## 1 Introduction
In special education, where individualized instruction is essential, intelligent learning technologies provide opportunities to customize the learning experience to meet unique needs. [PERSON] et al. [12] expand this idea, investigating the interaction between teachers' beliefs, the quality of the class process, and student participation in intelligent learning environments. Their findings suggest that teachers' perceptions significantly affect the learning climate, thus influencing the levels of student participation. This relationship is particularly pronounced in special education contexts, where teachers must take advantage of technology effectively to create an inclusive and supportive atmosphere. [PERSON] and [PERSON] [13] carry out an exhaustive review of special education studies backed by technology, revealing that several technological interventions have been beneficial in facilitating learning for students with special needs. Their analysis indicates that technology can close gaps in communication, provide visual support, and allow interactive learning experiences, thus improving educational results. Such findings emphasize the transformative potential of intelligent learning technologies when they are carefully implemented in special education environments. [PERSON] and [PERSON] [14] argue that the customization capabilities of these platforms lead to greater commitment and better academic results. This evidence supports the notion that adaptive learning technologies can serve as powerful tools to improve educational experiences for students with various learning requirements, particularly in specialized contexts. The self-regulated learning concept in smart learning environments is critically examined by [PERSON] and [PERSON] [15], who argue that these environments promote autonomy and self-directed learning. For students in special education, promoting self-regulation can be a challenge, but it is essential to develop independent learning skills. Smart learning technologies provide scaffolding that guides students to monitor their progress and adjust their strategies, ultimately improving their learning results. Mathematics, beyond being a basic academic discipline, offers a critical area for students to develop higher-order cognitive skills, problem-solving strategies, reasoning abilities, and analytical thinking capacities [1; 16]. However, not all students develop their mathematical skills equally and at the same pace. This issue becomes particularly evident in the context of mathematics learning disability (MLD), a condition characterized by significant and persistent difficulties in number perception, arithmetic operations, numerical reasoning, and problem-solving [17]. Mathematics learning disability (dyscalculia), although not as widely recognized as dyslexia, is increasingly gaining attention in educational and cognitive science research. Mathematics learning disability (MLD) can cause difficulties not only in students' numerical operations but also in daily life skills, time management, spatial relationships, and higher-level problem-solving processes [18]. In addition to having a negative impact on students' overall academic success, this situation can also lead to negative attitudes towards the mathematics and STEM (science, technology, engineering, and mathematics) fields in the long term and limit their career choices [17]. Although it is difficult to obtain clear and precise data on the prevalence of mathematics learning disability in society, the rates of this disorder vary from country to country. The main reasons for the variability in these rates are the differences in the sample selection used to define mathematics learning disability and the measurement tools used in the diagnosis process [19]. According to research, approximately 5% of the general student population and 47% of children evaluated among individuals with special education needs are diagnosed with mathematics learning disability.