AI technologies that sense student attention and emotions to enable more personalised teaching interventions are increasingly promoted, but raise pressing questions about student learning, well-being, and ethics. In particular, students' perspectives about AI sensing-intervention in learning are often overlooked. We conducted an online mixed-method experiment with Australian university students (N=132), presenting video scenarios varying by whether sensing was used (in-use vs. not-in-use), sensing modality (gaze-based attention detection vs. facial-based emotion detection), and intervention (by digital device vs. teacher). Participants also completed pairwise ranking tasks to prioritise six core ethical concerns. Findings revealed that students valued targeted intervention but responded negatively to AI monitoring, regardless of sensing methods. Students preferred system-generated hints over teacher-initiated assistance, citing learning agency and social embarrassment concerns. Students' ethical considerations prioritised autonomy and privacy, followed by transparency, accuracy, fairness, and learning beneficence. We advocate designing customisable, social-sensitive, non-intrusive systems that preserve student control, agency, and well-being.

翻译：通过感知学生注意力与情绪以实现更个性化教学干预的人工智能技术正日益受到推崇，但同时也引发了关于学生学习、福祉与伦理的紧迫问题。尤为关键的是，学生在学习过程中对人工智能感知-干预的视角常被忽视。我们对澳大利亚高校学生（N=132）开展了一项在线混合方法实验，通过呈现不同情境的视频场景进行对比分析，变量包括感知技术是否启用（使用中 vs. 未使用）、感知模态（基于注视的注意力检测 vs. 基于面部的情感检测）以及干预方式（通过数字设备 vs. 教师实施）。参与者还完成了成对排序任务，以对六项核心伦理关切进行优先级评估。研究发现：学生重视针对性干预，但对人工智能监控持负面态度，且该态度不受感知方法的影响。相较于教师发起的协助，学生更倾向于系统生成的提示，并援引学习自主权与社会性尴尬作为主要考量。学生的伦理考量优先关注自主权与隐私权，其次为透明度、准确性、公平性及学习受益性。我们主张设计可定制化、具备社会敏感性且非侵入性的系统，以维护学生的控制权、自主能动性与身心健康。