Adoption of Artificial Intelligence in Learning Management Systems and Its Impact on Student Learning Outcomes

Abstract

This study will explore the idea of incorporating Artificial Intelligence (AI) into LMS and how it might affect the results of higher education learning. Intelligent solutions providing personalized learning, automated evaluation, and data-driven decisions are now available on LMS platforms thanks to the growing use of artificial intelligence in NLP, ML, and predictive analytics. This study employs the quantitative method and compiles primary data collected on 200 learners of the Delhi NCR region by means of a structured questionnaire. The connections between AI-enabled LMS tools, perceived utility, and perceived ease of use, customized learning, automated feedback, and student learning outcomes were investigated with the help of statistical methods, i.e., descriptive analysis, correlation, and regression in SPSS and MS Excel. According to the results, AI-powered LMS solutions significantly improve the learning outcomes of students, but with a low explanatory power. Further, the perceived utility and perceived ease of use have a significant relationship with the learning efficacy, and perceived usefulness is a stronger predictor. Furthermore, personalized learning using AI and automated feedbacks have a profound effect on the academic performance of students, which is why they play a crucial role in enhancing academic performance of students. The study concludes that AI applications in LMS systems lead to the development of flexible, student-centered, and efficient learning environments. The results have interesting implications to educational institutions, policymakers, and technology designers in the enhancement of successful adoption and implementation of AI-based learning systems to enhance student outcomes.