PIs: Kevin Ashley, Diane Litman, Christian Schunn
University of Pittsburgh
The PIs are investigating the design of intelligent tutoring systems (ITSs) that are aimed at learning in unstructured domains. Such systems are not able to do as much automatically as ITSs working in traditionally narrow and well-structured domains, but rather they need to share responsibilities for scaffolding learning with a teacher and/or peers. In the work proposed, the three PIs, who share expertise in automated natural language understanding, intelligent tutoring systems, machine learning, argumentation (especially in law), complex problem solving, and engineering education, are integrating intelligent tutoring, data mining, machine learning, and language processing to design a socio-technical system (people and machines working together) that helps undergraduates and law students write better argumentative essays. The work of helping learners derive an argument is shared by the computer and peers, as is the work of helping peer reviewers review the writing of others and the work of learners to turn their argument diagrams into well-written documents. Research questions address the roles computers might take on in promoting writing and the technology that enables that, how to distribute scaffolding between an intelligent machine and human agents, how to promote better writing (especially the relationship between diagramming and writing), and how to promote learning through peer review of the writing of others.
This project is bringing together outstanding researchers from a variety of different disciplines — artificial intelligence, law education, engineering and science education, and cognitive psychology — to address an education issue of national concern — writing, especially writing that makes and substantiates a point — and to explore ways of extending intelligent tutoring systems beyond fact-based domains. It fulfills all aims of the Cyberlearning program — to imagine, design, and learn how to best design and use the next generation of learning technologies, to address learning issues of national importance, and to contribute to understanding of how people learn.