Investigating Students’ Behavioral Intentions towards a Smart Learning Platform Based on Machine Learning: A User Acceptance and Experience Perspective

Abstract

This study investigated students’ behavioral intentions toward a smart learning platform. In order to evaluate and implement the adoption model within the context of Thai education, an empirical study was conducted. On a sample of 1,250 pupils from throughout Thailand, an analysis technique known as structural equation modeling was used to evaluate the proposed research model. Results from the study showed that the adoption of smart learning platforms by students was most significantly impacted by attitudes towards using them. This was followed by internal variables, namely the Perceived Ease of Use (PEU) and Perceived Usefulness (PU) of the platforms. In addition, Accessibility (AC), Personalization (PL), and Perceived System Quality (PSQ) are peripheral factors that increase understanding of smart learning platform adoption. The findings of this study align with other research, with the exception that only AC had a detrimental impact on PEU. Therefore, this study will provide valuable insights for scholars and researchers by filling a knowledge gap in the existing literature and demonstrating the concrete use of a proficient smart learning platform in the realm of academic success.