提出首个可验证的生成式AI使用类型量表，区分学生依赖方式并支持教育干预。
Measuring How Students Rely on Generative AI in Academic Writing: Development and Multi-Source Validation of the Generative AI Reliance Types Scale (GenAI-RTS)
- 基于理论构建四类依赖类型，通过多源验证确保信度效度。
- 量表在382名本科生中验证，因子结构匹配良好（CFI=.92，RMSEA=.08）。
- 适用于研究学生写作行为差异，助力AI素养教育与学术诚信建设。
随着生成式AI日益融入本科写作，学生如何使用而非是否使用这些工具，已成为学习、学术诚信与教育公平的核心问题。现有测量方法多为归纳式设计，聚焦单一任务且样本同质。本研究开发并验证了生成式AI依赖类型量表（GenAI-RTS），包含20个题项，测量四类理论推导出的依赖类型：策略性、工具性、依赖性和对话性。验证采用教育与心理测试标准的多源框架，结合来自一所美国少数族裔支持机构的382名本科生调查，以及14名有目的抽样的学生访谈。六种竞争模型的验证性因子分析支持五因子结构，其中策略性依赖包含‘有意使用’与‘批判评估’两个子维度，其余为工具性、依赖性和对话性因子（CFI = .92，RMSEA = .08；DWLS CFI = .98，RMSEA = .07）。分量表可靠性良好（omega = .75–.88），并在性别、第一代大学生身份及理工/非理工专业间呈现标量测量不变性，据我们所知，这是首个此类证据。拉什分析表明五级评分更优。策略性依赖与AI素养正相关，且各依赖类型能有效区分学生在写作过程与结果变量上的表现。GenAI-RTS为研究者和教育工作者提供了一个理论基础扎实、心理测量学验证可靠的工具，可用于识别本科生依赖模式，支持研究、评估与干预。
原文摘要 · Abstract (English)
As generative AI (GenAI) becomes increasingly embedded in undergraduate academic writing, how students rely on these tools, rather than simply whether they use them, has become a central question for learning, academic integrity, and educational equity. Existing measures of reliance were developed inductively, focused on discrete problem-solving tasks, and validated mainly with homogeneous samples. This study developed and validated the GenAI Reliance Types Scale (GenAI-RTS), a 20-item instrument measuring four theoretically derived types of GenAI reliance: Strategic, Instrumental, Dependent, and Dialogic. Validation followed the multisource framework of the Standards for Educational and Psychological Testing, drawing on a survey of 382 undergraduates at a U.S. Minority-Serving Institution and interviews with 14 purposively sampled students. Confirmatory factor analyses of six competing models supported a five-factor structure in which Strategic Reliance comprises two facets, Deliberate Use and Critical Evaluation, alongside Instrumental, Dependent, and Dialogic factors (CFI = .92, RMSEA = .08; DWLS CFI = .98, RMSEA = .07). Subscale reliability was acceptable to good (omega = .75-.88), and scalar measurement invariance held across gender, first-generation status, and STEM/non-STEM majors, to our knowledge the first such evidence for a GenAI reliance instrument. Rasch analysis indicated that a five-point response format would improve category functioning. Strategic reliance was positively associated with AI literacy, and the reliance types differentiated students across multiple writing process and outcome variables. The GenAI-RTS offers researchers and educators a theoretically grounded, psychometrically validated instrument for identifying undergraduate reliance profiles and supporting research, assessment, and AI literacy intervention.
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