Forecasting the Acceptance of ChatGPT as Educational Platforms: An Integrated SEM-ANN Methodology

Technological progress has reshaped the methods we use to gather, disseminate, and access information. The realm of education has undergone significant transformation, evolving from conventional classroom settings to a dynamic, all-encompassing digital environment featuring online education platforms, engaging educational games, and immersive virtual reality scenarios. A noteworthy breakthrough in artificial intelligence is ChatGPT. This innovative tool enhances the learning journey, customizing it according to individual requirements and delivering personalized feedback and explanations. Our theoretical framework highlights essential aspects for adoption, including system quality, alignment between task technology fit, perceived satisfaction, and personal innovativeness. We collected a total of 834 questionnaires from various academic centers. For in-depth analysis of our research model, we utilized Partial Least Squares-Structural Equation Modeling (PLS-SEM) and a sophisticated Artificial Neural Network (ANN), drawing upon data from our student survey. We also employed IPMA to evaluate crucial performance and significance parameters. Our findings emphasize that the GPT (Generative Pre-trained Transformer) plays a pivotal role in determining user adoption, primarily influenced by elements such as system quality, perceived satisfaction, and personal innovativeness. However, it’s essential to highlight that the alignment between task technology fit didn’t exhibit substantial relevance, suggesting its limited predictive value for ChatGPT adoption. Significantly, the deep ANN model displayed superior accuracy compared to other techniques in forecasting the dependent factor. Overall, this study contributes to the academic conversation surrounding AI and its link to environmental sustainability, offering invaluable insights for industry experts, policymakers, and AI developers. These findings pave the way for crafting AI solutions in tune with user preferences and broader environmental concerns.