Designing GenAI-Supported Adaptive Hypermedia for Classical Chinese Poetry Learning: Expert-Informed Design Considerations

Abstract

Learning classical Chinese poetry requires readers to connect lines, imagery, the poet’s biography, place, and historical background. Adaptive hypermedia offers a useful way to organise and guide movement across such linked materials, and generative AI creates new opportunities for adaptive explanation and learner-specific support. It remains unclear, however, how GenAI-supported adaptive hypermedia should be designed for adult poetry learning. We report an expert-informed qualitative study based on semi-structured interviews with 19 participants, including experts in classical Chinese poetry education (n = 11) and experts in GenAI-supported hypermedia design and development (n = 8). Using reflexive thematic analysis, we identify three learning priorities: connected knowledge, cultural-emotional resonance, and sustained interest. We also identify three design challenges: AI answers may be wrong or miss key background, support for different learners remains limited, and learners often receive too little help in building historical and cultural context. Based on these findings, we derive design considerations for GenAI-supported adaptive hypermedia for poetry learning. We then present Poetictok, a prototype that combines place-based exploration, archive cards that learners can reopen later, poem reconstruction tasks, and four role-bounded AI agents. The paper contributes design knowledge for adaptive hypermedia, with particular attention to linked context, adaptive guidance, and visible sources.