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.

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<p class="MsoNormal" style="text-align: left; margin: 0cm 0cm 0pt; layout-grid-mode: char;" align="left"><span class="text"><span style="font-family: ";Arial";,";sans-serif";; font-size: 9pt;">We propose a new metric to automatically evaluate the confidence that a student knows a certain concept included in his or her conceptual model. The conceptual model is defined as a simplified representation of the concepts and relationships among them that a student keeps in his or her mind about an area of knowledge. Each area of knowledge comprises several topics and each topic several concepts. Each concept can be identified by a term that the students should use. A concept can belong to one topic or to several topics. Terms are automatically extracted from the answers provided to an automatic and adaptive free-text scoring system using Machine Learning techniques. In fact, the conceptual model is fully generated from the answers provided by the students to this system. In the paper, the automatic procedure that makes it possible is reviewed in detail. Finally, concept maps are used to graphically display the conceptual model to teachers and students. In this way, they can instantly see which concepts have already been assimilated and which ones should still be reviewed.</span></span><span style="font-family: ";Arial";,";sans-serif";; font-size: 9pt;"></span></p>

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In the last decades, there has been a great evolution in the field of Conversational Agents. Currently, there are agents to assist the navigation in Web pages, support elder users when interacting with some computer application to remind them which medicines they should take during the day, or to enhance the learning process by allowing students to review with systems that adapt themselves to their previous knowledge and rhythm of study. In this chapter, the goal is to provide a summary of the future trends that can be envisaged for the future of the field. It is our insight that the future of Conversational Agents are to become pervasive and natural in our daily lives.

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