DECISION TREE ON BEHAVIOURAL INTENTION TOWARDS USAGE OF AI TOOLS AMONG THE RESEARCH SCHOLARS

Abstract

Researchers are using AI technologies more and more frequently to improve their data analysis, literature review, and predictive modelling skills. By simplifying difficult activities, these technologies increase research productivity and accuracy while freeing researchers to concentrate more on the creative parts of their job. A decision tree can assist in identifying and visualizing the critical elements and decision paths that most strongly influence researchers' intentions to adopt these technologies, such as perceived utility, ease of use, and outside influences. This will help researchers better understand their behavioural intention towards the usage of AI tools among research scholars. This study evaluates the use of a decision tree using Python analysis to examine research academics' behavioural intentions about the use of AI tools. It was found that AI Tools for research are highly likely to be used by people with high levels and moderate levels of digital literacy in urban areas. However, those in rural locations with high levels of digital literacy have inconsistent adoption rates that are impacted by income brackets. Those individuals who are living in rural areas and have high digital literacy have mixed adoption rates. It was further seen that their incomes influence their likelihood of adopting AI Tools. For instance, it was found that Individuals living in Rural areas with low Income show a mixed likelihood towards adoption of AI Tools. Whereas, Individuals who have high incomes are unlikely to adopt AI Tools for research. It was discovered that age was not a significant indicator of AI Tools adoption.