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收稿：2026-04-13，
修回：2026-07-22，
录用：2026-08-10，
网络首发：2026-08-12，
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目的
2
图像辅助词汇习得是国际中文教育的重要教学策略，但现有提示词生成方法未能有效融合国际中文教育等级标准等领域专业知识，导致所生成图像在词义指向性、认知难度匹配性与教学适用性等方面存在显著不足。针对上述问题，本文提出一种面向国际中文教育词汇图像生成的渐进式提示词扩展模型（ Progressive prompt expansion model for vocabulary image generation，PPEM-VIG）。
方法
2
PPEM-VIG将词汇的认知等级、难度等级、词性、释义等离散教学要素统一建模为结构化教学要素集，并通过渐进式两阶段优化策略自动生成语义准确、结构规范且教学适用的图像提示词。监督学习阶段基于LLaMA模型以模板结构一致性损失与语言流畅性损失为约束，实现提示词的结构化生成；强化学习阶段基于监督微调LLaMA模型并增加模板内容相关性与基于CLIP的词图语义一致性奖励，对提示词进行深度优化，以实现提示词的拓展生成。
结果
2
本文构建了首个面向国际中文教育场景的词汇图像提示词数据集（international chinese language education vocabulary image prompt dataset， ICLE-VIPD）。实验结果表明，PPEM-VIG在模板结构覆盖率、CLIP语义一致性得分、及教师主观评价等核心指标上较最优的基线模型分别提升了4.10%，3.13%，4.54%。
结论
2
本文提出的渐进式监督-强化优化范式为词汇教学图像资源智能化生成上提供可借鉴的技术路线。
Objective
2
Image-assisted vocabulary acquisition has long been regarded as an effective pedagogical strategy in international Chinese language education， especially for helping learners establish intuitive connections between lexical meaning， visual representation， and communicative context. Compared with purely verbal explanation， instructional images can reduce cognitive load， support dual coding， and enhance learners’ retention of Chinese vocabulary. This is particularly important because Chinese words often involve polysemy， contextual dependence， cultural connotation， and different levels of visual expressibility. With the rapid development of text-to-image generation models， such as DALL-E， Stable Diffusion， and Midjourney， teachers are now able to generate customized visual materials at a lower cost and with greater flexibility. However， current prompt generation and prompt expansion methods are mainly designed for general-purpose image generation. They usually emphasize visual details， artistic style， lighting， composition， or aesthetic quality， but pay insufficient attention to pedagogical constraints in international Chinese language education. As a result， images generated from ordinary prompts may fail to accurately represent the target word meaning， may not match learners’ cognitive level or vocabulary difficulty， and may even introduce irrelevant or misleading visual elements. Existing methods also lack an effective mechanism for integrating domain-specific knowledge， such as vocabulary difficulty levels， cognitive stages， parts of speech， semantic explanations， and the Chinese proficiency grading standards for international Chinese language education. To address these limitations， this paper proposes a progressive prompt expansion model for vocabulary image generation， named PPEM-VIG， which aims to generate semantically accurate， structurally standardized， and pedagogically appropriate image prompts for Chinese vocabulary teaching.
Method
2
The proposed PPEM-VIG model formulates vocabulary image prompt generation as a structured mapping problem from pedagogical elements to image-generation prompts. Specifically， discrete teaching-related information， including cognitive grade， difficulty level， part of speech， and semantic explanation， is organized into a structured pedagogical element set. This representation enables the model to explicitly capture the instructional requirements of each vocabulary item rather than treating the word as an isolated text input. On this basis， PPEM-VIG adopts a progressive two-stage optimization framework. In the first stage， supervised learning is used to train a LLaMA-based structured prompt generation model. The model learns the mapping from the pedagogical element set to a structured prompt template consisting of several functional blocks， such as scene description， core vocabulary representation， primary visual features， auxiliary expression， pedagogical purpose， and style or cultural adaptation. To ensure that the generated prompt follows the expected template and remains readable， this stage introduces template structure consistency loss and language fluency loss. The former constrains the completeness and order of prompt blocks， while the latter encourages natural and coherent language generation.In the second stage， reinforcement learning is introduced to further optimize the supervised fine-tuned model. Unlike conventional prompt expansion methods that focus mainly on textual enrichment， this stage incorporates multiple reward signals related to pedagogical relevance and cross-modal alignment. The reward function includes template structure consistency， language fluency， template content relevance， and CLIP-based text-image semantic consistency. Among these rewards， the content relevance reward evaluates whether the generated prompt accurately reflects the input vocabulary and its teaching elements， while the CLIP-based reward measures whether the image generated from the prompt is semantically consistent with the vocabulary definition. Through this multi-objective reinforcement learning process， PPEM-VIG can refine prompts beyond surface-level template completion and improve their ability to guide text-to-image models toward educationally meaningful visual outputs. The progressive combination of supervised learning and reinforcement learning allows the model to first acquire stable structural generation ability and then enhance semantic alignment and visual teaching applicability.
Result
2
To support model training and evaluation， this study constructs the first vocabulary image prompt dataset specifically designed for international Chinese language education， named ICLE-VIPD. The dataset is built around vocabulary items selected from the Chinese proficiency grading standards for international Chinese language education and focuses on high-frequency nouns and verbs across different difficulty levels. Each sample contains a pedagogical element set and a corresponding structured prompt. The prompt annotation process considers the different visual representation principles of nouns and verbs. For nouns， the prompt design emphasizes core visual features， typical scenes， contrastive elements， and cultural appropriateness. For verbs， the prompt design focuses on action participants， action process， affected objects， result states， and contextual cues. Expert annotators with backgrounds in international Chinese language teaching participate in the construction and verification process to ensure that the prompts are both semantically accurate and suitable for classroom use.Experiments are conducted to evaluate PPEM-VIG from multiple perspectives， including template structure coverage， language fluency， block relevance， CLIP semantic consistency， and teachers’ subjective evaluation. The results show that PPEM-VIG achieves superior performance compared with representative baseline models. In particular， compared with the strongest baseline model， PPEM-VIG improves template structure coverage by 4.10%， CLIP semantic consistency score by 3.13%， and teachers’ subjective evaluation score by 4.54%. These improvements indicate that the proposed model not only follows the structured prompt template more reliably but also generates prompts that better support text-to-image models in producing semantically aligned teaching images. The teacher evaluation results further confirm that PPEM-VIG-generated prompts are more effective in terms of word-meaning accuracy， teaching adaptability， clarity of expression， and completeness of instructional information. Ablation analysis also demonstrates the necessity of the two-stage framework. The supervised learning stage substantially improves structural completeness and pedagogical element coverage， while the reinforcement learning stage further enhances cross-modal semantic consistency and practical teaching value. Removing individual reward components leads to performance degradation， which verifies the contribution of each reward signal to the overall model.
Conclusion
2
The proposes PPEM-VIG， a progressive prompt expansion model for vocabulary image generation in international Chinese language education. By incorporating structured pedagogical elements and combining supervised learning with reinforcement learning， the model effectively addresses the limitations of existing general-purpose prompt generation methods in educational scenarios. PPEM-VIG can generate prompts that are not only visually descriptive but also aligned with vocabulary meaning， learner cognition， difficulty level， and teaching objectives. The construction of ICLE-VIPD further provides a valuable benchmark resource for future research on vocabulary-oriented image generation， prompt engineering， and intelligent teaching material generation. Overall， the proposed progressive supervised-reinforcement optimization paradigm offers a practical technical route for the intelligent generation of vocabulary teaching images and contributes to the digital and intelligent transformation of international Chinese language education.
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