# Key Barriers to Personalized Learning in Times of Artificial Intelligence: A Literature Review
## 1 Introduction  
In the context of the Fourth Industrial Revolution, the personalization of learning has become crucial, as it represents the increasing integration of human capabilities with technological advancements [1]. This phenomenon is driven by disruptive technologies such as artificial intelligence (AI), the Internet of Things, and robotics. AI, defined as the ability of machines to process data through algorithms, learn from patterns, and apply that knowledge in decision-making similarly to humans [2], has made significant advancements. From the first intelligent tutoring systems to personalized learning environments [3], AI has facilitated the provision of resources and assessments tailored to each student, adapting to their profile, learning style, and cognitive levels [4, 5]. Moreover, biometric and contextual tracking technologies have emerged, enabling the detection of emotions and preferences, thus allowing systems to be adjusted more precisely to students' needs [6]. Consequently, the development of AI represents a transformative opportunity for educational ecologies and traditional learning processes. In this regard, humans have diverse educational goals, learning styles, and individual characteristics that influence their formation. Therefore, personalized learning (PL), understood as student-centered education, is seen as an alternative capable of addressing these differences through the use of technology, thus contributing to improving the quality of education [7]. In this context, PL allows instruction to be tailored to students' preferences, needs, and learning paces [8]through mechanisms such as Learning Analytics, which identifies individual characteristics and adjusts learning materials, spaces, and contexts accordingly [9]. According to the OEI-UNESCO [7], understanding the diverse nature of PL is essential for advancing its implementation. In this regard, it is important to take into account their prior knowledge, needs, and abilities [10]. In the same way, [PERSON] et al. [11], indicate that PL adapts instructions by considering differentiation, individualization, and inclusion. However, despite the widely documented advantages of PL, significant challenges persist, particularly in an educational context increasingly influenced by disruptive technologies such as AI. In this context, this literature review seeks to identify and analyze barriers that have been historically reported in the implementation of PL environments and may still be relevant today. Although PL has been the subject of extensive discussion, the literature identifies several obstacles that have yet to be overcome. These barriers can be classified into five dimensions: (1) Conceptual Barriers, which refer to the diversity in the understanding and implementation of concepts related to PL [12; 13; 14; 15]; (2) Institutional Barriers, which include resistance to change, lack of technological infrastructure, insufficient time, inadequate institutional support, and funding difficulties [8; 16; 17; 18]; (3) Psychological Barriers, related to the perception of additional workload, distrust in technological skills, and lack of motivation towards PL [19; 20]; (4) Technological Barriers, which include limited access to technology, deficiencies in digital skills, insufficient technical support, integration difficulties, and ethical considerations [16; 21; 22; 23; 24]; and (5) Pedagogical Barriers, related to the models used for personalized instruction, the lack of pedagogical knowledge necessary to adapt content and learning activities, insufficient pedagogical support for teachers, and the complexity of measuring and tracking students' individual progress [25; 26; 27; 28]. In this regard, it is worth mentioning that to date no clear categorization of these barriers has been found through a review of empirical studies on PL. Thus, this literature review seeks to fill that gap, identifying areas of opportunity and technological trends.