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.
Zhang et al. (Mon,) studied this question.