Articles AI MAGAZINEA rtificial intelligence affects growth and productivity in many sectors (for example, transportation, communication, commerce, and finance). However, one painful exception is education; specifically, very few AI-based learning systems are consistently used in classrooms or homes. Yet the potential exists for AI to have a large impact on education: As described by the articles on education in this and the previous AI Magazine issue, AI-based instructional software now routinely tailors learning to individual needs, connects learners together, provides access to digital materials, supports decentralized learning, and engages students in meaningful ways. As a society we have great expectations for the educational establishment (for example, train employees, support scientific and artistic development, transmit culture, and so on) and yet, no matter how much is achieved, society continues to expect even more from education. The current environment of fixed classrooms, lectures, and static printed textbooks is clearly not capable of serving a digital society or flexibly adapting for the future. Classrooms and textbooks are especially inappropriate for people who use mobile and digital technology every day. For example, digital natives learn and work at twitch speed, through parallel processing, and connected to others (Beavis 2010). For digital natives, information is instantly available, change is constant, distance and time do not matter, and multimedia is omnipresent. No wonder schools and classrooms are boring! AI Grand Challenges for Education

As part of the ongoing project, Project Halo, our goal is to build a system capable of answering questions posed by novice users to a formal knowledge base. In our current context, the knowledge base covers selected topics in physics, chemistry, and biology, and our question set consists of AP (advanced high-school) level examination questions. The task is challenging because the questions are linguistically complex and are often incomplete (assume unstated knowledge), and because the users do not have prior knowledge of the system's contents. Our solution involves two parts: a controlled language interface, in which users reformulate the original natural language questions in a simplified version of English, and a novel problem solver that can elaborate initially inadequate logical interpretations of a question by selecting relevant pieces of knowledge in the knowledge base. An evaluation of the work in 2006 showed that this approach is feasible and that complex, multisentence questions can be posed and answered, thus illustrating novel ways of dealing with the knowledge capture impedance between users and a formal knowledge base, while also revealing challenges that still remain.

In the winter, 2004 issue of AI Magazine, we reported Vulcan Inc.'s first step toward creating a question-answering system called "Digital Aristotle." The goal of that first step was to assess the state of the art in applied Knowledge Representation and Reasoning (KRR) by asking AI experts to represent 70 pages from the advanced placement (AP) chemistry syllabus and to deliver knowledge-based systems capable of answering questions from that syllabus. This paper reports the next step toward realizing a Digital Aristotle: we present the design and evaluation results for a system called AURA, which enables domain experts in physics, chemistry, and biology to author a knowledge base and that then allows a different set of users to ask novel questions against that knowledge base. These results represent a substantial advance over what we reported in 2004, both in the breadth of covered subjects and in the provision of sophisticated technologies in knowledge representation and reasoning, natural language processing, and question answering to domain experts and novice users.

L earning a scientific discipline such as biology is a daunting challenge. In a typical advanced high school or introductory college biology course, a student is expected to learn about 5000 concepts and several hundred thousand new relationships among them. 1 Science textbooks are difficult to read and yet there are few alternative resources for study. Despite the great need for science graduates, too few students are willing to study science and many drop out without completing their degrees. New approaches are needed to provide students with a more usable and useful resource and to accelerate their learning.The goal of the Inquire Biology textbook is to provide better learning experiences to students, especially those students who hesitate to ask questions. 2 We wish to create an engaging learning experience for students so that more students can succeed -and specifically to engage students in more actively processing the large number of concepts and relationships. Inquire Biology aims to achieve this by interactive features focused on the relationships among concepts, because the process of making sense of scientific concepts is strongly related to the process of understanding relationships among concepts (National Research Council 1999). To encourage students' engagement in active reading, our pedagogical approach is to help a student to articulate questions about relationships among concepts and to support them in finding the answers.Inquire Biology incorporates multiple technologies from the field of artificial intelligence. It includes a formal knowledge representation of the content of the textbook, reasoning methods for answering questions, natural language processing to understand a user's questions, and natural language generation to produce answers. It is based on a systematic knowledge-acquisition process that educators can use to represent the textbook's knowledge in a way that the computer can reason with to answer and suggest questions. A unique

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The long-term goal of Project Halo is to build an application called Digital Aristotle that can answer questions on a wide variety of science topics and provide user-and domain-appropriate explanations. As a near-term goal, we are focusing on enabling subject matter experts (SMEs) to construct declarative knowledge bases (KBs) from 50 pages of a science textbook in the domains of Physics, Chemistry, and Biology in a way that the system can answer questions similar to those in an Advanced Placement (AP) exam in the respective discipline. The textbook knowledge is a mixture of textual information, mathematical equations, tables, diagrams, and domain-specific representations such as chemical reactions. In this paper, we explore the following question: Can we build a knowledge capture system to enable SMEs to construct KBs from the knowledge found in science textbooks and use the resulting KB for deductive question answering? We answer this question in the context of a system called AURA that supports knowledge capture from science textbooks.

This special issue of AI Magazine presents articles on some of the most interesting projects at the intersection of AI and Education. Included are articles on integrated systems such as virtual humans, an intellgent textbook a game-based learning environment as well as technology focused components such as student models and data mining. The issue concludes with an article summarizing the contemporary and emerging challenges at the intersection of AI and education.