For the last 50 years, intelligent tutoring systems have been developed with the aim to supporting one of the most successful educational forms - individual teaching. Recent research has shown that emotions can influence human behavior and learning abilities, as a result developers of tutoring systems have also started to follow these ideas by creating affective tutoring systems. However, adaptation skills of the mentioned type of systems are still imperfect. The paper presents an analysis of emotion recognition methods used in existing systems to enhance ongoing research on the improvement of tutoring adaptation. Regardless of the method chosen, the achievement of accurate emotion recognition requires collecting ground-truth data. To provide ground-truth data for emotional states, the authors have implemented a self-assessment method based on Self-Assessment Manikin. (C) 2017 The Authors. Published by Elsevier B.V.