Many of the intelligent tutoring systems that have been developed during the last 20 years have proven to be quite successful, particularly in the domains of mathematics, science, and technology. They produce significant learning gains beyond classroom environments. They are capable of engaging most students' attention and interest for hours. We have been working on a new generation of intelligent tutoring systems that hold mixedinitiative conversational dialogues with the learner. The tutoring systems present challenging problems and questions to the learner, the learner types in answers in English, and there is a lengthy multiturn dialogue as complete solutions or answers evolve.

Included are articles on integrated systems such as virtual humans, an intelligent textbook, and a game-based learning environment as well as technology-focused components such as student models and data mining. The winter issue will conclude with an article summarizing the contemporary and emerging challenges at the intersection of AI and education. Everyone recognizes the need to improve teacher effectiveness, to improve student engagement, and to create a twenty-first century education system that maximizes potential of every student. The challenges that must be addressed to make these improvements greatly exceed the scope of any single approach, whether it is educational technology, improved teacher training and better after school programs, and so on. In past research, AI -- with its inextricable links to cognitive science, psychology, and mathematics -- has proven a close fit for many of these challenging educational problems.

Included are articles on integrated systems such as virtual humans, an intelligent textbook, and a game-based learning environment as well as technology-focused components such as student models and data mining. This issue concludes with an article summarizing the contemporary and emerging challenges at the intersection of AI and education. Everyone recognizes the need to improve teacher effectiveness, to improve student engagement, and to create a twenty-first century education system that maximizes potential of every student. The challenges that must be addressed to make these improvements greatly exceed the scope of any single approach, whether it is educational technology, improved teacher training and better after school programs, and so on. In past research, AI -- with its inextricable links to cognitive science, psychology, and mathematics -- has proven a close fit for many of these challenging educational problems.

Inquire Biology provides unique capabilities through a knowledge representation that captures conceptual knowledge from the textbook and uses inference procedures to answer students' questions. Students ask questions by typing free-form natural language queries or by selecting passages of text. The system then attempts to answer the question and also generates suggested questions related to the query or selection. The questions supported by the system were chosen to be educationally useful, for example: what is the structure of X? compare X and Y? how does X relate to Y? In user studies, students found this question-answering capability to be extremely useful while reading and while doing problem solving. In an initial controlled experiment, community college students using the Inquire Biology prototype outperformed students using either a hard copy or conventional ebook version of the same biology textbook. While additional research is needed to fully develop Inquire Biology, the ...

Robert S. (Bob) Engelmore, who retired in 1998 from the Knowledge Systems Laboratory at Stanford University, died in an ocean accident in Hawaii on March 25, 2003. As the second editor of AI Magazine, he guided its development from 1981 to 1991; he was also elected a fellow of AAAI in 1992. He had been involved in many aspects of AI and was respected for his uncommon common sense and good humor. He played football for Briarcliff Manor High School, learned to play the piano, and most importantly nurtured a deep interest in science. He won a nationally prestigious Westinghouse science scholarship to Carnegie Institute of Technology (later Carnegie Mellon University) and became a physics major.

Raymond Reiter, a professor of computer science at the University of Toronto, a fellow of the Royal Society of Canada, and winner of the International Joint Conference on Artificial Intelligence 1993 Outstanding Research Scientist Award, died September 16, 2002, after a yearlong struggle with cancer. Reiter, known throughout the world as "Ray," made foundational contributions to artificial intelligence, knowledge representation and databases, and theorem proving. Reiter, known throughout the world as "Ray," made foundational contributions to artificial intelligence, knowledge representation and databases, and theorem proving. Ray was born in Toronto, Canada, in 1939 to immigrant parents who came from Poland. He received a B.S. in mathematics from the University of Toronto in 1961 and an M.S. degree in mathematics in 1963 from the University of Toronto.

If it is true that good problems produce good science, then it will be worthwhile to identify good problems, and even more worthwhile to discover the attributes that make them good problems. This discovery process is necessarily empirical, so we examine several challenge problems, beginning with Turing's famous test, and more than a dozen attributes that challenge problems might have. We are led to a contrast between research strategies--the successful "divide and conquer" strategy and the promising but largely untested "developmental" strategy--and we conclude that good challenge problems encourage the latter strategy. More than fifty years ago, Alan Turing proposed a clever test of the proposition that machines can think (Turing 1950). He wanted the proposition to be an empirical, one and he particularly wanted to avoid haggling over what it means for anything to think.

I claim that achieving real human-level artificial intelligence would necessarily imply that most of the tasks that humans perform for pay could be automated. Rather than work toward this goal of automation by building special-purpose systems, I argue for the development of general-purpose, educable systems that can learn and be taught to perform any of the thousands of jobs that humans can perform. Joining others who have made similar proposals, I advocate beginning with a system that has minimal, although extensive, built-in capabilities. These would have to include the ability to improve through learning along with many other abilities. The long-term scientific goal for many artificial intelligence (AI) researchers continues to be the mechanization of "human-level" intelligence--even though reaching that goal may be many years away.

As a challenge problem for AI systems, I propose the use of hand-constructed multiple-choice tests, with problems that are easy for people but hard for computers. Specifically, I discuss techniques for constructing such problems at the level of a fourth-grade child and at the level of a high school student. For the fourth-grade-level questions, I argue that questions that require the understanding of time, of impossible or pointless scenarios, of causality, of the human body, or of sets of objects, and questions that require combining facts or require simple inductive arguments of indeterminate length can be chosen to be easy for people, and are likely to be hard for AI programs, in the current state of the art. For the high school level, I argue that questions that relate the formal science to the realia of laboratory experiments or of real-world observations are likely to be easy for people and hard for AI programs. I argue that these are more useful benchmarks than existing standardized tests such as the SATs or New York Regents tests.

We describe an application of AI search and information visualization techniques to the problem of school redistricting, in which students are assigned to home schools within a county or school district. This is a multicriteria optimization problem in which competing objectives, such as school capacity, busing costs, and socioeconomic distribution, must be considered. Because of the complexity of the decision-making problem, tools are needed to help end users generate, evaluate, and compare alternative school assignment plans. A key goal of our research is to aid users in finding multiple qualitatively different redistricting plans that represent different tradeoffs in the decision space. We present heuristic search methods that can be used to find a set of qualitatively different plans, and give empirical results of these search methods on population data from the school district of Howard County, Maryland.