Enhancing the Willingness of Adopting AI in Education Using Back-propagation Neural Networks

The use and application of artificial intelligence (AI) in Taiwanese higher education bring about new possibilities and challenges. Using AI will effectively change the entire internal structure of the Taiwan Institute of Higher Education; for instance, AI can be utilized in education to investigate how teachers enrich their knowledge, how students learn, and how higher education institutions make accurate and timely decisions. Timely responses are critical for higher education. Hence, AI applications in higher education are important from an educational perspective. In this research, we employ Statistical Package for the Social Sciences (SPSS) and smart partial least squares regression (Smart PLS) software as the primary analytics tools; the former is used to analyze fundamental statistics, whereas the latter is used to investigate the structural model. We also explore how stakeholders can adopt AI applications using back-propagation neural networks and structural equation modeling for analysis. A framework and model were developed for this study and 408 respondents analyzed, and we concluded that the model can explain the increase in the willingness to adopt AI in higher education.