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Learning how to program in Primary Education has attracted significant research in recent years. It is unclear though how programming environments and languages should be adapted to children to achieve better learning and use, but one trend seems to be the use of Scratch. The question in this paper is what programming environment can be used to continue teaching programming to children who have already been taught Scratch for years. This paper's proposal is that students aged between 10 and 12 can benefit from interacting with a friendly learning companion using p-code such as Alcody. The hypothesis is that students (aged between 10 and 12) with a knowledge of Scratch will be able to significantly improve their scores by using a learning companion to teach them how to program even during the COVID-19 pandemic. To check the hypothesis, an experiment was carried out during the 2019/2020 academic year with 137 students in Ecuador. A significant improvement in the scores of the students was recorded together with high satisfaction.

Pedagogic Conversational Agents (PCAs) can be defined as autonomous characters that cohabit learning environments with students to create rich learning interactions. Currently, there are many agents reported in the literature of this fast-evolving field. In this paper, several designs of PCAs used as instructors, students, or companions are reviewed using a taxonomy to analyze the possibilities that PCAs can bring into the classrooms. Finally, a discussion as to whether this technology could become the future of education depending on the design trends identified is open for any educational technology practitioner, researcher, teacher, or manager involved in 21st century education.

The automatic assessment of students' free-text answers has recently received much attention, due to the necessity of exploring and taking advantage of new and more complex computer-based assessment methods. In this paper, a review of the state-of-art of the field is presented, focusing on the techniques that underpin these systems and their evaluation metrics. Although there is still a long way to go so as to reach the ideal system, the fact that the existing systems are already being used commercially and as a second opinion in exams such as GMAT proves the uptake of this field.

A pedagogic conversational agent (PCA) can be defined as a computer system that interacts with the student in natural language assuming the role of the instructor, a student or a companion. It can have a personality and can generate different sentences according to the agent or the student mood. Empathy with the students' feelings seems to increase their motivation to study. However, the influence of the agent personality and role as well as the students' opinion is still unclear. Therefore, in this article, it is explored with the help of a field experiment, for the first time, how these factors can affect the interaction of children with PCAs, and their opinions according to an anonymous and voluntary opinion questionnaire and some personal interviews.