A progressive prompt-based image-generative AI approach to promoting students’ achievement and perceptions in learning ancient Chinese poetry

Abstract

In conventional ancient Chinese poetry learning, students tend to be under-motivated and fail to understand many aspects of poetry. As generative artificial intelligence (GAI) has been applied to education, image-GAI (iGAI) provides great opportunities for students to generate visualized images based on their descriptions of poems, and to situate students in a context similar to what a poem describes. In addition, the progressive prompt is a strategy that can progressively provide students with clues and guidance in technology-enhanced learning environments. Hence, this study proposed a progressive prompts-based image-GAI (PP-iGAI) approach to support students’ ancient Chinese poetry learning. To evaluate its effectiveness, the present study employed a quasi-experiment design and recruited 80 fifth-grade elementary school students to engage in one of two conditions: one class was assigned as the experimental group and adopted the PP-iGAI approach, while the other class was assigned as the control group and used the conventional prompt-based iGAI (C-iGAI) approach. The results revealed that the PP-iGAI approach could better promote students’ learning achievement, extrinsic motivation, problem-solving awareness, critical thinking, and learning performance. In addition, no significant differences were found in the two groups’ cognitive load. Moreover, the results of the interview disclosed the learning perceptions and experiences of both groups. Accordingly, the present study can provide a reference not only for ancient Chinese poetry learning but also for the application of GAI in educational fields for future research.

Keywords

• generative artificial intelligence
• image-gai
• progressive prompt
• ancient chinese poetry learning