof. Achim Bleicher (Project Management TP 4)
  • Prof. Christian Hentschel (Project Management TP 5)
  • Prof. Silke Michalk (Project Management TP 6)
  • Heike Bartholomäus (Project Management TP 6)
  • Stefan Gohrenz (Bachelor's Program Mechanical Engineering, Bachelor's Thesis within TP 2)
  • Johannes Höna (Master's Program Artificial Intelligence Technology, Master's Thesis within TP 2)
  • Alexander Howel (Bachelor's Program Computer Science, Bachelor's Thesis within TP 2)
  • Patrick Ebert (Bachelor's Program Computer Science, Bachelor's Thesis within TP 2)
  • Dustin Scharf (Bachelor's Program Computer Science, Bachelor's Thesis within TP 2)
  • Slavomíra Schneidereit (Master's Program Mechanical Engineering, Master's Thesis within TP 2)
  • T. Schneid

Affiliated Institutions

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External Partners

  • Polnische Hochschule (im Rahmen eines KI-Workshops mit polnischen Studierenden)
  • EUNICE-University (Teilnahme an der EUNICE Staff Week)
  • Brandenburger Hochschulen (Austausch im Rahmen von "Lessons Learned" mit anderen Hochschulen in Brandenburg)
  • Zentrum der brandenburgischen Hochschulen für Digitale Transformation (ZDT) (Kooperation bei Tagungen und Projekten)
  • Bund-Länder-Initiative "KI in der Hochschulbildung" (Projektbeteiligung im Rahmen der Verstetigungsstrategien)

Project Contents

Goals

  • Development and testing of contemporary, innovative study programs for practice-oriented qualification of specialists in the AI-driven workforce
  • Strengthening AI competencies among students through practice-oriented teaching and learning formats, particularly in STEM subjects
  • Establishment of a sustainable AI training laboratory with technical infrastructure (drones, sensors, 3D printers) and suitable software (e.g. TensorFlow)
  • Creation of practice-oriented teaching concepts such as the AI Learning Factory and integration of AI into engineering problem-solving scenarios
  • Promotion of permeability and awareness of AI topics among prospective students, school pupils, and professionally qualified individuals

Work Packages

  • TP 1: Innovative teaching and learning formats – didactic support and educational technology assistance
  • TP 2: KI Apprenticeship Lab – Algorithms, technologies, and methods: essential and sustainable
  • TP 3: Practice-oriented teaching concept with a KI Learning Factory
  • TP 4: KI competencies for practice-oriented problem solving in engineering
  • TP 5: Image analysis using weak KI
  • TP 6: Strengthening KI competencies in study and qualification offerings

Methods

  • Practice-oriented teaching and learning formats
  • Didactic support and educational technology assistance
  • Establishment of a KI apprenticeship lab with drones, sensor platforms, and 3D printers
  • Use of KI software (e.g. TensorFlow) for controlling technical systems
  • Development of didactic concepts and learning pathways for internships
  • Use of a multi-stage production plant for practical delivery of KI solutions
  • Introduction of micro-projects in engineering to apply KI algorithms
  • Image analysis using neural networks (weak KI) for applications such as skin detection, eye detection, temperature measurement
  • Practical approach to creating and preprocessing training and test data
  • Training and evaluation of neural networks
  • Development of self-learning courses (e.g. Python course, KI in higher education, ChatGPT)
  • Use of Jupiter Notebooks as BTU's own KI environment for teaching activities
  • Introduction of semi-automated support for teaching and learning
  • Conducting further education series (e.g. KI-Werkstatt) for university staff
  • Organization of KI symposia and KI-Mai events for the university public
  • Conducting KI workshops with international students

Expected Outcomes

  • Development and testing of contemporary, innovative study programs for practice-oriented qualification of specialists in the AI-driven working world
  • Creation of a sustainable AI learning environment through the establishment of an AI apprenticeship lab featuring drones, sensor platforms, and 3D printers
  • Implementation of practice-oriented teaching and learning formats, particularly through the AI Learning Factory and the integration of AI into engineering problem-solving scenarios
  • Strengthening of AI competencies among students and professionally qualified individuals through modularly structured, application-oriented teaching offerings
  • Creation and provision of courses on image analysis with weak AI, including practical application of neural networks and data preprocessing
  • Improvement of permeability and attractiveness of study programs for school students, professionally qualified individuals, and prospective students through targeted opening up and awareness-raising regarding AI modules
  • Development of a technological and didactic concept for digital learning platforms and tools to support the AI learning process
  • Ensuring quality and sustainability of teaching offerings through evaluation, quality assurance, and transfer measures
  • Promotion of scientific and practical further education of university staff in the field of AI in teaching
  • Creation of an open exchange space for AI topics in higher education through events such as KI-Werkstatt, KI-Mai, and K

Contact

Contact Person: Prof. Dr. Peer Schmidt
Email: vp-lehre@b-tu.de
Project Website: https://www.b-tu.de/universitaet/ueber-uns/qualitaet-von-lehre-und-studium/innovative-lehrprojekte/kimint

Recorded: 2026-01-11
Source: https://www.b-tu.de/universitaet/ueber-uns/qualitaet-von-lehre-und-studium/innovative-lehrprojekte/kimint

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