Envisioning ChatGPT's Integration as Educational Platforms: A Hybrid SEM-ML Method for Adoption Prediction

Technological advancements have revolutionized the avenues through which we acquire, impart, and access knowledge. The educational landscape has witnessed a paradigm shift, transitioning from traditional classrooms to a vibrant, immersive, and inclusive digital realm encompassing online learning platforms, interactive educational games, and virtual reality simulations. A standout innovation in the realm of artificial intelligence is ChatGPT. This tool not only augments learning experiences but also tailors them to individual needs, offering bespoke feedback and elucidations. Our conceptual framework encompasses key adoption facets such as information quality, task-technology congruence, perceived learning value, and individual innovativeness. We amassed a total of 982 questionnaires from diverse academic institutions. To dissect our research model, we employed Partial least squares-structural equation modeling (PLS-SEM) and machine learning algorithms (ML), leveraging student data sourced from our survey. Furthermore, we harnessed IPMA to gauge pivotal performance and significance metrics. Our assessment underscores that the GPT (Generative Pre-trained Transformer) greatly influences user acceptance, largely driven by facets like information quality, perceived learning worth, and personal innovativeness. Such factors are instrumental in shaping user acceptance towards GPT. Yet, it's pivotal to underscore that the task-technology congruence failed to garner significant support, indicating its negligible predictive influence on ChatGPT adoption. It's noteworthy that the J48 classifier predominantly outperformed its counterparts in predicting the dependent variable. In sum, this research enriches the scholarly discourse on AI's intersection with environmental sustainability, proffering crucial insights for industry professionals, decision-makers, and AI solution architects. Such insights serve as a beacon, guiding the design and roll-out of AI systems to cater to user predilections and the broader environmental canvas.