用于评估早期识字阅读理解的问题生成
Question Generation for Assessing Early Literacy Reading Comprehension
AI总结：
本研究提出一种面向K-2英语学习者的阅读理解问题生成方法，可覆盖学习材料、适配学习者能力并生成多类型多难度问题，经FairytaleQA数据集验证，有望成为AI英语教学的重要工具。
摘要：
通过基于内容的互动评估阅读理解能力在阅读习得过程中发挥着重要作用。本文提出了一种面向K-2阶段英语学习者的阅读理解问题生成新方法。该方法可确保完全覆盖基础学习材料，并适配学习者的特定能力水平，还能生成多种不同难度层级的问题类型，以实现全面评估。我们以FairytaleQA（童话问答）数据集为素材，评估了该框架下多种语言模型的性能。最终，该方法有望成为自主AI驱动英语教学系统的重要组成部分。
英文摘要：
Assessment of reading comprehension through content-based interactions plays an important role in the reading acquisition process. In this paper, we propose a novel approach for generating comprehension questions geared to K-2 English learners. Our method ensures complete coverage of the underlying material and adaptation to the learner's specific proficiencies, and can generate a large diversity of question types at various difficulty levels to ensure a thorough evaluation. We evaluate the performance of various language models in this framework using the FairytaleQA dataset as the source material. Eventually, the proposed approach has the potential to become an important part of autonomous AI-driven English instructors.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
