## 5 Discussion  
The results obtained in this systematic review show a growing interest in the implementation of AI technologies for assessing students' attentional state. The literature reviewed shows that most studies adopted experimental methodological approaches, based on automated observation using AI technologies and data analysis through machine learning algorithms [10; 12]. These findings are in line with previous research highlighting the potential of AI in education for measuring attention and recognising behavioural patterns [11; 14]. One of the main aspects identified is the predominance of studies conducted in Asia, especially in China, suggesting a consolidation of research lines in this geographical context [14; 15; 17]. Although there is interest in Europe and America, scientific production in these regions is comparatively lower, suggesting the need to extend the analysis to different educational and cultural environments to validate the applicability of these technologies in different contexts [13; 19]. From a methodological perspective, most of the studies used face and body recognition techniques to analyse students' attentional state in the classroom [12; 15; 19]. This trend is consistent with previous studies demonstrating the effectiveness of these methods in inferring students' attentional state and engagement in real time [14; 16]. However, authors have warned of the need to consider ethical issues, such as student privacy and informed consent in the use of these technologies [9; 17]. The studies reviewed also reflect a diversification in the tools used. While some studies used video cameras and computer vision algorithms to assess attention [12; 15], others combined these methods with biometric sensors, such as smart bracelets or electroencephalograms, in order to obtain more accurate measurements [13; 18]. This combination of techniques allows for a triangulation of data that can improve the reliability of results but poses additional challenges in terms of implementation in real educational settings [16; 19]. The findings also highlight the need to establish clear policy frameworks for the integration of AI in the assessment of students' attentional state. While some studies highlighted the potential of these technologies to optimise pedagogical strategies and personalise teaching [5; 7], others warned about the risk of over-reliance on automatedsystems, which could affect the teacher-student relationship and autonomy in the learning process [8; 9]. In terms of effectiveness, the studies report accuracy levels in detecting students' attentional state ranging from 70% to 90%, depending on the technology used and the AI model implemented [14; 15; 16]. However, the variability in the results suggests the need to standardise assessment procedures and to develop more robust models capable of adapting to different educational settings [12; 19]. The results of this systematic review have important implications for both educational practice and research. In practical terms, the use of artificial intelligence technologies to measure student attention can promote personalised teaching. These tools allow teachers to adapt their teaching strategies based on real-time indicators of the group's level of engagement [14; 15; 46]. However, it is essential to define ethical protocols and regulatory frameworks that protect privacy and ensure informed consent, especially in contexts involving minors [9; 50]. Several future lines of research are emerging. These include the design of more adaptive and less invasive AI models, the validation of these technologies in diverse educational settings, and the analysis of their impact on students' academic performance and emotional well-being [57; 59]. It is also important to emphasise the urgent need to study the balance between automation and teacher autonomy. It is essential that these tools support the educational task without replacing professional judgement [5; 7].