Objectives: When students use generative AI in coursework, what are its persistent effects on their intellectual development? We investigate (RQ1-How) how students' trust in and routine use of genAI affect their cognitive engagement habits in STEM coursework, and (RQ2-Who) which students are particularly vulnerable to cognitive disengagement. Method: Drawing on dual-process, cognitive offloading, and automation bias theories, we developed a statistical model explaining how and to what extent students' trust-driven routine genAI use affected their cognitive engagement -- specifically, reflection, the need for understanding, and critical thinking in coursework, and how these effects differed across students' cognitive styles. We empirically evaluated this model using Partial Least Squares Structural Equation Modeling on survey data from 299 STEM students across five North American universities. Results: Students who trusted and routinely used genAI reported significantly lower cognitive engagement. Unexpectedly, students with higher technophilic motivations, risk tolerance, and computer self-efficacy -- traits often celebrated in STEM -- were more prone to these effects. Interestingly, students' prior experience with genAI or academia did not protect them from cognitively disengaging. Implications: Our findings suggest a potential cognitive debt cycle where routine genAI use weakens students' intellectual habits, potentially driving and escalating over-reliance. This poses challenges for curricula and genAI system design, requiring interventions that actively support cognitive engagement.

翻译：目的：当学生在课程作业中使用生成式人工智能时，这对他们的智力发展有何持续影响？我们研究（RQ1-如何）学生对生成式AI的信任及常规使用如何影响他们在STEM课程中的认知投入习惯，以及（RQ2-谁）哪些学生特别容易受到认知脱离的影响。方法：基于双过程理论、认知卸载理论和自动化偏见理论，我们构建了一个统计模型，用以解释以信任为导向的常规生成式AI使用如何以及在多大程度上影响学生的认知投入——具体而言是课程作业中的反思、理解需求和批判性思维，以及这些影响如何因学生的认知风格而异。我们利用来自北美五所大学299名STEM学生的调查数据，通过偏最小二乘结构方程模型对该模型进行了实证评估。结果：信任并常规使用生成式AI的学生报告了显著较低的认知投入。出乎意料的是，在STEM领域常被看重的特质——如更倾向于技术热衷、风险承受能力及计算机自我效能感较高的学生——更易受到这些影响。有趣的是，学生此前生成式AI或学术领域的经验并未能保护他们免于认知脱离。启示：我们的发现暗示了一种潜在的认知债务循环，其中常规使用生成式AI削弱了学生的智力习惯，并可能驱动并加剧过度依赖。这对课程设计及生成式AI系统构成了挑战，需要采取积极支持认知投入的干预措施。