Predicting Preservice Teachers' Intentions to Integrate AI-Based Mobile Applications in Special Education: Examining the Role of Technology Self-Efficacy and Attitudes

Abstract

Grounded in the Theory of Reasoned Action, this study aims to examine how technology self-efficacy and attitudes toward AI-based mobile applications predict preservice special education teachers’ (SETs) intentions to integrate these applications into teaching students with learning disabilities (SWLD). A stepwise multiple regression analysis assessed the impact of these variables on preservice teachers’ intentions. Data were collected from 173 preservice SETs. The results revealed that preservice teachers exhibited moderate levels of technology self-efficacy, intentions to integrate AI-based mobile applications, and attitudes toward these applications. Furthermore, attitudes toward AI-based mobile applications emerged as the strongest predictor of teachers’ intentions to integrate these technologies (r = 0.878, p < 0.05), while technology self-efficacy also had a significant effect (r = 0.698, p < 0.05). Together, these variables accounted for 76% of the variance in intentions (R2 = 0.77). These findings underscore the important role of technology self-efficacy and positive attitudes in affecting teachers’ adoption of AI-based mobile applications. This study addresses the gap in the literature on integrating AI technologies in special education and emphasizes their potential to enhance teaching practices for SWLD. Based on the findings, the study recommends training and support to improve preservice teachers’ self-efficacy and attitudes toward AI-based mobile applications, facilitating their integration into special education settings.