MOERID: enhancing open educational resource discoverability through an artificial intelligence-powered chatbot recommender system

Abstract

Open educational resources (OER) are valuable assets in learning and teaching. They ensure cost-effectiveness and customizability, and contribute to global collaboration in the education realm. Hence, education stakeholders face a critical challenge in locating suitable OERs that meet their needs and pedagogical objectives. To cater to this issue, researchers propose diverse digital solutions, one being recommender systems (RS). While a wide array of the suggested tools focused on learners only, this study introduces MOERID, an AI-powered OER Recommender Chatbot aimed at the teaching community. It aspires to facilitate resource discoverability, allowing instructors to save time and energy to concentrate on other pedagogical duties. MOERID engages NLP and recommendation filtering techniques to locate and deliver relevant OERs to instructors. The study describes the implementation of MOERID, highlights its efficacy and provides actionable recommendations for future research by outlining the research gaps.