Traditional approaches to workplace training often treat learners as equally prepared, drive them through too much content in too short a time, and conclude before ensuring retention. These departures from ideal instructional practice have a common cause—the need to fit learning activities into constrained episodes such as classroom presentations and e-learning courses. Fortunately, advances in mobile technology, learning science, and artificial intelligence are making it possible to deliver learning experiences in less constrained conditions, with reduced risk of overload, and better alignment with an individual’s mental and situational readiness to learn. We developed a mobile strategy that leverages these advances to support adult learning, and implemented this strategy in PERLS, a mobile application that recommends bite-sized learning materials—or microcontent—through a deck of electronic cards. An intelligent algorithm tracks progress and recommends content based on principles of self-regulated learning, goal-setting, and adult learning motivation. Essentially, PERLS aims to engage users in becoming better self-regulated learners on the job. In this paper, we describe the PERLS mobile learning strategy and results of an evaluation of user satisfaction with the technology and pilot testing of several instruments for continuous improvement. The mobile app was deployed to support training of Defense Support for Civil Authorities (DSCA). By drawing from observations, online usage data, learning outcome measures, and surveys of learner characteristics and attitudes, this paper provides evidence of the feasibility of using this approach to enhance self-directed learning activity among military personnel.