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A Deterministic AI Foundation for Modeling Human Tutors: Fundamental Assumptions in Structural Learning Theory
Joseph M. Scandura

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.

Keywords: Structural Learning Theory, Structural Learning, Structural Analysis, What is learned, Intelligent Tutoring Systems, teaching and learning, Bloom’s 2-Sigma, Universal Control Mechanism, AuthorIT, TutorIT, GIFT, TICL, knowledge representation, ACT-R, BIG DATA, deterministic theory, higher order knowledge, short-term memory

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