构建可适应语境的提问质量评估框架，让好问题有标准可依。
"There Is No Such Thing as a Dumb Question," But There Are Good Ones
- 从恰当性与有效性双维度定义好问题，建立可动态调整的评分体系。
- 在CAUS与SQUARE数据集上验证，能准确识别良构与问题提问。
- 适合研究人机交互、教育技术及智能助手的开发者参考。
提问对人类与人工智能日益重要，但高质量提问的系统性评估仍不足。本文提出两个核心评价维度：恰当性（情境中的社会语言能力）与有效性（目标达成的战略能力）。基于此，构建了基于评分量表的评估体系，通过引入动态上下文变量，实现结构化与灵活性的统一。该方法在CAUS和SQUARE数据集上得到验证，能有效识别良好与有问题的提问，并适应不同语境。本研究为提问行为与结构化分析方法的融合提供了灵活且全面的框架，推动了对提问本质的深入理解。
原文摘要 · Abstract (English)
Questioning has become increasingly crucial for both humans and artificial intelligence, yet there remains limited research comprehensively assessing question quality. In response, this study defines good questions and presents a systematic evaluation framework. We propose two key evaluation dimensions: appropriateness (sociolinguistic competence in context) and effectiveness (strategic competence in goal achievement). Based on these foundational dimensions, a rubric-based scoring system was developed. By incorporating dynamic contextual variables, our evaluation framework achieves structure and flexibility through semi-adaptive criteria. The methodology was validated using the CAUS and SQUARE datasets, demonstrating the ability of the framework to access both well-formed and problematic questions while adapting to varied contexts. As we establish a flexible and comprehensive framework for question evaluation, this study takes a significant step toward integrating questioning behavior with structured analytical methods grounded in the intrinsic nature of questioning.
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