The Influence of Social Support Theory on AI Acceptance: Examining Educational Support and Perceived Usefulness Using SEM Analysis

This study investigates factors influencing the acceptance of artificial intelligence (AI) in education, focusing on attitudes and intentions toward its use. Drawing on the Technology Acceptance Model (TAM) and Social Support Theory (SST), the research examines perceived usefulness, ease of use, and support measures, including educational and emotional support. The study was conducted at King Saud University, where it adopted a quantitative approach, collecting data from 350 participants through a structured survey. A final sample of 308 valid responses was analyzed using Structural Equation Modeling (SEM). The findings revealed significant relationships between key constructs: perceived educational support positively influenced perceived usefulness ( $\beta = 0.160$ , p = 0.000) and attitudes toward AI ( $\beta = 0.140$ , p = 0.010). Perceived usefulness ( $\beta = 0.670$ , p = 0.000) and ease of use ( $\beta = 0.330$ , p = 0.000) were strong predictors of positive attitudes, which, in turn, significantly shaped intentions to use AI ( $\beta = 0.760$ , p = 0.000). Emotional support and cognitive support also had notable but mixed effects on perceived usefulness. However, social interaction support and perceived enjoyment did not demonstrate significant influence. These findings highlight the importance of designing AI systems that emphasise usability and provide robust educational and emotional support to enhance acceptance. The study contributes to theory by extending TAM with constructs from SST, offering new insights into AI adoption in academic contexts. Practically, the results guide policymakers and developers in fostering AI acceptance by addressing educational and emotional needs, ensuring effective implementation in higher education.