AI-Enhanced Science Education: From Data to Physical Laws

Ervin Szabolcs Murvai; Krisztián Hriczó; Attila Károly Varga

doi:10.2991/978-94-6239-823-8_13

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AI-Enhanced Science Education: From Data to Physical Laws

Authors

Ervin Szabolcs Murvai¹^{, *}, Krisztián Hriczó², Attila Károly Varga³

¹Institute of Automation and Infocommunication, University of Miskolc, Miskolc, Hungary

²Institute of Mathematics, University of Miskolc, Miskolc, Hungary

³Institute of Automation and Infocommunication, University of Miskolc, Miskolc, Hungary

^*Corresponding author. Email: ervin.szabolcs.murvai@student.uni-miskolc.hu

Corresponding Author

Ervin Szabolcs Murvai

Available Online 4 November 2026.

DOI: 10.2991/978-94-6239-823-8_13 How to use a DOI?
Keywords: SINDy; IoT sensors; bridging Physics & IT; physical laws
Abstract: This research explores the integration of Artificial Intelligence (AI) and Internet of Things (Iot) technologies into modern science education to bridge the”abstraction gap” between digital data and physical reality. By leveraging data-driven algorithms, the project transforms everyday smart devices into professional-grade scientific instruments, moving beyond the “black box” usage of 21st-century technology. The methodology employs advanced models such as Long Short-Term Memory (LSTM) networks for thermodynamic forecasting and the SINDy algorithm for the sparse identification of underlying physical laws from noisy kinematic data.

Furthermore, the study demonstrates how AI-enhanced sensors can uncover imperceptible environmental phenomena, such as nocturnal background radiation fluctuations, thereby expanding the scope of human perception in the classroom. Utilizing a Docker-based containerization, the research ensures that complex computational environments are accessible and reproducible for both students and educators. Ultimately, this work provides a scalable framework for interdisciplinary learning, empowering students to shift from passive memorization to active scientific discovery by “rediscovering” the fundamental laws of nature through their own data.
Copyright: © 2026 The Author(s)
Open Access: Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title: Proceedings of the Innovation in Engineering Education 2026 Conference (IEE 2026)
Series: Atlantis Highlights in Social Sciences, Education and Humanities
Publication Date: 4 November 2026
ISBN: 978-94-6239-823-8
ISSN: 2667-128X
DOI: 10.2991/978-94-6239-823-8_13 How to use a DOI?
Open Access: Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

ris enw bib

TY  - CONF
AU  - Ervin Szabolcs Murvai
AU  - Krisztián Hriczó
AU  - Attila Károly Varga
PY  - 2026
DA  - 2026/11/04
TI  - AI-Enhanced Science Education: From Data to Physical Laws
BT  - Proceedings of the Innovation in Engineering Education 2026 Conference (IEE 2026)
PB  - Atlantis Press
SP  - 145
EP  - 157
SN  - 2667-128X
UR  - https://doi.org/10.2991/978-94-6239-823-8_13
DO  - 10.2991/978-94-6239-823-8_13
ID  - Murvai2026
ER  -

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