## 6 Conclusions  
The aim of this work was to demonstrate the effectiveness of a model that integrates EDM, HITL-ML, and MT to improve learning personalization, identification of individual student needs, and pedagogical practice. Through this model, we seek to solve the current limitations of traditional tools by combining advanced technological capabilities with ethical and pedagogical criteria.  
The results obtained show that the proposed model allows for more effective learning personalization. The integration of EDM improved the accuracy in detecting learning patterns and predicting individual needs by 30% compared to conventional methods. This facilitates the implementation of adapted educational strategies in real time, allowing teachers to design specific interventions that respond to the unique characteristics of each student.  
In addition, it is highlighted that the incorporation of HITL-ML guarantees ethical use and is oriented towards educational objectives. This approach keeps the teacher at the center of the process, allowing AI recommendations to be monitored and adjusted in real time. As a result, automated decisions are aligned with pedagogical and ethical principles, ensuring that the solutions proposed by the system respect the social, cultural, and ethical context of students.  
On the other hand, MT proved to be a valuable resource for educators, as it allows them to design structured pedagogical frameworks and optimize the functioning of AI systems. This facilitates the modulation of learning according to individual needs, contributing to more accurate and effective educational experiences.  
The article also highlights the practical impact of these technologies in improving educational efficiency. The implementation of this model makes it possible to identify at-risk students early, optimize educational resources, and adapt pedagogical strategies according to the individual capabilities of students. At an institutional level, these tools can significantly improve resource management and administrative decision-making, increasing the overall efficiency of the educational system.  
Finally, the need for an ethical approach in the use of AI in education is highlighted. Respect for student privacy, transparency in automated processes, and equity in access to technological benefits are essential to avoid the generation of educational gaps. In this sense, the integration of the proposed model promotes a balance between innovation and ethical principles, contributing to the development of personalized and efficiently managed education.  
This study provides an effective method for personalizing learning and improving pedagogical practice, which combines technological innovation with ethical criteria. This approach not only optimizes educational processes but also ensures that AI acts in a manner aligned with the values and objectives of the educational system.  
Conceptualization, methodology, software, formal analysis, resources, data curation, and writing--original draft preparation, E.L.-M. and P.C.M.-M.; investigation, validation, writing--review and editing, visualization, supervision, project administration, E.L.-M., P.C.M.-M., L.L.-C. and N.P.-P.; funding acquisition, E.L.-M. and L.L.-C. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are openly available in [Elsevier's Scopus database].  
The authors declare no conflicts of interest.