This paper presents a detective scaffolding framework -- a three-phase instructional sequence (Hypothesis Activation -> Evidence Structuring -> Causal Integration) in which engineering students investigate a realistic industrial defect scenario using staged in-class polls as designed evidence probes. Unlike conventional uses of student response systems for engagement, the framework positions each poll as an Evidence-Centred Design instrument targeting a specific reasoning capability. In the primary implementation, 80 Year~3 polymer engineering students progressed from prior-knowledge-driven misconception (71% attributing defects to temperature) to complete root-cause convergence (100\% identifying humidity; Fisher's exact test, $p < .001$) across four sequenced prompts within a single 90-minute lecture slot. A dual-accuracy analysis revealed that at one intermediate stage, textbook-correct and analytically valid responses diverged, illustrating why conventional scoring can misrepresent reasoning quality. In a transferability study, 26 Year~12 students with no engineering background achieved identical root-cause identification rates across two adapted scenarios, with significant gains in data-analysis confidence and AI explanation ability. The results suggest that the pedagogical structure, rather than disciplinary content, drives the convergence effect, implying portability across disciplines and educational levels.

翻译：论文摘要：本文提出一种侦探式脚手架框架——包含三个阶段的教学序列（假设激活→证据结构化→因果整合），其中工科学生通过分阶段课堂投票作为设计好的证据探针，调查真实的工业缺陷场景。与传统将学生响应系统用于课堂互动不同，该框架将每次投票定位为针对特定推理能力的"以证据为中心"的设计工具。主要实施中，80名三年级高分子工程专业学生在单一90分钟教学时段内，通过四个连续提问，从先验知识驱动的错误概念（71%将缺陷归因于温度）过渡到完全根因收敛（100%识别湿度；Fisher精确检验，p<0.001）。双重准确性分析显示，在中间阶段，教科书正确与解析有效的回答出现分歧，揭示了传统评分可能歪曲推理质量的原因。在迁移性研究中，26名无工程背景的12年级学生通过两个改编场景实现相同的根因识别率，并在数据分析信心和AI解释能力方面取得显著提升。结果表明，促成收敛效应的是教学结构而非学科内容，暗示该框架具有跨学科和教育层级的可移植性。