## 1 Introduction  
In the 2030 Agenda for Sustainable Development [1], the United Nations proposed a decalogue of objectives in order to evaluate the degree of approximation to them in the year 2030 [1]. In order to achieve the first two objectives, the end of poverty and the end of hunger (the most important ones), another eight have been developed. The fourth one, ODS 4, addresses the problem of improving quality of education, which allows socio-economic mobility and is key to getting out of poverty. According to the United Nations [2], in 2018, more than 250 million children were out of school, representing 20% of the world population in this age range. In 2020, the arrival of the coronavirus pandemic made the situation much worse since, as the COVID-19 pandemic spread across the globe, a majority of countries made the decision to temporarily close of schools, highly impacting more than 91 percent of students worldwide. \"By April 2020, almost 1.6 billion children and youth were out of school.\" [2]. In this context, more than ever, the application of educational methodologies that allow students to work at home as autonomously as possible became essential. However, this was an idea that was not new. [PERSON] had already begun to work in 1982 on the idea of learning with the help of computers and without the supervision of teachers [3]. The idea was to bring quality training closer to environments where there was a lack of good teachers who could meet the training needs of young people in the region. Since then, experiences of this type have multiplied, and most of them have collided with the question of how to adapt evaluation to distant learning [4, 5, 6]. The main problem is that novel methodologies and didactics to improve the quality of distant learning can be proposed, but if the evaluation systems are the traditional ones, the innovative pedagogies collide with the reality of not having an adequate and adapted assessment methodology. Understanding evaluation as a component of classroom teaching research as well as a resource for the orientation of students, several studies have been developed to adapt the assessment process to the new distant learning methodologies [7, 8]. Regarding the background of similar initiatives, predicting the final grade of students can be useful to correct wrong learning strategies. An example of a study about this is [9], which also uses data of this study to apply supervised machine learning algorithms. In addition, [10] shows that good predictors in soft skills can be found in bad attitudes or strategies. As mentioned above, the prediction of grades has the potential of improving educational outcomes. Reference [11] provides a review of articles that tackle that aspect. In addition, there are already some studies which predict academic grades and dropout using classification [12, 13] and regression [14, 15] in higher education and high school [16]. Finally, other studies apply clustering to predict students' scores [17]. From the point of view of the student, some studies were done to measure student satisfaction during the pandemic. Results show that first-year students were more satisfied with the teaching strategies [18]. Some studies have predicted students' satisfaction using machine learning techniques [19]. These results suggest the need for both quality and quantity of modified assessments accommodated for emergency remote learning (ERL). In this article, we propose the use of artificial intelligence (AI) to help teachers to evaluate students' performance automatically in a context where most of the learning process is conducted over a distance. Our methodology is based on data extracted from adaptive tests used by students in their self-regulated learning and uses AI to classify students that are at risk of failure.