Abstract
The Generalized Intelligent Framework for Tutoring (GIFT) is a research prototype with three general goals associated with its functions and components: 1) lower the skills and time required to author Intelligent Tutoring Systems (ITSs) in a variety of task domains; 2) provide effective adaptive instruction tailored to the needs of each individual learner or team of learners; and 3) provide tools and methods to evaluate the effectiveness of ITSs and support research to continuously improve instructional best practices. This special issue focuses primarily on the third goal, GIFT as a research testbed. A discussion thread covers each article within this special issue and discusses its actual and potential impact on GIFT as a research tool for AIED. Our primary motivation was to introduce the AIED community to GIFT not just as a research tool, but as an extension of familiar challenges taken on previously by AIED scientists and practitioners. This preface provides a high level overview of the GIFT functions (authoring, instructional delivery and management, and experimentation) and presents its primary design principles. To learn more about GIFT, freely access the software, documentation, and associated technical papers visit www.GIFTtutoring.org.
Highlights
The primary purpose of this special issue is to introduce artificial intelligence in education (AIED) researchers to the experimentation capabilities of the Generalized Intelligent Framework for Tutoring (GIFT) (Sottilare et al 2012; Sottilare et al 2017a, 2017b), a research prototype sponsored and developed by the US Army Research Laboratory (ARL)
GIFT’s design goals are: 1) to lower the skills and time required to author Intelligent Tutoring Systems (ITSs) in a variety of task domains; 2) to deliver effective and efficient adaptive instruction that is tailored to the needs of each individual learner or team of learners; and 3) to provide tools and methods to evaluate the effectiveness of ITSs and support research to continuously improve instructional best practices
The principles that have shaped GIFT are based on the individual and team instructional literature which includes a heavy foundation in the AIED and computer-supported collaborative learning (CSCL) literature
Summary
The primary purpose of this special issue is to introduce AIED researchers to the experimentation capabilities of the Generalized Intelligent Framework for Tutoring (GIFT) (Sottilare et al 2012; Sottilare et al 2017a, 2017b), a research prototype sponsored and developed by the US Army Research Laboratory (ARL). GIFT’s design goals are: 1) to lower the skills and time required to author Intelligent Tutoring Systems (ITSs) in a variety of task domains; 2) to deliver effective and efficient adaptive instruction that is tailored to the needs of each individual learner or team of learners; and 3) to provide tools and methods to evaluate the effectiveness of ITSs and support research to continuously improve instructional best practices.
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