Beyond Technocentrism in AI-Supported Special Education: The PAISEN Model of Teacher-Mediated Inclusive Learning

Abstract

Artificial intelligence (AI) offers new possibilities for inclusive teaching and learning for students with special educational needs (SEN), yet AI-in-SEN research remains largely technocentric, often describing system capabilities without explaining how outputs become pedagogically meaningful. This conceptual paper develops the PAISEN Model (Pedagogical Artificial Intelligence for Special Education Needs) through an integrative synthesis of AI in education, inclusive pedagogy, Universal Design for Learning (UDL), and assistive technology. The model proposes a non-deterministic sequence in which AI Teaching Support Systems provide data and adaptive affordances, Teacher Pedagogical Mediation interprets and contextualises them, and an Inclusive Learning Environment translates decisions into accessible participation opportunities. SEN learning outcomes are treated as proposed consequences rather than established effects. Teacher AI competency, institutional support, digital infrastructure, and learner characteristics function as boundary conditions, while an adaptive feedback loop links learner evidence to teacher reflection and instructional adjustment. The novelty of PAISEN lies in making teacher judgement the central explanatory mechanism and separating technology, pedagogical processes, learning conditions and moderators. Theoretically, it bridges AIED and inclusive education; pedagogically, it clarifies differentiated and UDL-aligned AI use without replacing teacher agency; practically, it provides a testable structure for implementation and evaluation. The proposed relationships require empirical validation in authentic SEN settings.