The integration of new literature into the English curriculum remains a challenge since educators often lack scalable tools to rapidly evaluate readability and adapt texts for diverse classroom needs. This study proposes to address this gap through a multimodal approach that combines transformer-based text classification with linguistic feature analysis to align texts with UK Key Stages. Eight state-of-the-art Transformers were fine-tuned on segmented text data, with BERT achieving the highest unimodal F1 score of 0.75. In parallel, 500 deep neural network topologies were searched for the classification of linguistic characteristics, achieving an F1 score of 0.392. The fusion of these modalities shows a significant improvement, with every multimodal approach outperforming all unimodal models. In particular, the ELECTRA Transformer fused with the neural network achieved an F1 score of 0.996. Unimodal and multimodal approaches are shown to have statistically significant differences in all validation metrics (accuracy, precision, recall, F1 score) except for inference time. The proposed approach is finally encapsulated in a stakeholder-facing web application, providing non-technical stakeholder access to real-time insights on text complexity, reading difficulty, curriculum alignment, and recommendations for learning age range. The application empowers data-driven decision making and reduces manual workload by integrating AI-based recommendations into lesson planning for English literature.

翻译：将新文献整合至英语课程体系仍面临挑战，因为教育工作者往往缺乏可扩展的工具来快速评估文本可读性并针对多样化课堂需求调整文本。本研究提出通过多模态方法解决这一缺口，该方法结合基于Transformer的文本分类与语言特征分析，使文本与英国关键学段标准相匹配。我们在分段文本数据上对八种前沿Transformer模型进行微调，其中BERT在单模态模式下取得最高的F1分数0.75。同时，针对语言特征分类任务搜索了500种深度神经网络拓扑结构，获得0.392的F1分数。多模态融合显示出显著改进，所有多模态方法均优于单模态模型。特别是融合ELECTRA Transformer与神经网络的模型取得了0.996的F1分数。除推理时间外，单模态与多模态方法在所有验证指标（准确率、精确率、召回率、F1分数）上均呈现统计学显著差异。最终，所提出的方法被封装为面向利益相关者的网络应用程序，为非技术背景的利益相关者提供关于文本复杂度、阅读难度、课程匹配度及适读年龄范围建议的实时洞察。该应用通过将基于人工智能的建议整合至英语文学课程设计，赋能数据驱动决策并降低人工工作量。