Writing Individualized Education Programs (IEPs) is a high-labor, knowledge-intensive document burden; English-language research has demonstrated that generative AI can significantly reduce drafting time, yet automated IEP generation in Traditional Chinese remains virtually unexplored due to domain data scarcity, strict privacy regulations, and the absence of local evaluation benchmarks. We propose a low-resource fine-tuning pipeline centered on Corpus-Grounded Feature Diffusion (CGFD): (1) 25 dual-expert high-score seed transcripts are selected via a tau threshold with flag-aware score caps; (2) a FeatureProfile (sentence length, structure, quantification templates) is extracted from seeds and injected into LLM prompts alongside Verbalized-Sampling-style diversity control to drive diffusion; (3) 15 expert gold seeds are used as diffusion anchors, targeting 585 samples; 567 valid diffusion samples are obtained, yielding a 582-sample training set used to fine-tune Breeze-7B with QLoRA; (4) schema-constrained inference via Grammar-Constrained Decoding (GCD) enforces a hierarchical SMART Goal Ladder schema at inference time. Ablation results on a 55-sample schema stress set reveal an unexpected finding: GCD is counterproductive under Traditional Chinese token budgets -- the no-GCD path achieves 100% schema pass rate at 34% lower median latency, outperforming GCD on both reliability and speed. On the n=10 formal hold-out, the no-GCD inference path achieves BERTScore F1 = 0.779, exceeding GPT-5.4 (0.726), DeepSeek-V3.2 (0.703), Gemini-3-Flash-Preview (0.703), and Llama-4-Maverick (0.700) zero-shot baselines while maintaining fully local, air-gapped inference. This system addresses a gap in Traditional Chinese special-education NLP and offers a scalable, privacy-preserving local inference solution under an industrial engineering paradigm.

翻译：撰写个别化教育计划是一项高人力成本、知识密集型的文书负担；已有英文研究证明，生成式AI可显著缩短草拟时间，然而，由于领域数据稀缺、隐私法规严格且缺乏本地评估基准，繁体中文下的个别化教育计划自动生成仍几乎处于空白状态。我们提出一种低资源微调流水线，其核心是语料特征扩散方法：（1）通过tau阈值与标记感知分数上限筛选出25份双专家高评分种子记录；（2）从种子中提取特征画像（句子长度、结构、量化模板），并将其注入大型语言模型提示中，同时配合口头化采样式多样性控制以驱动扩散；（3）以15份专家金标准种子作为扩散锚点，目标生成585个样本；最终获得567个有效扩散样本，形成582样本训练集，并利用QLoRA方法微调Breeze-7B模型；（4）在推理阶段，通过语法约束解码强制实施层次化的SMART目标阶梯模式。在55样本的schema压力集上的消融实验揭示了一个意外发现：在繁体中文的token预算下，语法约束解码适得其反——无语法约束解码路径在保持100%模式通过率的同时，中位延迟降低34%，在可靠性和速度上均优于语法约束解码。在n=10的正式保留集上，无语法约束解码路径的BERTScore F1达到0.779，超过了GPT-5.4（0.726）、DeepSeek-V3.2（0.703）、Gemini-3-Flash-Preview（0.703）和Llama-4-Maverick（0.700）的零样本基线，同时保持完全本地化的气隙推理。该系统填补了繁体中文特殊教育自然语言处理领域的空白，并在工业工程范式下提供了可扩展且保护隐私的本地推理解决方案。