Lowering the Technical Skill Requirements for Building Intelligent Tutors: A Review of Authoring Tools (bibtex)
by Lane, H. Chad, Core, Mark G. and Goldberg, Benjamin S.
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 & Pelletier, 1995; D’Mello & Graesser, 2012; VanLehn 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 & Greer, 1995; Valerie J. Shute & 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.
Reference:
Lowering the Technical Skill Requirements for Building Intelligent Tutors: A Review of Authoring Tools (Lane, H. Chad, Core, Mark G. and Goldberg, Benjamin S.), Chapter in Design Recommendations for Intelligent Tutoring Systems, U.S. Army Research Laboratory, volume 3, 2015.
Bibtex Entry:
@incollection{lane_lowering_2015,
	title = {Lowering the {Technical} {Skill} {Requirements} for {Building} {Intelligent} {Tutors}: {A} {Review} of {Authoring} {Tools}},
	volume = {3},
	shorttitle = {Authoring {Tools} \& {Expert} {Modeling} {Techniques}},
	url = {http://ict.usc.edu/pubs/Lowering%20the%20Technical%20Skill%20Requirements%20for%20Building%20Intelligent%20Tutors-A%20Review%20of%20Authoring%20Tools.pdf},
	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 \& Pelletier, 1995; D’Mello \& Graesser, 2012; VanLehn 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 \& Greer, 1995; Valerie J. Shute \& 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.},
	booktitle = {Design {Recommendations} for {Intelligent} {Tutoring} {Systems}},
	publisher = {U.S. Army Research Laboratory},
	author = {Lane, H. Chad and Core, Mark G. and Goldberg, Benjamin S.},
	month = jun,
	year = {2015},
	keywords = {Learning Sciences, UARC, ARL, DoD},
	pages = {303 -- 318}
}
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