## 1 Introduction  
Artificial intelligence (AI) has emerged as a disruptive force across multiple domains, and education is no exception. The integration of advanced educational data mining (EDM) and machine-learning (ML) techniques has begun to redefine traditional pedagogical paradigms [1, 2]. In the current context, characterized by an exponential increase in the availability of educational data, it is imperative to capitalize on these data to optimize teaching and learning processes. This study is situated at this critical intersection, analyzing the implementation of AI in EDM and the human-in-the-loop (HITL) approach within the ML and automated teaching (AT) frameworks [3, 4]. The transformation of the educational context in the digital age has generated a vast amount of real-time data that reflect student performance, interactions, and preferences. The ability to analyze and extract meaningful insights from these data enables the design of more personalized and effective learning environments. However, the adoption of AI systems in education poses technical, ethical, and pedagogical challenges that require a multidimensional approach to maximize their potential [5]. This is particularly relevant in a context where equity and inclusion are imperative in the global educational agenda. An essential aspect of applying AI in education is the integration of the HITL approach, which fosters collaboration between educators and automated systems. This approach allows educators to play an active role in the design and adaptation of AI algorithms, ensuring that they are contextually relevant and pedagogically valid. Contemporary literature has shown that the inclusion of human expertise can increase the effectiveness and quality of AI systems, while mitigating inherent biases and potential errors that could negatively impact the educational process [6]. The concept of machine teaching (MT) represents another fundamental component of this research. This paradigm allows educators to use AI as a complementary tool, and also facilitates the creation of learning experiences in which automated systems take a proactive role in instruction [7; 8]. Through algorithms that learn from teachers' pedagogical strategies, MT has the potential to provide more adaptive and contextualized learning. Recent research has underlined the ability of this approach to improve educational outcomes by creating more relevant and personalized learning experiences. The current body of literature on EDM and ML in education highlights diverse applications, ranging from predicting academic performance to identifying patterns of learning behavior. However, controversies also emerge regarding data privacy, ethics in the use of algorithms in educational contexts, and the ability of these systems to emulate the complexity of human learning. Some studies suggest that while AI systems can offer accurate recommendations, their inability to capture the richness of the educational context can result in overly reductionist teaching approaches that fail to meet individual students' needs [9; 10]. The aim of the study is to prove/refute the possibility/effectiveness of using AI to determine the personalized needs of students by introducing into pedagogical practice some elements of machine learning and an element of manual verification of the ethics of AI advice. A systematic review of the existing literature will be conducted, assessing the methodologies used, the pedagogical approaches adopted, and the results obtained. This analysis will identify the main lines of research and map the development of the area, revealing the connections between previous studies and emerging areas of research. By addressing this need, it is intended to provide a comprehensive perspective on the current state of research in this area, highlighting both the opportunities and the associated challenges.