Affiliations: Learning Research and Development Center &
Intelligent Systems Program, University of Pittsburgh, Pittsburgh, PA, USA.
E-mail: [email protected] | School of Law, Learning Research and Development
Center, & Intelligent Systems Program, University of Pittsburgh,
Pittsburgh, PA, USA. E-mail: [email protected] | Institut für Informatik, Technische
Universität Clausthal, Clausthal-Zellerfeld, Germany.
E-mail: [email protected] | Human-Computer Interaction Institute, School of
Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA.
E-mail: [email protected]
Abstract: In this paper we consider prior definitions of the terms
"ill-defined domain" and "ill-defined
problem". We then present alternate definitions that better
support research at the intersection of Artificial Intelligence and Education.
In our view both problems and domains are ill-defined when essential concepts,
relations, or criteria are un- or underspecified, open-textured, or intractable
requiring a solver to recharacterize them. This definition focuses on the core
structural and pedagogical features that make problems and domains ill-defined
while providing a consistent and functional frame of reference for this special
issue and for future work in this area. The concept of ill-definedness is an
open-textured concept where no single static definition exists. We present the
most suitable definition for the present goals of facilitating research in AI
and Education, and addressing the pedagogical need to focus learners on
addressing this ambiguity.
