## 2 Literature Review
### Learning Personalization Models in AI Applied to Education  
Personalization of learning has become one of the main objectives of the application of AI in the educational area. This approach seeks to adapt the teaching-learning process to the characteristics, abilities, and specific needs of each student, promoting an educational model focused on the individual and their learning context. EDM and MT techniques have been especially relevant in achieving these objectives, allowing systems to identify learning patterns, preferences, and areas for improvement in students [9]. One of the most relevant models in the personalization of learning is the predictive analysis of student performance. By using EDM algorithms, the difficulties that a student might face in certain subjects can be anticipated, so that they can be provided with proactive support. AI systems analyze interaction patterns, responses to assessments, and study habits to predict performance and detect risk areas. This approach has proven effective in the early identification of students who could benefit from additional resources or adaptations in learning pace [3]. Another model that has gained ground is that of ITSs, which function as virtual tutors capable of adapting to the skill level and learning style of each student. These systems rely on MT techniques to optimize their responses and pedagogical strategies based on the feedback received [26; 27]. The ITS adjusts activities, difficulty, and content, promoting a more personalized educational experience. In this sense, the technology mimics traditional teaching practices and introduces a level of adaptability that would be difficult to implement in a face-to-face environment due to time and resource constraints [28]. In addition, educational recommendation models have become a fundamental component of AI in the educational area, especially in online learning environments. These systems, which are based on data mining and ML algorithms, use historical data and user preferences to suggest educational content, activities, and resources that fit their interests and needs. Through HITL-ML, recommendation models can receive direct feedback from students and teachers, allowing recommendations to be continuously adjusted and improved. These systems are useful in open education platforms and e-learning environments, where students often have access to a vast amount of resources and learning options [29; 30]. An emerging concept in learning personalization is the creation of personalized learning paths. This approach uses both AI and direct feedback from the student to map out specific learning paths that are tailored to their goals, abilities, and progress. Unlike traditional methods, where all students must follow the same curricular path, personalized learning paths allow each student to progress according to their own pace and specific goals [31]. AI plays a key role in updating and adjusting these paths based on students' changing performance and preferences, allowing for greater autonomy and more effective learning. Learning personalization has also driven the development of adaptive formative assessments, where AI automatically adjusts questions and exercises based on student responses, so that the assessment measures knowledge and acts as a learning tool. This ability to adapt assessments in real time provides a more accurate view of student progress and helps reinforce knowledge in areas where the student might need additional support. AI systems in these models adapt the difficulty and select topics based on the individual needs of the student, transforming the assessment process into a personalized and continuous experience [32]. This personalization of learning through AI has revolutionized the traditional approach to education, allowing teaching to be tailored to each individual and optimized through models that adjust in real time.