The integration of artificial intelligence (AI) into online education has transformed the digital learning space, offering new ways to enhance learner satisfaction and engagement. This systematic literature review, covering a five-year span from 2020 to 2025, explores how AI technologies, such as chatbots, intelligent tutoring systems (ITS), sentiment analysis, gaze tracking and predictive analytics, support learner engagement across cognitive, emotional, behavioral, and social dimensions. Drawing from 30 peer-reviewed studies, the current review addresses three central research questions: (1) What aspects of AI positively influence learner satisfaction and engagement in online courses within higher education institutions; (2) What potential challenges from using these technologies may arise; and (3) What research approaches are most commonly used to assess AI’s impact in such learning contexts? The findings highlight that adaptive learning, real-time feedback, and emotion-aware systems contribute positively to personalized learning and motivation. However, concerns persist around data privacy, algorithmic bias, over-reliance on automation, and system usability. Experimental and quasi-experimental designs, as well as machine learning, mixed methods, and survey-based approaches are found to dominate in reviewed studies. Based on these insights, this work offers a foundation for future AI-enhanced learning management systems designed primarily to enhance learner engagement across cognitive, emotional, behavioral, and social domains.

This systematic literature review investigates the synergistic role of Learning Management Systems (LMS), social media (SM) and artificial intelligence (AI) in enhancing student engagement, satisfaction, and academic success within smart online and blended learning environments. LMS platforms are highlighted as the backbone of digital pedagogy, offering increasingly personalized, interactive, and adaptive experiences. The paper examines 34 peer-reviewed articles sourced from Scopus and Web of Science that address the connection between LMS and SM in higher education. In addition, a separate search query was conducted for research question 3, resulting in 11 studies that focus specifically on the intersection of AI, LMS, and SM. Findings indicate that LMS tools such as discussion forums, automated assessment, learning analytics, and interactive content modules consistently support motivation, timely feedback, and improved academic outcomes. The integration of SM enhances these outcomes by introducing familiar, interactive and collaborative dynamics that foster peer engagement and community - building. Case studies- including platforms like Moodle-VKontakte, KnowMore, and my.eskwela - show how features like newsfeeds, chatbots, and analytics can transform static learning portals into autonomous learning ecosystems. Looking ahead, the review also identifies significant potential for emerging technologies - particularly AI - to elevate the effectiveness of LMS and SM integrations. Future learning environments may incorporate AI-driven personalization, predictive analytics for early intervention, and chatbot assistants that provide academic guidance or direct students to well-being resources in an empathetic, non-diagnostic manner, influencing their engagement and overall satisfaction with learning. Immersive virtual spaces such as metaverse-based classrooms also offer new avenues for simulation-based learning and collaborative engagement. Together, these developments signal a shift toward digital ecosystems that are adaptive, community-oriented, emotionally supportive, and grounded in evidence-based design principles. Overall, this review contributes a consolidated understanding of how LMS and SM can jointly advance the quality of digital education, supported by emerging technologies such as AI. By outlining both current practices and future opportunities, it offers a clear foundation for the development of next-generation learning systems that are more engaging, equitable, and responsive to the diverse needs of today's learners.

Understanding the needs and expectations of students in online courses is critical for improving satisfaction, fostering engagement, and increasing academic success. This paper explores these factors through empirical research. After reviewing existing literature on student needs and expectations in online learning, the paper examines key issues influencing student satisfaction, with a particular focus on Learning Management Systems (LMS). It identifies research gaps and highlights critical factors affecting student engagement. The paper presents the results of an empirical study conducted to collect data directly from computer science students about their needs and expectations of online courses. The research methodology includes a survey of 49 university students who participated in online courses. It includes questions about students’ experiences with online courses, how they perceive the organization and implementation of online courses, which activities are most useful to them, and questions providing insight into students’ expectations for future online courses. The study also explores the role of social and emotional aspects of online learning, such as the importance of building a community and interacting with peers, in shaping the overall learning experience and compares the results with previous related work that has examined the impact of integrating social networks into e-learning on student satisfaction. Findings highlight the need for a student-centered approach to online course design. This research lays the foundation for future work aimed at developing recommendations for managing student engagement and proposes a novel online course model. This model integrates features of both LMS and social media to enhance sustainability, inclusivity, and scalability in online education.