研究发现本科生使用大模型有四种不同方式，其中最独立的学生反而得分最低。
Four Types of LLM Reliance and Their Predictors Among Undergraduate Writers: A Mixed-Methods Study at a Minority-Serving R1 University
- 基于多方法分析，识别出四种使用大模型的模式。
- 战略型使用者最独立，但标准评分却最低。
- 现有评估工具忽略学生自主性，需改进衡量方式。
尽管大多数本科生如今使用大型语言模型（LLMs）进行学术写作，但尚无有效方法区分其依赖方式的质性差异。现有量表仅以使用频率衡量依赖程度，本研究发现这反而奖励了对AI的过度依赖，忽视了学生的独立思考贡献。研究在一所公立少数族裔服务型研究型大学开展，基于人工智能素养框架、期望-价值理论及Biggs的预置-过程-成果模型，采集382名本科生数据，包含14次访谈和396份开放式问卷。识别出四类依赖类型：策略型（34.3%）、工具型（30.9%）、对话型（30.4%）和依赖型（4.5%）。学生对价值与成本的认知影响依赖强度，而人工智能素养则决定依赖类型。值得注意的是，策略型用户最为自主，但在标准测评中得分最低，反映出当前评估体系只计AI贡献而不重写作质量，从而惩罚了最具独立性的学生。另有约13%的学生因伦理原因拒绝使用AI，现有框架未涵盖此群体。研究对人工智能素养教育、学习成果评估及少数族裔高校的公平政策具有重要意义。
原文摘要 · Abstract (English)
Although most undergraduates now use large language models (LLMs), a form of generative artificial intelligence (GenAI) for academic writing, no validated method distinguishes the qualitatively different ways students rely on them. Existing instruments assess reliance solely by frequency of use, a measure that, as this study shows, inadvertently rewards dependence on AI rather than recognizing students' own intellectual contribution. Conducted at a public minority-serving university and grounded in the AI Literacy Framework, Expectancy-Value Theory, and Biggs's Presage-Process-Product model, the study drew on 382 undergraduates, 14 interviews, and 396 open-ended survey responses. Four distinct reliance types were identified and confirmed: Strategic (34.3%), Instrumental (30.9%), Dialogic (30.4%), and Dependent (4.5%). Students' value and cost beliefs predicted the intensity of their reliance on LLMs, whereas their AI literacy predicted the type of reliance they adopted, indicating that differentiated support is needed. Notably, Strategic users, those who engaged AI most deliberately, scored lowest on standard outcome measures. This pattern reflects a limitation of current instruments, which index AI's contribution rather than writing quality, thereby penalizing students who show the greatest independent thinking. Analysis also revealed an additional group, roughly 13%, who declined to use AI for ethical rather than practical reasons, and who existing frameworks overlook. These findings carry implications for AI literacy programs, the measurement of student learning outcomes, and equitable AI policy at minority-serving institutions.
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