The dominant paradigm in using generative UI (GenUI) for adaptive EdTech considers the use of AI as a runtime engine: content is authored once in a fixed form, and AI adapts delivery dynamically based on learner needs, behaviors, or profiles. We argue that this paradigm has an issue: it moves the burden of accessibility and representation diversity onto systems that see learners only after content has already been locked into particular details. For learners who might need audio-first, simplified text, interactive, or low-bandwidth representations, runtime adaptation is too late and too costly to be equitable at scale, and might lead to inaccurate learning content due to the inability to conduct verification at scale. We propose an alternative method: accessibility belongs in the authoring layer. Specifically, we advocate for a card-based GenUI paradigm, in which educational content is encoded as modality-agnostic semantic units, and GenAI produces multiple interface representations, such as interactive, audio, text-simplified, or low-bandwidth, at learning design time to be verified by the instructor before it reaches any learner. This shifts the AI intervention from delivery to creation, embeds Universal Design for Learning principles into the authoring workflow, and removed per-learner inference costs. We situate this idea against recent work on GenUI, multimodal content generation, adaptive authoring, and equitable delivery, and argue that realizing this goal requires closer integration of AI, HCI, and learning sciences than what either of those communities has so far provided.

翻译：当前，在教育科技自适应系统中运用生成式用户界面（GenUI）的主流范式是将人工智能作为运行时引擎：内容以固定形式一次性生成，再由AI根据学习者需求、行为或画像动态调整呈现方式。我们认为这一范式存在根本性问题——它将可访问性和表征多样性的负担转嫁到系统身上，而这些系统只能在内容被锁定为特定细节后才接触学习者。对于需要音频优先、简化文本、交互式或低带宽表征的学习者而言，运行时适配既迟且代价高昂，难以实现规模化公平，更因无法进行大规模验证而可能导致学习内容失真。为此，我们提出替代方案：可访问性应归于创作层。具体而言，我们倡导基于卡片的GenUI范式，将教育内容编码为与模态无关的语义单元，由生成式AI在学习设计阶段产出多种界面表征（如交互式、音频、文本简化版或低带宽版本），并在触达学习者前由教师进行验证。这一转变将AI的介入点从"分发"移至"创作"，将通用学习设计原则嵌入创作流程，并消除了针对每个学习者的推理成本。我们将此构想置于近期GenUI、多模态内容生成、自适应创作与公平分发相关研究背景下展开论述，并指出实现该目标需要AI、人机交互与学习科学领域更紧密的整合——其程度远超现有各领域独立研究的水平。