学生用大模型写论文是动态协商过程，非简单使用或不用。
The Reliance Negotiation Framework: A Dynamic Process Model of Student LLM Engagement in Academic Writing
- 从482名本科生中提炼出依赖协商框架，考虑收益、风险、伦理与情境四重输入。
- 13%学生因伦理立场直接拒绝使用，体现非使用也是一种理性选择。
- 适合关注教育公平、学术诚信和AI素养教学的教师与政策制定者。
学生在学术写作中使用大语言模型（LLMs）并非固定特质、一次性采纳或静态能力，而是一个持续协商的过程，现有框架难以理论化。类型化模型仅分类无机制；技术接受模型解释采纳却忽略采纳后的质量；AI素养框架将能力视为静态预测因子，而非动态输入。这些均未涵盖个体在不同任务中的变化、经验导致习惯化而非精进的悖论，以及基于伦理的合理不使用。本文基于对美国一所公立少数族裔服务型高校382名本科生的混合方法研究（问卷N=382；14次半结构访谈；三个定性调查分支；1,435个编码实例），提出依赖协商框架（RNF）。该框架将LLM依赖重构为四个并行输入（感知收益、感知风险、伦理承诺、情境需求）的持续协商，输出会递归影响后续决策。双模型架构容纳了13.0%因类别化伦理承诺而完全排除协商的参与者。框架生成四项可验证预测，对AI素养教学、学术诚信政策及少数族裔服务型机构的公平实践具有启示。
原文摘要 · Abstract (English)
Student engagement with large language models (LLMs) in academic writing is not a stable trait, an adoption decision, or a competency level; it is a continuously negotiated process that existing frameworks cannot adequately theorize. Typological models provide categories without mechanisms; technology acceptance models explain adoption but not post-adoption quality; AI literacy frameworks treat competency as a static predictor rather than a live input. None accounts for within-student variability across tasks, the developmental paradox whereby experience produces habituation rather than sophistication, or principled non-use as a form of ethical reasoning. This article introduces the Reliance Negotiation Framework (RNF), developed from a sequential explanatory mixed-methods study of 382 undergraduates at a public minority-serving institution in the United States (survey, N = 382; 14 semi-structured interviews; three qualitative survey strands; 1,435 coded instances). The RNF reconceptualizes LLM reliance as an ongoing negotiation among four concurrent inputs (perceived benefits, perceived risks, ethical commitments, and situational demands) with outputs that recursively modify subsequent decisions. A Two-Model Architecture accommodates the 13.0% of participants whose categorical ethical commitments foreclose negotiation entirely. The framework generates four falsifiable predictions with implications for AI literacy pedagogy, academic integrity policy, and equity-centered practice at minority-serving institutions.
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