超越简化：用于阅读障碍友好型教育文本中保真视觉可访问性的DFT-GEN

中文摘要 AI 辅助

密集型教育文本会给阅读障碍人群带来可避免的阅读摩擦，而通用文本简化会删除读者仍需的术语、任务约束或原始证据。对阅读障碍成年人及专家的利益相关者访谈揭示了核心矛盾：减轻负担绝不能损害信息保真度。我们提出DFT-GEN，这是一种经利益相关者意见整合的内容密集型教育材料文本转换框架。其核心贡献并非通用大型语言模型（LLM）优化循环，而是一个阅读障碍专属的可访问性层，该层结合了受保护区间保留机制与确定性阅读障碍可访问性控制器（DAC），用于渲染后的视觉组织。DAC将利益相关者及专家偏好转化为可复现的视觉单元长度、块间距、源/任务分离、高亮预算及可审查风险标记控制项。因此我们将评估分为保真度安全性诊断工具DCFI，以及渲染视觉可访问性诊断工具B-DVAS-VL。在2280个双语考试式条目上，DFT-GEN在保留任务关键信息的同时提升了视觉可访问性：在与同主干对照组的B-DVAS-VL pairwise判断中，其英文项胜率达93%、中文项胜率达64%；在阅读障碍成年人的对照试点中，该框架在保留答题能力的同时降低了阅读努力程度。

英文摘要

Dense educational texts impose avoidable reading friction on people with dyslexia, yet generic simplification can delete terminology, task constraints, or source evidence that readers still need. Stakeholder interviews with dyslexic adults and specialists reveal a core tension: reduced burden must not compromise information fidelity. We present DFT-GEN, a stakeholder-informed text transformation framework for content-heavy educational materials. Its central contribution is not a generic LLM refinement loop, but a dyslexia-specific accessibility layer that combines protected-span preservation with a deterministic Dyslexia Accessibility Controller (DAC) for rendered visual organization. DAC converts stakeholder and expert preferences into reproducible controls for visual-unit length, chunk spacing, source/task separation, highlighting budget, and reviewable risk flags. We therefore separate evaluation into DCFI, a fidelity-safety diagnostic, and B-DVAS-VL, a rendered visual-accessibility diagnostic. On 2,280 bilingual exam-style items, DFT-GEN preserves task-critical information while improving visual accessibility: it wins 93% in English and 64% in Chinese of B-DVAS-VL pairwise judgments against same-backbone controls, and in a controlled pilot with dyslexic adult readers it preserves answerability while reducing effort.

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Preprint. Code available at https://github.com/MorrisYUJQ/DFT-GEN