[Translate to English:] Sina Shahmoradi

I am a postdoctoral researcher in the project Machine Learning for Adaptive Teaching (MATE) at the Tübingen Center for Digital Education. In MATE, I work at the interface of machine learning and classroom practice: together with teachers, I co-design and evaluate AI-based systems, such as LLM-based tutors and task generators, that support adaptive teaching in real classrooms.

My research focuses on classroom orchestration, that is, how teachers manage learning activities in real time, and on how technology can support them in doing so. In my PhD at EPFL (CHILI Lab), I studied teachers who use educational robots in mathematics lessons. I designed and developed a learning dashboard for robot-mediated classrooms, and evaluated it with eleven teachers and more than 200 students in Swiss primary and secondary schools.

After my PhD, I researched extended reality and social robots for learning at PHBern and then led, for two years, the engineering of a cloud-based learning management system used daily in schools. Building on this combination of research and engineering, I am interested in how teachers and AI can share the orchestration of students' learning, and in building AI tools that are not only effective but also usable and sustainable in everyday school life.

I am always happy to connect with teachers, schools and researchers interested in co-designing or testing AI-based tools for the classroom.

Since 10/2026

Postdoctoral researcher (machine learning), project MATE

Tübingen Center for Digital Education (TüCeDE), University of Tübingen

07/2024 – 09/2026

Lead Software Engineer

Hazu EdTech Technologies, Switzerland – cloud-based learning management system for schools

01/2023 – 02/2024

Postdoctoral researcher

Institute for Research, Development and Evaluation, PHBern, Switzerland

10/2018 – 12/2022

PhD in Robotics, Control and Intelligent Systems

CHILI Lab, EPFL, Switzerland – thesis: “Supporting Teachers' Orchestration in Robot-mediated Classrooms”; supervised by Prof. Pierre Dillenbourg and Dr. Barbara Bruno; part of the EU project ANIMATAS

02/2017 – 09/2018

Research associate

Djavad Movafaghian Neuro-rehabilitation Center, Tehran, Iran

10/2014 – 01/2017

MSc in Mechatronics

Sharif University of Technology, Tehran, Iran

10/2010 – 09/2014

BSc in Mechanical Engineering

K. N. Toosi University of Technology, Tehran, Iran

Journal articles

Dobricki, M., Rihs, M., & Shahmoradi, S. (2025). Learning eco-friendly food choice using extended reality: An exploratory investigation. Frontiers in Virtual Reality, 6, 1498770. https://doi.org/10.3389/frvir.2025.1498770

Shahmoradi, S., Kothiyal, A., Bruno, B., & Dillenbourg, P. (2023). Evaluation of teachers’ orchestration tools usage in robotic classrooms. Education and Information Technologies. https://doi.org/10.1007/s10639-023-11909-z

Shahmoradi, S., & Bagheri Shouraki, S. (2018). Evaluation of a novel fuzzy sequential pattern recognition tool (fuzzy elastic matching machine) and its applications in speech and handwriting recognition. Applied Soft Computing, 62, 315–327. https://doi.org/10.1016/j.asoc.2017.10.036

Conference papers

Shahmoradi, S., Kothiyal, A., Olsen, J. K., Bruno, B., & Dillenbourg, P. (2020). What teachers need for orchestrating robotic classrooms. In Addressing Global Challenges and Quality Education. EC-TEL 2020 (pp. 87–101). Springer. https://doi.org/10.1007/978-3-030-57717-9_7

Dillenbourg, P., Shahmoradi, S., & Olsen, J. K. (2020). Opening the doors of classrooms for technologies: Measures for orchestrability. DEStech Transactions on Social Science, Education and Human Science (ICEDDE 2019). https://doi.org/10.12783/dtssehs/icedde2019/33673

Shahmoradi, S., Olsen, J. K., Haklev, S., Johal, W., Norman, U., Nasir, J., & Dillenbourg, P. (2019). Orchestration of robotic activities in classrooms: Challenges and opportunities. In Transforming Learning with Meaningful Technologies. EC-TEL 2019. Springer. https://doi.org/10.1007/978-3-030-29736-7_57

Nasir, J., Norman, U., Johal, W., Olsen, J. K., Shahmoradi, S., & Dillenbourg, P. (2019). Robot analytics: What do human-robot interaction traces tell us about learning? In 2019 28th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN). IEEE. https://doi.org/10.1109/RO-MAN46459.2019.8956465

Shahmoradi, S., & Bagheri Shouraki, S. (2017). A fuzzy sequential locomotion mode recognition system for lower limb prosthesis control. In 2017 Iranian Conference on Electrical Engineering (ICEE). IEEE. https://doi.org/10.1109/IranianCEE.2017.7985417

Shahmoradi, S., Zare, A., & Behzadipour, S. (2017). Fatigue status recognition in a post-stroke rehabilitation exercise with sEMG signal. In 2017 24th Iranian Conference on Biomedical Engineering (ICBME). IEEE. https://doi.org/10.1109/ICBME.2017.8430264

Dissertation

Shahmoradi, S. (2023). Supporting teachers’ orchestration in robot-mediated classrooms [Doctoral dissertation, EPFL, no. 9835]. https://doi.org/10.5075/epfl-thesis-9835