There is an inextricable link between attention and learning, yet AIED systems in 2015 are largely blind to learners' attentional states. We argue that next-generation AIED systems should have the ability to monitor and dynamically (re)direct attention in order to optimize allocation of sparse attentional resources. We present some initial ideas towards achieving this goal, starting with a 2 × 2 (direction of attention × content of thoughts) organizational framework that encapsulates a range of attentional states including overt inattention, covert inattention, zone outs, tune outs, and focused, alternating, and divided attention. We then sketch out a three component attentional computing architecture consisting of: (1) devices to monitor where attention appears to be directed; (2) mechanisms for real-time attentional state diagnosis; and (3) interventions to dynamically (re)direct attention. We describe two closed-loop attention-aware AIED systems to serve as concrete renditions of these ideas. We conclude by arguing that AIED can achieve the dual goals of advancing basic research on the science of learning while simultaneously developing highly-effective AIED systems by "attending to attention."