Preservice teachers’ behavioural intention to use artificial intelligence in lesson planning: A dual-staged PLS-SEM-ANN approach

Abstract

In the ever-changing landscape of education, the integration of technology has become an inevitable force that reshapes the foundations of teaching and learning. Amidst this transformative wave, the concept of Artificial Intelligence (AI) has taken center stage, promising innovative approaches, and increased efficiency. Within this context, the exploration of preservice teachers' behavioural intention to employ AI in lesson planning has emerged as a critical issue for examination. This study used a descriptive cross-sectional survey design and employed a purposive sampling technique to recruit 783 preservice teachers. By employing a cutting-edge dual-staged partial least squares structural equation modelling-artificial neural network (PLS-SEM-ANN) approach, this study investigated the influence of the following essential variables on preservice teachers' intentions to incorporate AI into their lesson planning endeavours: performance expectancy, effort expectancy, habit, hedonic motivation, social influence, and facilitating conditions. Social influence emerged as the most significant positive predictor of preservice teachers' behavioural intention to use AI in lesson planning. Additionally, habit, performance expectancy, effort expectancy, and facilitating conditions substantially positively influenced preservice teachers' behavioural intention to use AI in lesson planning. Conversely, hedonic motivation did not significantly affect preservice teachers’ behavioural intention to use AI in lesson planning. This study not only enhances our understanding of technology integration in pedagogy from a theoretical standpoint but also provides practical recommendations for refining educational curricula and instructional strategies that promote effective AI integration.