为特殊需求学生定制智能阅读支架,避免信息过载。
Tailoring AI-Driven Reading Scaffolds to the Distinct Needs of Neurodiverse Learners
- 对比四种阅读支持形式:纯文本、分句、加图示、加关键词标签。
- 部分学生受益于分句和图示,另一些则因视觉干扰而更费力。
- 需个性化调节支架,适合有特殊教育需求的学生使用。
神经多样性学习者常需阅读支持,但过度丰富的支架可能反而加重注意力与工作记忆负担。基于建构-整合模型与条件性支架视角,本研究在有监督的融合教育环境中,考察结构化与语义化支架对理解力与阅读体验的影响。采用改进的阅读界面,对比四种模式:原始文本、句子分段、分段+图标、分段+图标+关键词标签。在14名有特殊教育需要的小学生中进行被试内初步实验,通过标准化问题测量理解力,并收集儿童与治疗师的简短体验反馈及开放式意见。结果显示,不同学习者反应差异显著:部分学生在分段和图标支持下表现更好,另一些则因引入视觉元素导致认知协调成本上升。体验评分差异较小,但临床复杂度影响对理解难度的感知。学习者普遍反馈希望用更简单语言并增加视觉辅助。结果表明,无单一最优支架,强调需可调校、个性化的支持设计,为师生-人工智能协同调控提供实践启示。
原文摘要 · Abstract (English)
Neurodiverse learners often require reading supports, yet increasing scaffold richness can sometimes overload attention and working memory rather than improve comprehension. Grounded in the Construction-Integration model and a contingent scaffolding perspective, we examine how structural versus semantic scaffolds shape comprehension and reading experience in a supervised inclusive context. Using an adapted reading interface, we compared four modalities: unmodified text, sentence-segmented text, segmented text with pictograms, and segmented text with pictograms plus keyword labels. In a within-subject pilot with 14 primary-school learners with special educational needs and disabilities, we measured reading comprehension using standardized questions and collected brief child- and therapist-reported experience measures alongside open-ended feedback. Results highlight heterogeneous responses as some learners showed patterns consistent with benefits from segmentation and pictograms, while others showed patterns consistent with increased coordination costs when visual scaffolds were introduced. Experience ratings showed limited differences between modalities, with some apparent effects linked to clinical complexity, particularly for perceived ease of understanding. Open-ended feedback of the learners frequently requested simpler wording and additional visual supports. These findings suggest that no single scaffold is universally optimal, reinforcing the need for calibrated, adjustable scaffolding and provide design implications for human-AI co-regulation in supervised inclusive reading contexts.
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