A Deterministic AI Foundation for Modeling Human Tutors: Fundamental Assumptions in Structural Learning Theory

Autor/in	Scandura, Joseph M.
Titel	A Deterministic AI Foundation for Modeling Human Tutors: Fundamental Assumptions in Structural Learning Theory
Quelle	In: Technology, Instruction, Cognition and Learning, 11 (2018) 1, S.73-112 (40 Seiten) PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1540-0182
Schlagwörter	Artificial Intelligence; Models; Tutors; Learning Theories; Automation; Intelligent Tutoring Systems; Computer Uses in Education; Knowledge Representation + Suchen Sie Ihr Suchwort? Künstliche Intelligenz; Analogiemodell; Förderlehrer; Lehrender; Tutor; Learning theory; Lerntheorie; Intelligentes Tutorsystem; Computernutzung; Wissensrepräsentation
Abstract	This paper summarizes key stages in development of the Structural Learning Theory (SLT) and explains how and why it is now possible to model human tutors in a highly efficient manner. The paper focuses on evolution of the SLT, a deterministic theory of teaching and learning, on which AuthorIT authoring and TutorIT delivery systems have been built. It explains how SLT differs fundamentally from other theories used to motivate adaptive tutor development and how AuthorIT and TutorIT technologies differ from others used in developing adaptive learning systems. Implicitly, the paper also makes clear why it has been possible using AuthorIT to develop so many TutorIT tutorials in record time at minimal cost. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01