发表机构
University of Maryland Baltimore County; University at Buffalo（马里兰大学巴尔的摩县分校; 布法罗大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究如何衡量学生在学术写作中对生成式人工智能的依赖，开发并验证GenAI-RTS量表，该量表含20个条目测四种依赖类型，经多源验证有良好信效度，能区分学生，为相关研究和教育干预提供工具。
AI 中文摘要
随着生成式人工智能（GenAI）越来越多地融入本科学生的学术写作中，学生如何依赖这些工具，而非仅仅是否使用它们，已成为学习、学术诚信和教育公平的核心问题。现有依赖程度的测量方法是归纳得出的，侧重于离散的解决问题任务，且主要通过同质样本进行验证。本研究开发并验证了生成式人工智能依赖类型量表（GenAI-RTS），这是一个包含20个条目的工具，用于测量从理论上推导的四种GenAI依赖类型：策略性、工具性、依赖性和对话性。验证遵循教育和心理测试标准的多源框架，基于对美国一所少数族裔服务机构的382名本科生的调查以及对14名有目的抽样学生的访谈。对六个竞争模型的验证性因素分析支持了一个五因素结构，其中策略性依赖包括两个方面，即刻意使用和批判性评估，以及工具性、依赖性和对话性因素（CFI =.92，RMSEA =.08；DWLS CFI =.98，RMSEA =.07）。子量表信度良好（omega =.75-.88），并且在性别、第一代身份和STEM/非STEM专业之间具有标量测量不变性，据我们所知，这是GenAI依赖工具的首个此类证据。Rasch分析表明，五点反应格式将改善类别功能。策略性依赖与人工智能素养呈正相关，并且这些依赖类型在多个写作过程和结果变量上区分了学生。GenAI-RTS为研究人员和教育工作者提供了一个基于理论、经过心理测量验证的工具，用于识别本科学生的依赖概况，并支持研究、评估和人工智能素养干预。
英文摘要
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.
Comments21 pages, 3 figures, 14 tables
