Lowering the Technical Skill Requirements for Building Intelligent Tutors: A Review of Authoring Tools
Abstract
In this chapter, we focus on intelligent tutoring systems (ITSs), an instance of educational technology that is often criticized for not reaching its full potential (Nye, 2013). Researchers have debated why, given such strong empirical evidence in their favor (Anderson, Corbett, Koedinger and Pelletier, 1995; DMello and Graesser, 2012; Van Lehn et al., 2005; Woolf, 2009), intelligent tutors are not in every classroom, on every device, providing educators with fine-grained assessment information about their students. Although many factors contribute to a lack of adoption (Nye, 2014), one widely agreed upon reason behind slow adoption and poor scalability of ITSs is that the engineering demands are simply too great. This is no surprise given that the effectiveness of ITSs is often attributable to the use of rich knowledge representations and cognitively plausible models of domain knowledge (Mark and Greer, 1995; Valerie J. Shute and Psotka, 1996; VanLehn, 2006; Woolf, 2009), which are inherently burdensome to build. To put it another way: the features that tend to make ITSs effective are also the hardest to build. The heavy reliance on cognitive scientists and artificial intelligence (AI) software engineers seems to be a bottleneck.
Document Details
- Document Type
- Technical Report
- Publication Date
- Jan 01, 2015
- Accession Number
- AD1159074
Entities
People
- Benjamin S. Goldberg
- H. Clifford Lane
- Mark G. Core
Organizations
- University of Southern California