This essay reflects the shared experiences of four college faculty members (a biologist, a psychologist, a computer scientist, and a feminist literary scholar) working together with K-12 teachers to explore a new perspective on educational practice. It offers a novel rationale for independent thinking and learning, one that derives from rapidly developing interdisciplinary and transdisciplinary inquiries in the sciences and social sciences into what are known as ''complex'' or ''emergent'' systems. Using emergent systems as a model of teaching and learning makes at least three significant contributions to our thinking bout teaching, in three very different dimensions. It invites us into an awareness that the brains of individual students and teachers operate as emergent systems that are neither possible nor desirable to control fully. It invites us to appreciate as well that the activities and benefits of a classroom are not all individual interactions between teacher and student. Interactions among students and teachers are collectively contributing to a somewhat unpredictable project with an insistently social dimension, which is in turn crucial to the individual achievements of all involved. Finally, emergent pedagogy encourages us to consider more carefully the relations between the individual classroom and the larger educational community of which it is a component, including a challenge to rethink the matter of assessment.

We propose an intrinsic developmental algorithm that is designed to allow a mobile robot to incrementally progress through levels of increasingly sophisticated behavior. We believe that the core ingredients for such a developmental algorithm are abstractions, anticipations, and self-motivations. We describe a multilevel, cascaded discovery and control architecture that includes these core ingredients. As a first step toward implementing the proposed architecture, we explore two novel mechanisms: a governor for automatically regulating the training of a neural network and a pathplanning neural network driven by patterns of ''mental states'' that represent protogoals.

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A s a field, computer science faces a problem. From 2000 to 2004, the percentage of first-year undergraduates planning to major in CS declined by more than 60 percent (see the "Declining Interest in Computer Science" sidebar). 1 To attract more students, the introductory CS curriculum must be motivating and relevant. CS courses that are set in a motivating context (for example, using multimedia, gaming, or robotics) can excite students and get them hooked. Other researchers have worked on introductory programming classes with robots as well as introduction to robotics classes (http://myro. roboteducation.org/robobiblio). We didn't want to create a robotics course but rather an introductory CS course based on robots. Introduced properly, robots make visible and tangible those aspects of CS that are often hidden behind computer screens and in computer memory. To further this goal, we formed the Institute for Personal Robots in Education (IPRE), a joint effort between Georgia Tech and Bryn Mawr College and sponsored by Microsoft Research (www.roboteducation. org). This article discusses the first-year results of a three-year project.

They also make it more hands-on, real, practical, and immediate, inspiring a new generation of scientists' deep interest in the field.