PoemDirector: A Multi-Agent Context-Adaptive Instructional Mode Selection Framework for Chinese Classical Poetry Video Generation

Abstract

Classical poetry is a significant component of aesthetics and cultural inheritance in China's K–12 language education, and web-based instructional videos have become the primary way students can learn about classical poetry. However, current approaches have failed to produce both high-quality explanations and large-scale automatic videos that teach classical poetry. Teachers are responsible for ensuring instructional design in semi-automated processes, but the time and expense of production are too great. Although end-to-end fully automated generation is efficient, the logic is unclear and the explanations are shallow because there is no systematic instructional design. The majority of current approaches lack an end-to-end generative framework with a hierarchical explanation structure for deep understanding, instructional intelligence with context-adaptive capabilities, and clear teaching objectives. In this paper, a multi-agent framework called PoemDirector is proposed that combines context-adaptive strategies, multi-layered explanations, and pedagogically grounded end-to-end generation into one framework. Based on the poem and the situation, the director agent in PoemDirector creates a structured creative blueprint and organizes for other agents to collaborate in order to build a link for creating instructional videos on classical poetry. In the meantime, we further establish a multi-dimensional evaluation framework for instructional effectiveness and poetic presentation, and conduct comparative studies based on both this framework and extra objective video quality metrics. The results demonstrated that PoemDirector significantly lowered labor costs and outperformed the baseline in a number of metrics, thereby resolving the conflict between high-quality instruction and mass production.