用问卷识别学术界用AI与政策之间的脱节现象
Academics and Generative AI: Empirical and Epistemic Indicators of Policy-Practice Voids
- 设计十项间接问卷，结合解释框架挖掘师生使用AI的实证与认知信号
- 发现仅34%教师愿完全允许AI参与考试，显示评估能力存在空白
- 揭示学者对AI本质认知分歧，助政策制定对接真实使用场景
随着生成式AI在学术界的扩散，政策与实践间的差距日益显著，亟需可审计的对齐指标。本研究构建了一个包含十项题目的间接测量工具，并嵌入结构化解释框架，以揭示制度规范与实际使用之间的空隙。该框架从学术工作者中提取实证与认知信号，生成三项经筛选的脱节指标：(1) AI整合评估能力（代理指标）——在包含AI技能、教学效益感知和检测信心三个信号的筛选条件下，表示可完全允许AI用于考试的占比为34%；(2) 行业必要性（代理指标）——在高输出控制用户中，仍认为AI具有重大贡献者中，有56%认为其能挑战现有学科体系；(3) 本体立场——在认定AI与以往工具本质不同、已改变实践行为并通过元认知检验的受访者中，持物质性与非物质性观点的比例分别为41%与59%，构成与采购主张相匹配的认知图谱。
原文摘要 · Abstract (English)
As generative AI diffuses through academia, policy-practice divergence becomes consequential, creating demand for auditable indicators of alignment. This study prototypes a ten-item, indirect-elicitation instrument embedded in a structured interpretive framework to surface voids between institutional rules and practitioner AI use. The framework extracts empirical and epistemic signals from academics, yielding three filtered indicators of such voids: (1) AI-integrated assessment capacity (proxy) - within a three-signal screen (AI skill, perceived teaching benefit, detection confidence), the share who would fully allow AI in exams; (2) sector-level necessity (proxy) - among high output control users who still credit AI with high contribution, the proportion who judge AI capable of challenging established disciplines; and (3) ontological stance - among respondents who judge AI different in kind from prior tools, report practice change, and pass a metacognition gate, the split between material and immaterial views as an ontological map aligning procurement claims with evidence classes.
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