摘要
当前,药物分析探究性学习面临探究性主题设计缺乏创新性、实施过程动态支持以及评价方案设计不足等问题。人工智能生成内容(AIGC)凭借其强大的内容生成能力及支撑多轮有意义对话的功能,能够有效协助教师进行多样化与精准化的教学设计,并辅助学生完成深度探究性学习。基于生成式探究学习理念,构建了适用于药物分析课程的生成式探究学习框架,从人机协同的教学设计与学生探究等2个方面详细阐述具体实施流程,并提出了融入AIGC应用能力评价的综合评价方案。实践应用表明,该模式显著提升了学生的探究性学习表现、综合分析能力、学习兴趣和自信心,有效促进了深度学习的实现,可为人工智能时代教学模式的优化提供有益参考。
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 Gener ative 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.
作者
孔兴欣
田清青
刘群群
邱艳明
KONG Xing-Xin;TIAN Qing-Qing;LIU Qun-Qun;QIU Yan-Ming(Department of Pharmacy,Hunan Traditional Chinese Medical College,Zhuzhou 412000,China)
出处
《化学教育(中英文)》
北大核心
2025年第16期84-92,共9页
Chinese Journal of Chemical Education
基金
湖南省“十四五”教育科学规划课题“基于知识图谱的职业院校学生增值评价实施路径研究”(XJK24CZY009)研究成果。
