• Since ​​​​​02/2022: Project leader of Simulierte Welten and CAMMP at KIT
• Since ​​​​​02/2022: PostDoc at the Department of Mathematics, KIT
• ​​​​​05/2019 – 02/2022: PhD in mathematics education, KIT
• 05/2018 – 05/2019:  Research assistant, RWTH Aachen, Lehrstuhl II für Mathematik
• 02/2017 – 07/2017: Teacher at a high-school in Aachen, Germany
• 08/2016 – 12/2016: Semester abroad, Universidad Nacional Autónoma de México (UNAM), Mexico City
• 08/2015 – 02/2018: M. Ed. RWTH Aachen, Mathematics and Chemistry in a teacher training program
• 09/2015 – 10/2015: Internship at the Colegio Humboldt, San José, Costa Rica
• 03/2014 – 04/2014: Internship at the German School Montevideo, Uruguay
• 10/2012 – 08/2015: B. Sc. RWTH Aachen, Mathematics and Chemistry in a teacher training program

2022

Kindler, S., Schönbrodt, S. & Frank, M. (in press). Von der Schulmathematik zu künstlichen neuronalen Netzen [From school mathematics to artificial neural networks]. Beiträge zum Mathematikunterricht 2022.

Schönbrodt, S. & Frank, M. (in press). Klassifizierungsprobleme: Maschinelles Lernen und KI im Mathematikunterricht [Classification problems: Machine Learning and AI in mathematics education]. Beiträge zum Mathematikunterricht 2022.

Schönbrodt, S. & Frank, M. (2022). Data Science and Machine Learning in mathematics education: High-school students working on the Netflix Prize. Proceedings of CERME12. https://hal.archives-ouvertes.fr/hal-03754716/

Schönbrodt, S. (2022). Erneuerbare Energien – Modellierung und Optimierung eines Solarkraftwerks [Renewable Energies – Modeling and Optimization of a Solar Power Plan]. In Frank, M. & Roeckerath, C. (eds.) Neue Materialien für einen realitätsbezogenen Mathematikunterricht 9. Realitätsbezüge im Mathematikunterricht. Springer. https://doi.org/10.1007/978-3-662-63647-3_3

Gerhardt, M., Hattebuhr, M., Schönbrodt, S. & Wohak, K. (2022). Aufbau und Einsatzmöglichkeiten des Lehr- und Lernmaterials [Structure and possible applications of the teaching and learning material]. In Frank, M. & Roeckerath, C. (eds.) Neue Materialien für einen realitätsbezogenen Mathematikunterricht 9. Realitätsbezüge im Mathematikunterricht. Springer. https://doi.org/10.1007/978-3-662-63647-3_2

Frank, M., Roeckerath, C. & Schönbrodt, S. (2022). Einführung [Introduction]. In Frank, M. & Roeckerath, C. (eds.) Neue Materialien für einen realitätsbezogenen Mathematikunterricht 9. Realitätsbezüge im Mathematikunterricht. Springer. https://doi.org/10.1007/978-3-662-63647-3_1

Schönbrodt, S. & Hofmann, S. (2022). Mathematische Modellierungswochen – auch online! [Mathematical modeling weeks - also online!] Mitteilungen der Deutschen Mathematiker-Vereinigung, 30(1), S. 46–50. https://doi.org/10.1515/dmvm-2022-0016

Schönbrodt, S. (2022). Optimierungsprobleme in der mathematischen Modellierung: Grundlegende Aspekte und Chancen aus Sicht der Mathematikdidaktik – herausgestellt an aktuellen Problemen aus der Forschung zu künstlicher Intelligenz und erneuerbaren Energien [Optimization problems in mathematical modeling: Fundamental aspects and opportunities from the perspective of mathematics education – highlighted by current problems from research on Artificial Intelligence and Renewable Energies]. Dissertation, Fakultät für Mathematik, KIT. https://doi.org/10.5445/IR/1000143530

Schönbrodt, S., Wohak, K. & Frank, M. (2022): Digital Tools to Enable Collaborative Mathematical Modeling Online. Modelling in Science Education and Learning, 15(1), S. 151–174, https://doi.org/10.4995/msel.2022.16269.

2021

Schönbrodt, S. & Frank, M. (2021): Digitales Lernmaterial zur Netflix Challenge [Digital learning material on the Netflix challenge], In Hein, K., Heil, C., Ruwisch, S. & Prediger, S. (eds.). Beiträge zum Mathematikunterricht 2021. Münster: WTM Verlag.

Schönbrodt, S., Camminady, T. & Frank, M. (2021): Mathematische Grundlagen der Künstlichen Intelligenz im Schulunterricht - Chancen für eine Bereicherung des Unterrichts in linearer Algebra [Mathematical foundations of artificial intelligence in school lessons: Opportunities for enriching the teaching of linear algebra]. Mathematische Semesterberichte. Springer. https://doi.org/10.1007/s00591-021-00310-x

Wohak, K., Sube, M., Schönbrodt, S., Frank, M. & Roeckerath, C. (2021). Authentische und relevante Modellierung mit Schülerinnen und Schülern an nur einem Tag?! [Authentic and relevant modeling with students in just one day?!]. In Bracke, M., Ludwig, M. & Vorhölter, K. (eds.) Neue Materialien für einen realitätsbezogenen Mathematikunterricht 8. Realitätsbezüge im Mathematikunterricht. Springer. https://doi.org/10.1007/978-3-658-33012-5_4

2018 – 2020

Schönbrodt, S. & Frank, M. (2020). Schüler/innen forschen zu erneuerbaren Energien – Optimierung eines Solarkraftwerks [Students conducting research on renewable energies – Optimization of a solar power plant.]. In Siller, H.-S., Weigel, W. and Wörler, J. F. (eds.), Beiträge zum Mathematikunterricht, S. 1534–1534, Münster. WTM-Verlag.

Schönbrodt, S. (2019). Maschinelle Lernmethoden für Klassifizierungsprobleme – Perspektiven für die mathematische Modellierung mit Schülerinnen und Schülern [Machine learning methods for classification problems – Perspectives for mathematical modeling with students]. Springer Spektrum. https://doi.org/10.1007/978-3-658-25137-6

Frank, M., Richter, P., Roeckerath, C. & Schönbrodt, S. (2018). Wie funktioniert eigentlich GPS? - Ein Computergestützter Modellierungsworkshop [How does GPS actually work? - A computer-based modeling workshop]. In Greefrath, G. & Siller, H.-S. (eds.), Digitale Werkzeuge, Simulationen und mathematisches Modellieren. Realitätsbezüge im Mathematikunterricht. Springer. https://doi.org/10.1007/978-3-658-21940-6_7

• Opening the Blackbox – ein allgemeinverständlicher Einblick in die Grundlagen der KI und Perspektiven für die Einbindung in den schulischen Unterricht
  Talk in the lecture series "Opportunities and risks of digital transformation“ of the Center for School Quality and Teacher Education and KIT
  June 28, 2023, Karlsruhe

• How much mathematical modeling is in AI? 
  Talk in the Paderborn Colloquium on Data Science and Artificial Intelligence in School (with Martin Frank)
  Mai 17, 2023, online

• AI education as a starting point for interdisciplinary STEM projects
  Talk at the IAMIT Workshop 2023,
  March 2023, Karlsruhe

• Mathematische Grundlagen der Künstlichen Intelligenz im Schulunterricht – Chancen für eine Bereicherung des Unterrichts in linearer Algebra
  Plenary talk at ISTRON teachers day,
  November 2022, Karlsruhe

• Authentische Modellierung am Beispiel von Data Science und Künstlicher Intelligenz
  Invited talk in the working group meeting of ISTRON at the GDM-Conference 2022
  September 2022, Frankfurt

• Klassifizierungsprobleme: Maschinelles Lernen und KI Mathematikunterricht
  Talk in the im Minisymposium "Data Science" at the GDM-Conference 2022
  September 2022, Frankfurt

• Mathematische Grundlagen von Maschinellem Lernen und KI im Schulunterricht!? – Perspektiven aus Sicht der Mathematikdidaktik
  Invited talk at the Department of Mathematics and Statistics, University of Konstanz
  March 25, 2022, online.

• Data Science & Machine Learning in Mathematics Education – High-school students working on the Netflix Prize
  Talk at the CERME12 conference 
  February 2022, online

• Digitales Lernmaterial zur Netflix Challenge (Sek. II)
  Invited talk at the didactical seminar of the University of Freiburg
  October 26, 2021, Freiburg

• Digitales Lernmaterial zur Netflix Challenge – Data Science & Maschinelles Lernen im Mathematikunterricht
  Talk at the GDM-Jahrestagung 2021
  March 2021, online

• Schüler/innen forschen zu erneuerbaren Energien – Optimierung eines Solarkraftwerks (Sek I und II)
  Poster presentation at the GDM-Jahrestagung 2020
  September 2020, online

• Solarenergieforschung mit Schüler/innen im Rahmen eines computergestützten Projekttages – Optimierung der Spiegel in einem Fresnelkraftwerk (Sek I/II)
  Presentation at the teacher's day of the ISTRON conference 2019
  September 2019, Berlin

• Perspectives on teaching the Mathematics of Machine Learning to high-school students
  Talk at ICTMA 19 conference
  July 2019, Hong Kong

• Teaching and training concept CAMMP – Supporting the establishment of mathematical modeling in school teaching in a sustainable way by implementing a teacher training concept
  Poster presentation at the ICSE Conference Educating the Educators III (with K. Wohak)
  October 2019, Freiburg

• Chancen für Machine Learning im Mathematikunterricht (Sek. II)
  Presentation at the teacher's day of the ISTRON conference 2018 
  October 2018, Würzburg

• Komplexe Modellierung: Chancen für Maschinelles Lernen im Mathematikunterricht", March
  Talk at the GDMV-Jahrestagung 2018
  March 2018, Paderborn 

Training of (prospective) teachers

• SS 23: Seminar Fundamentals of Artificial Intelligence and its Application in all School Subjects, KIT
• 03/2023: Workshop at the teacher training day Digitale Mathematische Werkzeuge (DMW), KIT
• WS 22/23: Blockseminar Fundamentals of Artificial Intelligence and its Application in all School Subjects, University of Konstanz
• 11/2022: Workshop on the ISTRON teachers day, KIT
• WS 22/23: Seminar Digital-based learning contexts of mathematics education, KIT
• 09/2022: Teacher training Perspectives of Machine Learning for mathematics education, Referat für Bildung und Sport Landeshauptstadt München
• WS 21/22: Co-organization of the seminar Fundamentals of Artificial Intelligence and its Application in all School Subjects, University of Konstanz
• SS 21: Invited workshop for student teachers in the context of the seminar Application-Oriented Mathematics Education, TU Darmstadt
• 11/2020: Teacher training in the context of the MINT Kongress (lecture), online 
• SS 20: Invited workshop for student teachers within the seminar Application and Modeling, RWTH Aachen University
• SS 20: Workshop for student teachers in the context of the seminar Digital Tools for Mathematics Teaching, KIT
• WS 20/21: Supervision of a student group in the context of the seminar Individual Subject Didactics Projects, KIT
• SS 20: Supervision of a group of students in the context of the seminar Individual Subject Didactics Projects, KIT
• WS 19/20: Invited workshop for student teachers within the seminar Application and Modeling, RWTH Aachen University

Supervised theses

• Hoeffer, K. (in progress): Activity recognition on smartphones – Development of learning material for computer-based mathematical modelling projects, Master thesis, KIT.
• Rantzau, L. (2021): Recommender systems based on neighborhood methods – mathematical-scientific discussion and development of digital learning material on the Netflix Challenge for students at secondary level, Bachelor thesis, KIT.
• Hoeffer, K. (2020): Development of teaching material on solar energy in the context of an interdisciplinary mathematical modeling project, Bachelor thesis, KIT.
• Schmidt, L. (2019): Machine learning: automatic image recognition with mathematics?! ­– A teaching-learning module in the context of a mathematical modeling day for secondary school students, Master's thesis, RWTH Aachen University.

School projects

• 09–12/2022: Organization and implementation of a project course for girls on mathematics and AI.
• SS 21: Design and implementation of the KIT mathematics introductory  course for high-school students.
• 10/2019: Design and implementation of mathematical modeling days in Tijuana, Mexico.
• Since 2019: Organization and supervision of various mathematical modeling weeks for high-school students (CAMMP week)
• Since 2015: Organization, design and supervision of several mathematical modeling days for high-school students (CAMMP day, RWTH Aachen and KIT)
• 02–07/2017: Teacher at Einhard-Gymnasium, Aachen, Germany

Further teaching

• 2018 and 2019: Organization and supervision of two mathematical modeling weeks (CAMMP week Pro) for students of Mathematics, CES and Simulation Sience, RWTH Aachen University.
• Tutor for Higher Mathematics I, RWTH Aachen University

• Research on mathematical modeling in high-schools in the context of the student program CAMMP
• Development and testing of teaching and learning materials around Data Science and Artificial Intelligence