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.

翻译：学生与大型语言模型（LLMs）在学术写作中的互动并非一种稳定特质、采纳决策或能力水平，而是一个持续协商的过程，现有框架无法充分理论化这一过程。类型学模型提供分类却缺乏机制；技术接受模型解释采纳行为却忽视采纳后的质量；人工智能素养框架将能力视为静态预测因子而非动态输入要素。这些框架均未能解释：学生在不同任务间的个体内变异性、经验导致习惯化而非精进的发展悖论，以及基于伦理推理的原则性非使用行为。本文基于在美国一所公立少数族裔服务机构对382名本科生开展的顺序解释性混合方法研究（调查N=382；14项半结构化访谈；三项质性调查线索；1435个编码案例），提出依赖协商框架（RNF）。该框架将LLM依赖重新概念化为四个并发输入（感知收益、感知风险、伦理承诺与情境需求）之间的持续性协商过程，其输出结果会递归性地修正后续决策。双模型架构则适用于13.0%因绝对性伦理承诺而彻底终止协商的参与者。该框架生成了四项可证伪的预测，对少数族裔服务机构的AI素养教学法、学术诚信政策及公平导向实践具有启示意义。