Plain Language and Easy-to-Read formats in text simplification are essential for cognitive accessibility. Yet current automatic simplification and evaluation pipelines remain largely automated, metric-driven, and fail to reflect user comprehension or normative standards. This paper introduces a hybrid framework that explicitly integrates human participation into LLM-based accessible text generation. Human-in-the-Loop (HiTL) contributions guide adjustments during generation, while Human-on-the-Loop (HoTL) supervision ensures systematic post-generation review. Empirical evidence from user studies and annotated resources is operationalized into (i) checklists aligned with standards, (ii) Event-Condition-Action trigger rules for activating expert oversight, and (iii) accessibility Key Performance Indicators (KPIs). The framework shows how human-centered mechanisms can be encoded for evaluation and reused to provide structured feedback that improves model adaptation. By embedding the human role in both generation and supervision, it establishes a traceable, reproducible, and auditable process for creating and evaluating accessible texts. In doing so, it integrates explainability and ethical accountability as core design principles, contributing to more transparent and inclusive NLP systems.

翻译：以通俗语言和易读格式进行的文本简化对认知可及性至关重要。然而，当前的自动简化与评估流程仍以全自动化、指标驱动为主，未能反映用户理解程度或规范性标准。本文提出一种混合框架，将人类参与显式整合至基于大语言模型的可及文本生成中：人在回路（HiTL）机制在生成过程中引导调整，而人对回路（HoTL）监督则确保生成后系统性审核。通过将用户研究与标注资源的实证证据转化为：(i) 符合标准规范的检查清单，(ii) 激活专家监督的事件-条件-动作触发规则，以及(iii) 可及性关键绩效指标。该框架展示了如何将人本机制编码用于评估，并通过结构化反馈改进模型适配。通过将人类角色嵌入生成与监督双环节，该框架建立了可追溯、可复现、可审计的可及文本创建与评估流程。由此，该框架将可解释性与伦理问责制作为核心设计原则，助力构建更透明、更具包容性的自然语言处理系统。