  Explicitly alerting users is not always an optimal intervention, especially
when they are not motivated to obey. For example, in video-based learning,
learners who are distracted from the video would not follow an alert asking
them to pay attention. Inspired by the concept of Mindless Computing, we
propose a novel intervention approach, Mindless Attractor, that leverages the
nature of human speech communication to help learners refocus their attention
without relying on their motivation. Specifically, it perturbs the voice in the
video to direct their attention without consuming their conscious awareness.
Our experiments not only confirmed the validity of the proposed approach but
also emphasized its advantages in combination with a machine learning-based
sensing module. Namely, it would not frustrate users even though the
intervention is activated by false-positive detection of their attentive state.
Our intervention approach can be a reliable way to induce behavioral change in
human-AI symbiosis.
