@article{ZHANG2025, 
author = {Rui ZHANG and Hui ZHAO and Fei JIA and Jianwei ZHANG and Tangan HAN},
title = {THE APPLICATION OF DIGITAL-INQUIRY LEARNING IN SMART UNIVERSITY PHYSICS COURSES},
year = {2025},
journal = {Physics and Engineering},
volume = {35},
number = {6},
pages = {113-118},
keywords = {AI-based inquiry learning, Large language model, machining learning, innovation assessment},
url = {https://www.sciopen.com/article/10.26599/PHYS.2025.9320619},
doi = {10.26599/PHYS.2025.9320619},
abstract = {The rapid advancement of large language models (LLMs) has opened new avenues for innovation in physics teaching. This study focuses on a computational-thinking-driven methodology for creating digital and intelligent inquiry-learning scenarios with LLMs, enabling the rapid development of such scenarios for university-level smart physics courses. Using the projectile motion experiment as an example, we illustrate how AI-based inquiry learning can be employed to assess students' innovative capabilities. By mining the data generated during inquiry activities, we uncover the problem-solving strategies students adopt. Coupled with explainable AI, we can then derive scientifically grounded rubrics for evaluating their inquiry processes, thereby offering a quantifiable means of measuring innovation.}
}