Abstract Current inquiry-based learning in pharmaceutical analysis faces challenges such as lack of innovation in inquiry topic design,insufficient dynamic support during implementation,and inadequate evaluation plan design.With its powerful content generation ability and the advantage of carrying out multiple rounds of meaningful dialogues,Artificial Intelligence in Generative Content(AIGC)can effectively collaborate with teachers to carry out diversified and precise instructional design,and assist students in completing in-depth inquiry learning.Based on the concept of generative inquiry learning,this study constructs an overall framework of generative inquiry learning for pharmaceutical analysis courses,elaborates the specific implementation process from the aspects of human-computer collaborative teaching design and student inquiry perspectives,and proposes a comprehensive evaluation plan incorporating AIGC application ability assessment.The practical application of the pharmaceutical analysis course shows that this mode can significantly improve students' inquiry learning performance,comprehensive analysis ability,learning interest and self-confidence,effectively promote the achievement of in-depth learning,and can provide a useful reference for the optimization of teaching modes in the era of artificial intelligence.
KONG Xing-Xin, TIAN Qing-Qing, LIU Qun-Qun, QIU Yan-Ming. Application of the Generative Inquiry-Based Learning Mode in Pharmaceutical Analysis Courses Based on AIGC[J]. Chinese Journal of Chemical Education, 2025, 46(16): 84-92.
