This article addresses the challenges and opportunities that higher education institutions in Mexico face when seeking international accreditation of their programs. educational programs in Information Technology (IT). Using an inclusive approach and considering the integration of artificial intelligence (AI) tools, the study is based on a qualitative synthesis and quantitative analysis of the responses obtained from a form addressed to various educational institutions. The results highlight the importance of social inclusion in the internationalization of educational programs, the challenges anticipated by institutions, and the best practices to ensure compliance with the quality criteria required by international accreditation bodies. Additionally, the ethical and practical implications of using AI in the educational process are explored. The findings underscore the need for inclusive policies and the potential of AI to transform higher education.

Multiple sclerosis (MS) is an inflammatory disease of the central nervous system (CNS) that impacts nearly 3 million people worldwide. While the etiology and pathogenesis of MS are not yet fully understood, current evidence suggests that it results from complex interactions between genetic and environmental conditions. Clarifying the autoimmune mechanisms underlying MS remains a central objective in the development of effective therapeutic strategies. Interferon-beta (IFN-$\beta$) is one of the most frequently prescribed disease-modifying treatments for individuals with MS. However, despite its established efficacy, recent studies report that approximately 30–50% of patients exhibit inadequate response to IFN-$\beta$, largely due to genetic variability. Machine learning (ML), a branch of artificial intelligence (AI), employs data-driven computational models to enhance predictive accuracy and classification. In recent MS research, unsupervised learning techniques such as hierarchical clustering and K-means have been applied for classification purposes. However, these methods often fail to yield optimal solutions because they require numerous arbitrary decisions and perform adequately only when datasets contain clusters of similar sizes and lack significant outliers. Fuzzy systems (FSs) are designed to model complex, ambiguous real-world phenomena. In this study, an AI algorithm incorporating a fuzzy system, informed by expert neurologist input, is proposed to enhance the assignment of unknown class labels related to IFN-$\beta$ response in MS patients. Additionally, a genetic algorithm (GA) is introduced to identify optimal solutions within the search space, facilitating hyperparameter optimization of a deep learning (DL) model trained with genetic biomarkers to identify patients likely to benefit from this therapy. Experimental results demonstrate that the fuzzy system achieved $80\%$ classification efficiency, in contrast to $64\%$ with conventional hierarchical clustering. Furthermore, an artificial neural network (ANN) model, with hyperparameters optimized by the GA, achieved an accuracy of 0.8–1.0, surpassing the multi-layer perceptron (MLP), which achieved 0.6–0.8 accuracy using conventional tuning methods.

Multiple sclerosis (MS) is a chronic inflammatory disease of the central nervous system (CNS) affecting nearly 3 million people worldwide. Although its etiology and pathogenesis are not clearly established, it is likely the result of complex interactions between genetic and environmental factors. The MS autoimmune etiology has been the goal of therapeutic approaches for patients. IFN-β is one of the most prescribed disease-modifying therapies in MS patients. However, IFN-β is partially effective, and a significant proportion of patients partially respond or do not respond to this treatment. Machine learning (ML) is a subset of artificial intelligence (AI) focusing on developing data-driven computational models to enhance specific tasks. In recent years, unsupervised learning methods, such as hierarchical clustering and K-Means, have been applied in the MS study for classification tasks. Although these methods are relatively easy to implement, they rarely provide the best solution due to the large number of arbitrary decisions, and they perform only when the dataset contains clusters of similar sizes and no notable outliers. Therefore, in this paper, we propose an AI algorithm including a fuzzy expert system, based on the opinion of a neurology expert, to improve the efficiency in the assignment of unknown class labels associated to the response to IFN-β in MS patients, and a genetic algorithm (GA) to optimize the hyperparameter tuning of a deep learning (DL) model trained with genetic biomarkers to estimate whether the patients are potential candidates to be treated with this therapy. The experimental results show the fuzzy system achieved a 80% of classification efficiency against 64% of a conventional hierarchical clustering method. Furthermore, the artificial neural network (ANN) model, whose hyperparameters were optimized by the GA, achieved an 1.0 accuracy against 0.8 of a multi-layer perceptron (MLP), whose hyperparameters were adjusted by a conventional tuning method.