Teachers' technology acceptance of language skills applications use for children with autism spectrum disorders.

Researchers studied what makes teachers want to use language apps with autistic children aged 7-10. They found that teachers are most likely to use apps when they can customize them for individual children and when using the apps becomes a regular habit. The apps also need to work well, be easy to use, and have good support available. Making apps fun didn't seem as important for getting teachers to use them.

This study examined factors influencing 592 Georgian teachers' acceptance of language skills applications for children with autism spectrum disorders aged 7-10. Using a modified technology acceptance model with structural equation modeling and artificial neural networks, researchers found that habit and customization were the strongest predictors of app adoption among special education teachers. Performance expectancy, effort expectancy, and facilitating conditions also influenced adoption, while perceived joyfulness had limited impact. The findings suggest that for successful implementation of digital language interventions in autism education, applications should be customizable and teachers need time to develop usage habits.

Educational technology developers should prioritize customization features and provide adequate training to build usage habits among teachers. Implementation strategies should focus on demonstrating clear performance benefits, ensuring ease of use, and providing strong technical support rather than emphasizing entertainment value for educator adoption.

Study limited to one country (Georgia) which may affect generalizability. Sample characteristics beyond number of teachers not detailed. Technology acceptance model may not capture all relevant factors for autism-specific educational applications. Long-term adoption patterns not assessed.

Digital technology fosters great opportunities to care children with autism spectrum disorders (ASD).This study investigates the key determinants for the acceptance of language skills applications among 592 teachers working with children with ASD, aged 7-10, from the country of Georgia. A modified version of the 'Unified Theory of Acceptance and Use of Technology' (UTAUT) model, enhanced by incorporating customization as an additional construct, was employed. The analysis utilized Structural Equation Modeling (SEM) and Artificial Neural Networks (ANN) to validate the framework. SEM revealed that habit and customization significantly impact app adoption, while perceived-joyfulness had limited effects on behavioural-intention.

Performance-expectancy, effort-expectancy and facilitating-conditions were found to be relevant for app adoption. ANN analysis confirmed these findings. Habit and customization are the most important predictors of both intention and adoption of digital applications among special education teachers to support children with ASD.