The dramatic improvement of information and communication technology (ICT) has made an evolution in learning management systems (LMS). The rapid growth in LMSs has caused users to demand more advanced, automated, and intelligent services. This paper discusses how Artificial Intelligence and Machine Learning techniques are adopted to fulfill users' needs in a social learning management system named "CourseNetworking". The paper explains how machine learning contributed to developing an intelligent agent called "Rumi" as a personal assistant in CourseNetworking platform to add personalization, gamification, and more dynamics to the system. This paper aims to introduce machine learning to traditional learning platforms and guide the developers working in LMS field to benefit from advanced technologies in learning platforms by offering customized services.

Hashtag recommendation aims to suggest hashtags to users to annotate and describe the key information in the text, or categorize their posts. In recent years, several hashtag recommendation methods are proposed and developed to speed up processing of the texts and quickly find out the critical phrases. The methods use different approaches and techniques to obtain critical information from a large amount of data. This paper investigates the efficiency of unsupervised keyword extraction methods for hashtag recommendation. To do so, well-known unsupervised keyword extraction methods are applied to three real-world datasets including a new dataset containing texts of user-generated posts on a social learning platform. Experimental evaluations demonstrate that statistical methods performs newer methods including graph-based and embedding-based approaches in generating hashtags for long text, whereas the embedding-based approaches works better on generating hashtags for short texts. As a consequence, it can be concluded that unsupervised keyword extraction models can be adapted for hashtag recommendation when the social platform is new or there is no existing data to develop dedicated supervised learning models.