"There Is No Such Thing as a Dumb Question," But There Are Good Ones

TL;DR

This paper introduces a comprehensive evaluation framework for assessing question quality, focusing on appropriateness and effectiveness, validated through datasets to improve understanding of questioning behavior.

Contribution

It presents a novel, semi-adaptive rubric-based framework for systematically evaluating question quality across diverse contexts.

Findings

Framework effectively assesses well-formed and problematic questions

Incorporates dynamic contextual variables for flexibility

Validated on CAUS and SQUARE datasets

Abstract

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.