Predicting the Adoption of Artificial Intelligence in Higher Education Institutions Through Multiple Regression Analysis

This study aimed to predict the adoption of artificial intelligence in higher education through various factors by means of multiple regression analysis. The participants consisted of 401 male and female students from four universities in India. Data was collected through an online survey questionnaire. Stated hypotheses and models were tested and verified using a correlation matrix, Chi-Square goodness of fit, and multiple regression examination. The findings of the study showed that the factors of the first model predict a 65.4 % variance in the adoption of AI in higher education (AAIHE), the factors of the second model predict a 62.1 % variance in perceived usefulness, and the factors of the third model predict 38.5 % of variance in perceived ease of use (PEU), while the factors of the fourth model predict 55.8 % of variance in the adoption of AI in higher education (AAIHE). The findings of the study confirmed the stated hypotheses and supported the Theory of Reasoned Action (TRA), the Theory of Planned Behavior (TPB), and the Technology Acceptance Model (TAM). The study highlighted a need for higher education institutions to guarantee that AI-required infrastructures and tools are easily reached to students and have sufficient knowledge and skills to use them effectively.