Description

Research focus: Quantum technology education research (QTEd), QT-intelligent reality, AI-based personalized learning environments

Quantum technology education (QTEd) will be one of the main drivers of commercial and academic gain in the context of the second quantum revolution. But due to its high abstractness, the relevant concepts and fundamental experiments are complex to understand for students. As genuine quantum properties are hard to visualize and hence to intuitively understand, different graphical representations (e.g., qubit visualizations) have to be developed and studied for specific quantum concepts, e.g., entanglement. Additionally, there is an increasing heterogeneity in students in terms of mathematical competences, prior physics knowledge, and reasoning competences. Therefore, it is beneficial for learning if the environments adapt to the learners and provide personalized learning opportunities.

Therefore we focus on R&D in multimedia learning using advanced educational technology (XR, AI) making invisible and complex relations, processes and phenomena visible and tangible with different types of visualizations for a more intuitive access to abstract concepts in physics education. To measure the effects of multimedia (XR-based) learning environment and create intelligent, (generative) AI-based systems, we develop, apply and combine different formats of measures and instrumentations for studying effects, such as classical concept inventory on learning outcome level and physiological measures (e.g. with eye tracking).

Related Publications:

Coban, A., Dzsotjan, D., Küchemann, S., Durst, J., Kuhn, J., & Hoyer, C. AI support meets AR visualization for Alice and Bob: personalized learning based on individual ChatGPT feedback in an AR quantum cryptography experiment for physics lab courses. EPJ Quantum Technol. 12, 15 (2025).
DOI: doi.org/10.1140/epjqt/s40507-025-00310-z

Rexigel, E., Bley, J., Arias, A., Küchemann, S., Kuhn, J., & Widera, A. (2025).. Investigating the use of multiple representations in university courses on quantum technologies. EPJ Quantum Technol. 12, 22 (2025).
DOI: doi.org/10.1140/epjqt/s40507-025-00327-4

Donhauser, A., Bitzenbauer, P., Qerimi, L., Heusler, S., Küchemann, S., Kuhn, J., & Ubben, M. S. (2024). Empirical insights into the effects of research-based teaching strategies in quantum education. Physical Review Physics Education Research, 20(2), 020601.

Rexigel, E., Qerimi, L., Bley, J., Malone, S., Küchemann, S., & Kuhn, J. (2025). Learning quantum properties with informationally redundant external representations: An eye-tracking study. arXiv preprint arXiv:2501.07389.

Kennel, K., Ishimaru, S., Küchemann, S., Steinert, S., Kuhn, J. & Ruzika, S. Gaze-Based Prediction of Students’ Math Difficulties - A Time Dynamic Machine Learning Approach to Enable Early Individual Assistance. Int J Artif Intell Educ (2025).
DOI: doi.org/10.1007/s40593-024-00447-5