Currently, massive open online courses (MOOCs) are experiencing major developments and arebecoming increasingly popular in distance learning programs. The goal is to break down inequalitiesand disseminate knowledge to everyone by creating a space for exchange and interaction.Despite the improvements to this educational model, MOOCs still have low retention rates, whichcan be attributed to a variety of factors, including learners’ heterogeneity. The paper aims toaddress the issue of low retention rates in MOOCs by introducing an innovative prediction modelthat provides the best (optimal) learning path for at-risk learners. For this purpose, learners at riskof dropping out are identified, and their courses are adapted to meet their needs and skills. A casestudy is presented to validate the effectiveness of our approach using classification algorithms forprediction and the ant colony optimization (ACO) algorithm to optimize learners’ paths.

Academic advising is fundamental to student success, yet the rapid integration of Artificial Intelligence (AI) and Machine Learning (ML) is fundamentally transforming the delivery of academic support. While predictive models and Recommendation Systems (RS) are becoming more accessible, the existing literature remains fragmented across diverse technical architectures and institutional objectives, preventing a clear understanding of the field’s evolution. In view of this, we present a Systematic Literature Review (SLR) of AI-driven academic advising, adhering to the PRISMA 2020 framework. We analyzed 27 peer-reviewed studies published between 2018 and 2025 to synthesize methodological trends and functional applications. Our findings reveal that while most systems prioritize pathway recommendations via classical ML or hybrid architectures, Early-Warning Systems (EWS) remain anchored in predictive classification. Furthermore, a nascent shift toward Generative AI (GenAI) indicates a move toward more interactive advising, though transparency and evaluation standards remain inconsistent. This review identifies a critical tension between algorithmic performance and institutional interpretability. We conclude by proposing a research agenda that emphasizes the need for cross-context validation and the development of socio-technical frameworks that integrate AI into existing higher education management structures.

Predicting student failure has recently become a major topic of interest for academic researchers. Research in the field of e-learning focuses mainly on the performance and accuracy of machine learning models designed for specific courses. Within the existing literature, not much attention has been given to the application of a prediction model, initially developed for a specific course, and across various other courses. By “model portability”, we mean the capacity of a machine learning model to be applied in many different courses and platforms while maintaining consistent or minimal loss in accuracy. Several factors can significantly affect the portability of a model. The purpose of this study is to evaluate the portability of models developed from data extracted from Moodle logs, augmented with an ontology layer. The adopted approach aims to determine how the quantity of data extracted from Moodle activity traces, the type of attributes chosen (numerical or discretized), and particularly the integration of an ontological structure, affect the portability of models to predict student performance. We applied the K-NN classification algorithm to a set of courses at a similar level to build a model. Then, we evaluated the transferability to other courses by assessing accuracy. The results show that the portability of machine learning models and their implementation with different courses is possible in some cases with an accepted decrease in accuracy. Moreover, the findings demonstrate that the use of an ontology allows a notable improvement in terms of portability.