## 2 Literature Review  
Numerous applications of AI exist in all areas of our society, but its use in education requires particular exploration. AI has been shown to have interesting applications in the design and production of educational content [2], student assessment [3], designing innovative educational experiences incorporating AI [4], personalising training [5], and even promoting student engagement [6]. Changes are becoming particularly evident in higher education, making it crucial to examine the possibilities and risks of AI in this context [7], as well as to critically analyse the myths emerging around its use [8,9].  
One of the ongoing lines of research is focused on analysing the use of AI either to monitor or to improve students' attention. In this sense, attention is a basic element in the construction of learning and to ensure the effectiveness of communication in the educational process. As reported by [PERSON] et al. [10], students with a higher level of attention increase their retention capacity and therefore improve their learning, which is why they tend to be more academically successful. Studies have tracked students' gaze to analyse what attracts their attention [11].  
The research most closely linked to our study is that which uses AI as a tool to analyse students' attention, both in face-to-face classroom situations and in e-learning models. Research has analysed how students express their attention levels and emotions through the analysis of facial expressions or body positions while in the classroom [12]. In this paper, the authors demonstrate how AI can help improve teaching strategies, as AI can determine the emotions that students reflect in their facial expressions and thus detect whether they are paying sufficient attention.  
Along the same lines, other authors [13] complement the use of AI with biometric analysis tools (smart watches, sensors, cameras, etc.) to monitor students in online teaching systems, also suggesting that the resulting attention indicators can be used by teachers to redefine their teaching strategies. The research by [PERSON] et al. [14] was also carried out in online teaching contexts, with the authors coinciding on the need for further research in this area, as the results show the effectiveness of identifying emotions. Another study focusing on these objectives is that by [PERSON] et al. [15], who also use AI to analyse emotions and attention through students' facial expressions, reporting that the training of the AI system performed correctly in 76% of the situations. Other work shows similar results [16] and connect these technologies with \"smart classroom\" environments [17, 18, 19], with that by [PERSON] et al. [14] also being of interest. Meanwhile, new environments have been tested [20; 21; 22] in order to analyse their educational possibilities.  
Our research is based on the scoping review (ScR) method, with the aim of exploring the relevant literature on the use of AI technologies for identifying students' attentional state during an educational activity and to describe the experiences being developed in this field of study. To this end, the following research questions are posed:  
1. Question 1: Are AI technologies being used to analyse students' attention or concentration levels?
2. Question 2 (RQ2): What theoretical frameworks underpin the experiences analysed on the use of artificial intelligence in educational settings?
3. Question 3 (RQ3): What methodologies and types of interventions have been used in studies implementing artificial intelligence in education?
4. Question 4: What types of AI techniques and tools are used to measure the attention span of learners?