用使用频率预测研究生对AI的感知，比课程经历更有效
Engagement Intensity as a Learner-Modeling Signal for Adaptive AI Ethics Instruction
- 用自报使用频率、熟悉度和过往课程三类特征评估学生对AI的认知
- 使用频率与五项认知指标均显著相关，课程经历则无关联
- 低频使用者在兴趣与信任上呈现断点式差异，适合快速筛查
在93名生物科学研究生及博士后参与的必修科研伦理课程中，比较了三项入学特征——自报的大语言模型使用频率、自我评定的熟悉度以及过往AI教育经历——与五个基础认知结果的关系。使用频率在霍尔姆校正后与全部五项结果相关，熟悉度与其中三项相关，而过往课程经历则无显著关联。在训练兴趣与准确性信任方面，低频使用者表现出明显的阈值效应，而非均匀梯度。简短的入学调查中，自报使用频率比课程经历更能稳定预测这些认知态度，熟悉度可作为次要指标。结果表明，简单的使用行为信号可用于轻量级入学画像，支持自适应AI伦理教学。
原文摘要 · Abstract (English)
Adaptive AI ethics instruction in graduate research training benefits from intake measures that reflect differences in prior LLM experience. Prior coursework or workshop attendance is an obvious candidate, but it is not clear whether it is associated with pre-instruction ratings on key AI perception items. We compare three candidate intake features, self-reported usage frequency, self-rated LLM familiarity, and prior AI education, across five baseline perception outcomes in 93 bioscience graduate and postdoctoral trainees enrolled in a required research ethics course. Usage frequency shows Holm-corrected associations with all five outcomes, self-rated familiarity with three, and prior AI education with none. A threshold-like pattern at the lower end of the scale is most visible for training interest and accuracy trust rather than appearing as a uniform gradient across all five outcomes. In a short intake survey, reported LLM use is more consistently associated with these perceptions than prior coursework or workshops, with self-rated familiarity serving as a secondary indicator. These results suggest that simple pre-instruction behavioral signals can inform lightweight intake profiling for adaptive AI ethics education.
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