Embracing Generative AI in Higher Education: Predictors and Emerging Trends

This study investigates the key drivers behind the adoption and integration of generative AI technologies, such as ChatGPT, in higher education. A comprehensive data survey involved 206 respondents from various academic and professional backgrounds. The survey explored the factors influencing the use and acceptance of GenAI tools within educational contexts. To analyze the data and predict user behavior and usage patterns, a machine learning algorithm random forest regressor, was utilized using Python. Firstly, the composite artificial intelligence adoption index has been developed to investigate how individuals adopt and integrate GenAI into their routine academic usage. Secondly, three thematic dimensions of GenAI usage have been identified as cognitive benefit score, writing benefit score, and information benefit score, which are presumed to be the most important part of AIAI. Furthermore, Pearson correlation coefficient and heat map, along with statistical p-value significance, were generated to understand the association among the AI adoption index and the three thematic dimensions. There exists strong evidence that perceived cognitive benefits are the primary drivers of AI adoption, followed by writing and information gathering benefits. The predictive models generated helped identify trends and provide insights into how generative AI could be leveraged more effectively in educational settings, offering valuable recommendations for both educators and policymakers.