PREDICTING AI TOOL ADOPTION AMONG SPECIAL EDUCATION TEACHERS USING THE TECHNOLOGY ACCEPTANCE MODEL: A QUANTITATIVE STUDY OF PUBLIC SCHOOLS AT THE DISTRICT LEVEL

Abstract

Artificial intelligence tools hold considerable promise for transforming the quality and reach of special education, yet their adoption among teachers in public school settings remains limited and poorly understood, particularly in developing country contexts. Grounded in the Technology Acceptance Model (TAM), this quantitative, survey-based study examines the perceived usefulness, perceived ease of use, and behavioral intention to adopt AI tools among special education teachers in public primary and secondary schools at the district level in Punjab, Pakistan. A sample of 100 special education teachers was selected using simple random sampling from multiple public schools within a single district. Data were collected using a structured TAM-based questionnaire comprising 24 items across three constructs: Perceived Usefulness (PU), Perceived Ease of Use (PEOU), and Behavioral Intention to Adopt (BIA). Descriptive statistical analysis revealed that teachers generally perceive AI tools as potentially useful for supporting students with special needs but express moderate uncertainty about their ease of use and report relatively low behavioral intention to adopt, primarily due to inadequate training and limited institutional support. These findings highlight significant gaps in teacher readiness and institutional infrastructure that must be addressed before AI tools can be meaningfully integrated into special education practice in Pakistani public schools.