During ages, new and innovative learning technologies are often criticized or rejected, while their full acceptance is commonly delayed. As a result, the progress of Smart Learning Environments is noticed nowadays to be delayed, while educator debate about the technology used in classroom effectiveness. Author’s objective is to explore potential factors in order to render modern communication devices such as mobile phones and tablets suitable for learning in schools, taking into consideration possible advantages or disadvantages. In the case that students use mobile devices during learning procedure, a shortage of suitable content as well as adequate integration of educational and edutainment systems employing gamification techniques within the school framework. These factors are considered to be sufficient enough to strengthen and improve learning experience and effectiveness.

“Serious games” refer to games that go beyond pure entertainment and promote learning. They are utilized within a variety of learning environments enabling students to acquire knowledge and skills, while they offer wide benefits. The authors' team measured and analyzed various factors related to the gameplay and educational content when 2D and 3D serious games are introduced in the educational process. The main objective focused on the correlation of the University students' views that were sharing common characteristics, like gender, information and communication technology skills, game playing experience, and specific scientific background with factors that related to the gameplay as well as the learning effectiveness. The results revealed that game-playing experience had a more positive impact in the case of males, while perceived learning effectiveness of 2D was higher compared to the 3D serious game for both genders. Moreover, there are differentiations among females concerning the scientific background, Information and Communication Technology skills and game-playing experience.

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The fusion of Artificial Neural Networks and Fuzzy Logic Systems allows researchers to model real world problems through the development of intelligent and adaptive systems. Artificial Neural networks are able to adapt and learn by adjusting the interconnections between layers while fuzzy logic inference systems provide a computing framework based on the concept of fuzzy set theory, fuzzy if-then rules, and fuzzy reasoning. The combined use of those adaptive structures is known as “Neuro-Fuzzy” systems. In this chapter, the basic elements of both approaches are analyzed while neuro-fuzzy networks learning algorithms are presented. Here, we combine the use of neuro-fuzzy algorithms with multimedia-based signals for training. Ultimately this process may be employed for automatic identification of patterns introduced in medical applications and more specifically for analysis of content produced by brain imaging processes.