Abstract— There exist several context aware recommender systems (CARS) that have been designed to perform specific tasks in facilitating smart learning. Such context aware recommender systems have potentially played important roles in education, especially in recommending to a learner certain activities. In this research we reviewed existing context aware recommender systems used in facilitating pedagogical smart learning. Our survey paper addressed the following research questions; which contexts are used in smart learning, how the contexts are collected, recommended activities, data mining techniques, and future work in CARS for smart learning. In our findings we addressed the research questions with reference to smart learning. We identified that there are numerous context aware recommender systems; but despite all existing CARS in smart learning, there are several challenges and gaps that are still existing. Such challenges include: - Absence of CARS that perfectly fits the ever changing learner’s needs and preferences, and lack of standard database for smart learning, among other issues highlighted in our future work. Index Terms — Artificial intelligence, Context aware recommender system, Technology enabled Learning I. INTRODUCTION uthor [16], [25], and [40], explain that there is massively huge educational content that has been digitised in digital libraries and other learning platforms globally. On contrary there are insufficient mechanisms put in place to recommend relevant and personalized learning content to learners. Specifically based on the learners’ ever changing preferences, that we consider to be smart learning. Reference [5], [45], and [9], point out that context aware systems collect a variety of information from their environment and adapt their behavior according to the collected data. The collected bits of information is what the system interprets into a meaningful action, based on the current status of the entities that interact with the system. Context information that the system collects could refer and not limited to identity, time, temperature, mood, location, activity, environment, etc. In order to recommend relevant learning content to learners in smart learning environment, there is need to incorporate CARS to pick learners preferences and likes. Author [24], [13], [58], and [40], all clarify that currently learning activities have gone digital in most learning institutions, with most of the learners preferring to use smart learning platforms. Thus calling for the need to automatically recommend to a learner certain activities at any particular time of learning. This would in turn reduce the time taken to manually searching for the relevant learning activity. The recommended activities could range from books for a certain level, courses to undertake, professionals in a certain field etc. These activities are recommended based on the contexts collected from a learner. Such that to recommend professional to assist a learner, we may collect the location details and level of expertise of a learner. Hence pointing out that there are diverse contexts and context variables that can be used in context aware systems. There are several research that have been done in CARS in smart learning, with each research addressing a particular research problem. In this paper we surveyed existing literature in CARS for smart learning that were in line with our research questions. II. BACKGROUND In our background we gave an insight of smart learning, context aware recommender systems, environment and techniques in smart learning. Context aware recommender system components, how smart learning contexts are collected from the environment and considerations when designing smart learning context aware recommender systems. Context Aware Recommender Systems and Techniques in offering Smart Learning: A Survey and Future work Kevin Otieno Gogo, Lawrence Nderu, Stephen Makau Mutua Department of Computer Science School of Computing and Information Tech. School of Computing and Informatics Chuka University JKUAT University MUST University Chuka, Kenya Juja, Kenya Meru, Kenya kevingogo2002@gmail.com lnderu@jkuat.ac.ke, smutua@must.ac.ke A Copyright © 2020 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).