Understanding Teachers' Adoption of AI-Based Tools in Special and Inclusive Education: A Systematic Review and UTAUT-Based Integrative Framework

Abstract

Artificial intelligence is increasingly used in special and inclusive education, yet teachers do not adopt these tools in the same way or under the same conditions. This systematic literature review examines the factors shaping teachers' adoption of AI-based educational tools through the Unified Theory of Acceptance and Use of Technology (UTAUT). Following PRISMA 2020, 120 Scopus records were screened, 36 full-text studies were assessed, and 28 studies published between 2024 and 2026 were included in the final synthesis. Findings were mapped against performance expectancy, effort expectancy, social influence, and facilitating conditions. Performance expectancy emerged as the clearest adoption driver, particularly where AI supported personalization, accessibility, planning, and learner support. Facilitating conditions were also prominent, especially training, infrastructure, technical support, and institutional readiness. Evidence for effort expectancy was mixed, while social influence appeared mainly through leadership, professional norms, and organizational expectations. Three factors repeatedly extended the original UTAUT structure: AI literacy and self-efficacy, trust and ethical assurance, and pedagogical compatibility with professional agency. The review proposes a parsimonious UTAUT-based integrative framework for studying AI adoption in special and inclusive education. The findings suggest that teachers' adoption depends not only on expected benefits and available resources, but also on whether they can understand, trust, and meaningfully control AI within their professional practice.