The Impact of Generative AI on Chinese Poetry Instruction: Enhancing Students' Learning Interest, Collaboration, and Writing Ability

Introduction

Teaching poetry writing, an essential aspect of language learning that integrates creative expression and critical thinking, often presents challenges. Students may struggle with inspiration or find it difficult to express themselves effectively (Liao, 2022). This is especially relevant in Chinese poetry instruction, where linguistic nuances and poetic traditions pose additional hurdles for young learners.

Traditional methods of teaching Chinese poetry often rely on rote memorization and the analysis of classical texts, which can lead to disengagement and limit students’ opportunities for creative exploration (Lee, 2019). Such approaches may not accommodate the diverse learning styles and needs of students, hindering their ability to connect with and appreciate the beauty and depth of Chinese poetry. Moreover, the emphasis on traditional forms and structures can sometimes constrain students’ creative expression, limiting their ability to experiment with language and explore new poetic possibilities (Creely & Waterhouse, 2024).

However, a growing body of research suggests that alternative pedagogical approaches can more effectively foster students’ creativity and engagement in Chinese poetry instruction. In Taiwan, for example, creative thinking strategies have shown promise in improving students’ writing abilities, particularly in cohesion, coherence, and grammatical accuracy (Wen et al., 2023). Teachers’ multicultural experiences and cultural intelligence positively influence creative teaching practices, with variations observed across Asia-Pacific countries (Wei et al., 2022). Self-regulated learning and translanguaging approaches have also been explored to enhance L2 creative writing in multilingual contexts (Yang, 2024). In mainland China, creative writing education has expanded rapidly, with efforts to develop “sinicized” theories and localized textbooks (Gao, 2021). Poetry has been used to cultivate critical thinking skills in literature classrooms (Azizi et al., 2022), and frameworks for integrating critical thinking into argumentative writing have been proposed for Chinese college students (Lu & Swatevacharkul, 2021).

Building on these promising developments, this study explores the potential of generative artificial intelligence (AI) to enhance Chinese poetry instruction. Generative AI tools offer a dynamic, engaging approach that fosters creativity, collaboration, and critical thinking. These tools provide students with personalized learning experiences, adapting to their individual needs and preferences. They also facilitate collaborative learning by enabling students to co-create and share their work with peers, fostering a sense of community. Moreover, AI-powered tools offer real-time feedback and suggestions, helping students refine their writing skills and develop their unique poetic voice.

Organizing poetry by themes can enhance the learning experience by breaking away from the conventional historical narrative and replacing monotonous individual instruction with a more dynamic approach (Hurst et al., 2013). This thematic approach allows students to explore connections between different poems and time periods, deepening their understanding of the cultural and historical contexts in which the poems were written. It also helps students engage with the emotional and thematic content of the poems on a personal level, making the learning experience more meaningful and relevant.

This study draws on established cooperative learning frameworks, such as those outlined by Johnson and Johnson (2018), along with recent research in AI education. It examines the role of generative AI technology in fostering creativity, problem-solving skills, literary sensibility, and information literacy (Liu et al., 2023) within the specific domain of Chinese poetry instruction. With the advancement of generative AI technologies like Copilot, these tools provide visual support that can effectively inspire students’ creative expression, particularly in teaching imagery in poetry (Hedley, 2024). The ability of these tools to generate visuals based on textual prompts is especially valuable in bridging the gap between the abstract concepts of poetry and their visual representations, helping students better grasp the essence of poetic imagery.