結合生成式人工智慧與自然語言處理技術之「合作科學探究想法收斂自動回饋鷹架」系統開發與初步評估
Author(s):
Ying-Tien Wu (Graduate Institute of Network Learning Technology, National Central University)
Xuan-Hong Ye (Graduate Institute of Network Learning Technology, National Central University)
Li-Jen Wang (Language Center, National Central University)
Abstract:
This study developed an “Automatic Feedback Scaffold” system based on natural language processing technology, aimed at facilitating students’ knowledge refinement and deep learning during collaborative scientific inquiry. By incorporating meta-discourse, the system provides structured guidance and reflection mechanisms throughout the collaborative inquiry process. It helps students effectively organize and examine their ideas, thereby enhancing their understanding and reflection on the learning content. The study involved 54 fifth-grade elementary school students over a four-week collaborative scientific inquiry activity. Machine evaluation (using ROUGE-SU9) and manual evaluation by an experienced elementary school teacher were conducted to verify the system’s effectiveness. The results indicated that the system effectively supports students’ knowledge refinement processes during collaborative inquiry, while also improving their inquiry abilities and learning outcomes. Machine evaluation results demonstrated that the system-generated summaries achieved high standards in terms of coverage, relevance, and consistency, ensuring the timely delivery of valuable feedback that further promotes effective meta-discourse and self-reflection. Teacher evaluation results also supported the system’s practicality, particularly in its stability and applicability in facilitating students’ inquiry activities and knowledge refinement. In summary, this study confirms the potential of generative artificial intelligence and natural language processing technology to support collaborative scientific inquiry and promote knowledge innovation.
Keywords:
generative artificial intelligence (GenAI)、collaborative scientific inquiry、automated feedback scaffold、natural language processing (NLP) technology、knowledge building
