Woolf, Beverly Park (University of Massachusetts, Amherst) | Lane, H. Chad (University of Southern California) | Chaudhri, Vinay K. (SRI International) | Kolodner, Janet L. (Georgia Institute of Technology)
This article focuses on contributions that AI can make to address long-term educational goals. It describes five challenges that would support: (1) mentors for every learner; (2) learning twenty-first century skills; (3) interaction data to support learning; (4) universal access to global classrooms; and (5) lifelong and life-wide learning. A vision and brief research agenda are described for each challenge along with goals that lead to access to global educational resources and the reuse and sharing of digital educational resources. Instructional systems with AI technology are described that currently support richer experiences for learners and supply researchers with new opportunities to analyze vast data sets of instructional behavior from big databases, containing elements of learning, affect, motivation, and social interaction.
Chaudhri, Vinay K. (SRI International) | Cheng, Britte (SRI International) | Overtholtzer, Adam (SRI International) | Roschelle, Jeremy (SRI International) | Spaulding, Aaron (SRI International) | Clark, Peter (Vulcan Inc.) | Greaves, Mark (Pacific Northwest National Laboratory) | Gunning, Dave (Palo Alto Research Center)
Inquire Biology is a prototype of a new kind of intelligent textbook -- one that answers students' questions, engages their interest, and improves their understanding. Inquire Biology provides unique capabilities via a knowledge representation that captures conceptual knowledge from the textbook and uses inference procedures to answer students' questions. In an initial controlled experiment, community college students using the Inquire Biology prototype outperformed students using either a hardcopy or conventional E-book version of the same biology textbook. While additional research is needed to fully develop Inquire Biology, the initial prototype clearly demonstrates the promise of applying knowledge representation and question-answering technology to electronic textbooks.
Gunning, David (Vulcan, Inc.) | Chaudhri, Vinay K. (SRI International) | Clark, Peter E. (Boeing Research and Technology) | Barker, Ken (University of Texas at Austin) | Chaw, Shaw-Yi (University of Texas at Austin) | Greaves, Mark (Vulcan, Inc.) | Grosof, Benjamin (Vulcan, Inc.) | Leung, Alice (Raytheon BBN Technologies Corporation) | McDonald, David D. (Raytheon BBN Technologies Corporation) | Mishra, Sunil (SRI International) | Pacheco, John (SRI International) | Porter, Bruce (University of Texas at Austin) | Spaulding, Aaron (SRI International) | Tecuci, Dan (University of Texas at Austin) | Tien, Jing (SRI International)
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.
This special issue issue of AI Magazine presents six articles on some of the most interesting question answering systems in development today. Included are articles on Project, the Semantic Research, Watson, True Knowledge, and TextRunner (University of Washington's clever use of statistical NL techniques to answer questions across the open web).