Academic choice is crucial in U.S. undergraduate education, allowing students significant freedom in course selection. However, navigating the complex academic environment is challenging due to limited information, guidance, and an overwhelming number of choices, compounded by time restrictions and the high demand for popular courses. Although career counselors exist, their numbers are insufficient, and course recommendation systems, though personalized, often lack insight into student perceptions and explanations to assess course relevance. In this paper, a deep learning-based concept extraction model is developed to efficiently extract relevant concepts from course descriptions to improve the recommendation process. Using this model, the study examines the effects of skill-based explanations within a serendipitous recommendation framework, tested through the AskOski system at the University of California, Berkeley. The findings indicate that these explanations not only increase user interest, particularly in courses with high unexpectedness, but also bolster decision-making confidence. This underscores the importance of integrating skill-related data and explanations into educational recommendation systems.

We present SmartCourse, an integrated course management and AI-driven advising system for undergraduate students (specifically tailored to the Computer Science (CPS) major). SmartCourse addresses the limitations of traditional advising tools by integrating transcript and plan information for student-specific context. The system combines a command-line interface (CLI) and a Gradio web GUI for instructors and students, manages user accounts, course enrollment, grading, and four-year degree plans, and integrates a locally hosted large language model (via Ollama) for personalized course recommendations. It leverages transcript and major plan to offer contextual advice (e.g., prioritizing requirements or retakes). We evaluated the system on 25 representative advising queries and introduced custom metrics: PlanScore, PersonalScore, Lift, and Recall to assess recommendation quality across different context conditions. Experiments show that using full context yields substantially more relevant recommendations than context-omitted modes, confirming the necessity of transcript and plan information for personalized academic advising. SmartCourse thus demonstrates how transcript-aware AI can enhance academic planning.

How Good Are Large Language Models for Course Recommendation in MOOCs? Shin'ichi Konomi Kyushu University, Japan konomi@artsci.kyushu-u.ac.jp ABSTRACT Large Language Models (LLMs) have made significant strides in natural language processing and are increasingly being integrated into recommendation systems. However, their potential in educational recommendation systems has yet to be fully explored. This paper investigates the use of LLMs as a general-purpose recommendation model, leveraging their vast knowledge derived from large-scale corpora for course recommendation tasks. We explore a variety of approaches, ranging from prompt-based methods to more advanced fine-tuning techniques, and compare their performance against traditional recommendation models. Extensive experiments were conducted on a real-world MOOC dataset, evaluating using LLMs as course recommendation systems across key dimensions such as accuracy, diversity, and novelty. Our results demonstrate that LLMs can achieve good performance comparable to traditional models, highlighting their potential to enhance educational recommendation systems.

Course selection is a critical aspect of a student's academic journey, significantly impacting their educational experience and future career prospects [Bruch and Feinberg, 2017]. On large campuses such as the University of Michigan, a major public university that offers more than 10,000 courses each year, this process can be quite challenging and time consuming, especially for new students. Traditionally, students have relied on academic advisors and peer networks for guidance in course selection. However, this approach can lead to inequities in access to quality information, as different students may have varying levels of access to knowledgeable peers or experienced advisors [Lynch and O'riordan, 1998]. Traditional recommender systems, such as collaborative filtering, have been employed in various domains to provide personalized recommendations. However, these systems face several limitations when applied to course recommendations in higher education: 1. Lack of interactivity: Traditional systems typically provide static recommendations based on historical data, without the ability to engage in a dynamic dialogue with the user.

Massive Open Online Courses (MOOCs) have significantly enhanced educational accessibility by offering a wide variety of courses and breaking down traditional barriers related to geography, finance, and time. However, students often face difficulties navigating the vast selection of courses, especially when exploring new fields of study. Driven by this challenge, researchers have been exploring course recommender systems to offer tailored guidance that aligns with individual learning preferences and career aspirations. These systems face particular challenges in effectively addressing the ``cold start'' problem for new users. Recent advancements in recommender systems suggest integrating large language models (LLMs) into the recommendation process to enhance personalized recommendations and address the ``cold start'' problem. Motivated by these advancements, our study introduces RAMO (Retrieval-Augmented Generation for MOOCs), a system specifically designed to overcome the ``cold start'' challenges of traditional course recommender systems. The RAMO system leverages the capabilities of LLMs, along with Retrieval-Augmented Generation (RAG)-facilitated contextual understanding, to provide course recommendations through a conversational interface, aiming to enhance the e-learning experience.

Current course recommender systems primarily leverage learner-course interactions, course content, learner preferences, and supplementary course details like instructor, institution, ratings, and reviews, to make their recommendation. However, these systems often overlook a critical aspect: the evolving skill demand of the job market. This paper focuses on the perspective of academic researchers, working in collaboration with the industry, aiming to develop a course recommender system that incorporates job market skill demands. In light of the job market's rapid changes and the current state of research in course recommender systems, we outline essential properties for course recommender systems to address these demands effectively, including explainable, sequential, unsupervised, and aligned with the job market and user's goals. Our discussion extends to the challenges and research questions this objective entails, including unsupervised skill extraction from job listings, course descriptions, and resumes, as well as predicting recommendations that align with learner objectives and the job market and designing metrics to evaluate this alignment. Furthermore, we introduce an initial system that addresses some existing limitations of course recommender systems using large Language Models (LLMs) for skill extraction and Reinforcement Learning (RL) for alignment with the job market. We provide empirical results using open-source data to demonstrate its effectiveness.

Recommender systems have been studied for decades with numerous promising models been proposed. Among them, Collaborative Filtering (CF) models are arguably the most successful one due to its high accuracy in recommendation and elimination of privacy-concerned personal meta-data from training. This paper extends the usage of CF-based model to the task of course recommendation. We point out several challenges in applying the existing CF-models to build a course recommendation engine, including the lack of rating and meta-data, the imbalance of course registration distribution, and the demand of course dependency modeling. We then propose several ideas to address these challenges. Eventually, we combine a two-stage CF model regularized by course dependency with a graph-based recommender based on course-transition network, to achieve AUC as high as 0.97 with a real-world dataset.

Over the last few years, the team has built the course recommendation engine from the ground up and evolved it to serve recommendations using hyper-personalized models that learn billions of coefficients for our millions of members (Shivani Rao et al CIKM 2019, Polatkan et al blog post). A key goal of this recommendation engine is to surface the most relevant and personalized course recommendations, which can help learners develop new skills and drive engagement on the platform. In this two-part series, we'll show how Learning AI is recommending relevant courses to our members and helping drive engagement by using state-of-the-art AI technologies. In part 1, we'll share an overview of our recommendation engine design and then present a high-level explanation of the three main components of the engine. Later, in part 2, we'll delve deeper into each of the engine's components, providing insight into how we generate personalized course recommendations for every learner on the platform.

In order to help undergraduate students towards successfully completing their degrees, developing tools that can assist students during the course selection process is a significant task in the education domain. The optimal set of courses for each student should include courses that help him/her graduate in a timely fashion and for which he/she is well-prepared for so as to get a good grade in. To this end, we propose two different grade-aware course recommendation approaches to recommend to each student his/her optimal set of courses. The first approach ranks the courses by using an objective function that differentiates between courses that are expected to increase or decrease a student's GPA. The second approach combines the grades predicted by grade prediction methods with the rankings produced by course recommendation methods to improve the final course rankings. To obtain the course rankings in the first approach, we adapt two widely-used representation learning techniques to learn the optimal temporal ordering between courses. Our experiments on a large dataset obtained from the University of Minnesota that includes students from 23 different majors show that the grade-aware course recommendation methods can do better on recommending more courses in which the students are expected to perform well and recommending fewer courses in which they are expected not to perform well in than grade-unaware course recommendation methods.

Dubai based startup, Black Cube Solutions, a frontier tech company, is poised to disrupt the learning and education sector by focusing on skill development and career progression through machine learning and artificial intelligence. Black Cube Solutions has recently unveiled the world's first complete platform for skill development for university students, graduates and working professionals powered by artificial intelligence and machine learning. It's the only platform that brings together all the stakeholders of the skill development ecosystem including universities, corporates, training providers and executive education providers. Peer Mohaideen Sait, Founder and CEO, Training Calendar said, "Skill development is the most pressing challenge for governments and corporates across the world. That is why we came up with the idea of Training Calendar. Our product is aligned to the UAE's national strategy on innovation and is focused on skill development."