While there is a growing body of research on using Artificial Intelligence technology and generative AI in education, the comprehensive examination of its impact on student’s learning outcomes and satisfaction remains largely uncharted. We briefly introduce artificial intelligence and its subfields, including machine learning, deep learning, and generative AI. We then discuss the roles of AI in e-learning inputs, processes, and output through the lens of the System’s view of the e-learning success model. We examined, through the lens of the System’s view of e-learning, the impact of AI on each of the isolated subentities, such as input (student motivation and instructor course facilitation), processes (self-regulated learning behavior, student-student dialogue, and student-instructor dialogue) and output (learning outcomes and satisfaction). We conclude that the systems approach helps us realize that the current literature on the role of AI and generative AI in e-learning systems failed to achieve the goal of e-learning systems as a dynamic set of interdependent subentities interacting to optimize learning outcomes and student satisfaction. We recommend conducting AI and generative AI research to contribute positively to learning outcomes and student satisfaction. To make that happen, we recommend going one step further by integrating AI/generative AI research results with structural equation modeling-based empirical research to optimize e-learning processes and learning outcomes.