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AI Literacy, Tutoring, and Special Education

A Multi-Theoretical Pedagogical Architecture for Adaptive AI Tutoring in Special Education

Publisher: Real Safety AI Foundation

Research article.

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Abstract

Intelligent tutoring systems built on large language models now demonstrate learning gains comparable to, or exceeding, in-person instruction in general education settings. These systems rarely address the pedagogical, legal, and psychosocial requirements of students with documented learning needs, which in the United States are governed by the Individuals with Disabilities Education Improvement Act of 2004 and parallel frameworks in other jurisdictions. This article presents a theoretical mapping of the Teacher in the Loop platform, a design-stage adaptive tutoring system built specifi- cally for students receiving legally mandated accommodations, to the established lit- eratures that inform each of its published features. Platform features are grouped into five theoretical clusters: cognitive and learning theory, disability studies and accom- modation psychology, affective and behavioral sensing, academic integrity reframed through authorship, and institutional design with human oversight. For each feature the article identifies the seminal tradition on which the feature draws, the recent em- pirical work that validates the feature or a closely related mechanism, and the specific claims that remain theoretical pending deployment. The contribution is synthesis rather than efficacy demonstration: a resource that allows designers, researchers, and policy makers to see whether a platform built for students with disabilities has actu- ally been grounded in the literatures that govern how those students learn, and where the remaining evidentiary gaps lie. Limitations, including the absence of deployment data and the novelty of population-specific adaptive content strategies, are discussed.

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Gilly, Travis. "A Multi-Theoretical Pedagogical Architecture for Adaptive AI Tutoring in Special Education." Real Safety AI Foundation Research Article, n.d.. https://realsafetyai.org/research/g97x32/

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