Editor for this issue: Naomi Ogasawara <naomi
linguistlist.org>
The Atlas and Why2000 projects seek a full-time AI researcher/programmer to participate in the design, implementation and evaluation of two advanced natural-language intensive tutoring systems; one that supplements the existing Andes quantitative physics problem solving coach and one that provides qualitative physics problem solving tutoring. The primary duties for this position are leading the design and implementation of the underlying knowledge bases and inferencing capabilities needed for both systems. The Atlas project's goal is to engage students in dialogue to tutor them about applicable physics rules and concepts whenever the student asks for assistance while attempting to solve a quantitative physics problem. The dialogue system will attempt to have the student recall the physics rule he needs to apply for the current problem using a variety of techniques (e.g. leading him to derive the rule or simply reminding him of the rule if it is one the dialogue component recently discussed with him). The primary knowledge that needs to be developed for this project is a physics-based ontology to support the understanding and generation components of the system and support for matching student responses to expected correct and incorrect responses. Student contributions are typically short answers or requests. The Why2000 project's goal is to have students provide natural language answers to qualitative physics problems that are then followed up with a dialogue in which the tutor provides feedback and attempts to correct recognizable misconceptions evident in the student's answer. The primary knowledge that needs to be developed for this project are representations for expected answers, limited causal connections between events, and recognition of how the student's answer matches with expected answers. In this case, the student's initial answer is expected to contain multiple sentences and inferencing may be necessary to determine if the student's answer is coherent and complete. Although the knowledge representation issues are challenging, the problems are limited by the fact that the tutoring systems are meant to supplement typical classroom instruction. We can assume that the students will "talk physics" in non-creative ways. The problem is further circumscribed by the fact that the system provides the physics problems that the student will attempt to solve and does not have to be able to tutor physics problems the student presents to it. The qualifications for this position are at least an M.S. in Computer Science or equivalent with an emphasis in either Knowledge Representation or Natural Language Processing and experience in designing and implementing Knowledge Bases in support of research and development applications. Although the position does not require prior expertise in physics, the applicant should be willing to learn about the physics concepts involved. This position is located at the University of Pittsburgh in the Learning Research and Development Center. It is a full-time research staff position with full benefits. Salary will be commensurate with experience. Send inquiries and CVs or resumes to Pamela Jordan (pjordanMail to author|Respond to list|Read more issues|LINGUIST home page|Top of issuepitt.edu). The Atlas and Why2000 groups are composed of artificial intelligence researchers, computational linguists and psychologists. Atlas is led by Kurt VanLehn at the University of Pittsburgh (http://www.pitt.edu/~vanlehn) while Why2000 is jointly led with Art Graesser at the University of Memphis (http://mnemosyne.csl.psyc.memphis.edu/home/graesser/). Both projects are part of CIRCLE, an NSF-funded research center that studies human and computer tutoring (see http://www.pitt.edu/~circle).