implicit in the user’s choice of training examples); and they address the question of how to learn by applying a single, fixed learning method. Although such systems provide a useful test bed for examining individual learning mechanisms, they are inadequate for use as real-world learners. The problem is that realworld situations offer countless opportunities for learning, and each of these opportunities licenses the learning of infinitely many concepts, few of which are actually useful. Consequently, an indiscriminat e learning system will expend enor■ In AI, psychology, and education, a growing body of research supports the view that learning is a goal-directed process. Psychological experiments show that people with varying goals process information differently, studies in education show that goals have a strong effect on what students learn, and functional arguments in machine learning support the necessity of goalbased focusing of learner effort. At the Fourteenth Annual Conference of the Cognitive Science Society, a symposium brought together researchers in AI, psychology, and education to discuss goaldriven learning. This article presents the fundamental points illuminated at the symposium, placing them in the context of open questions and current research directions in goal-driven learning. Learning is a central area of study for researchers interested in human cog