Effects of AI-generated drawing on students’ learning achievement and creativity in an ancient poetry course

诗歌 创造力 背景(考古学) 理解力 班级(哲学) 模式(计算机接口) 数学教育 心理学 教学方法 视觉艺术 美女 视觉学习 控制(管理) 生成语法 古代艺术 美学 主动学习(机器学习) 古代文学 群(周期表)
作者
Hui-Chun Chu, Chia-Ying Hsu, Chun-Chieh Wang
出处
期刊:DOAJ: Directory of Open Access Journals - DOAJ 被引量:3
标识
DOI:10.30191/ets.202504_28(2).tp03
摘要

In ancient poetry courses, students are expected to learn literary skills and the ability for interpreting ancient poems, from which they can gain insight into the historical context and appreciate the beauty of the poems. However, due to the gaps between ancient and modern living contexts, students often encounter difficulties in comprehending ancient texts. Scholars have suggested that visualizing learning scenarios could enhance learners’ comprehension and knowledge retention, implying that engaging students in learning by illustrating the context of ancient poems could encourage them to explore the contexts in that historical background and comprehend the contents. Therefore, a generative Artificial Intelligence (GenAI) drawing mode is proposed in the present study. In this learning mode, students describe the characters, events, and scenes in the poems in words, and GenAI creates visual representations to immerse students in the contexts of the ancient poems. An experiment was conducted with two classes of students from a high school. One class was the experimental group consisting of 31 students, who used GenAI drawing mode to assist their learning of ancient poems. Another class was the control group consisting of 29 students, who used the conventional drawing mode. The experimental results showed that the experimental group had better learning achievement than the control group. Moreover, for the students in the experimental group, the higher creative ones showed better self-efficacy than the lower creative ones. Additionally, the drawings by the experimental group had rich contents with more details, including emotions, contexts, objects, and expressions.
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