Technological advances have been the main driver of enhancing human–computer interaction and interactive simulations have experienced exponential growth in recent years. However, visual and auditory channels are usually the only ones considered for educational simulations even though the sense of touch is also an important one. Touch allows us to recognize and interact with our surroundings. A common way to develop a visuo-haptic simulation in the area of interactive systems is by using a graphic and physics-based engine orchestrated with a haptic rendering framework. However, new solutions, such as professional game engines, have enabled the development of high-quality applications in much shorter time. In this paper, a novel architecture for fast development of interactive visuo-haptic applications in game engines is discussed. To validate the proposed architecture, the Haptic Device Integration for Unity (HaDIU) plugin was implemented. Simulations were implemented to verify the operability of haptic devices. Each scenario was properly modelled and has different haptic objectives. Furthermore, to validate that the usage of this approach provides better visualizations than an existing single purpose application, an experimental study was performed. Results suggest that by using this approach faster development of interactive visuo-haptic simulators can be achieved than using traditional techniques.

In this paper, the potential of visuo-haptic simulators to help engineering students to understand the nature of electric forces between different electric charge distributions is addressed. Three visuo-haptic simulators were designed to perceive the attractive–repulsive behavior as well as the dependence on distance of electrical forces for: (a) point charge, (b) line charge, and (c) plane charge. Design elements were incorporated to improve the 3D perception of the simulators. A sample of N = 111 engineering students practiced with the simulators: 87 enrolled in an Electricity and Magnetism course and 24 enrolled in a more advanced Electromagnetic Fields course. Pre-test and Post-test were applied before and after working with the simulators and average learning gains were obtained. t-tests were performed to determine the statistical significance of the results. Significant learning gains were obtained for the comprehension of the force dependence in the case of line charge and plane charge, but not for the point charge, due to the fact that most students started with very high Pre-test scores in this last case. These results suggest that the use of visuo-haptic simulators may help students to better identify the dependence of electric forces on distance. It was also observed that the potential effect of improving the recognition of electric interactions was higher among students with lower previous familiarity with these topics, as compared to more advanced students. Through exit surveys, it was found that the students liked very much the haptic activity and that it sparked their interest in learning new physical concepts.

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Virtual Laboratories (VLs) have to overcome important challenges to improve student knowledge, understanding and motivation. This research aims to test the hypothesis that, through adding features of serious games to VLs and integrating artificial intelligence (AI) techniques, an enhancement of student motivation, knowledge and understanding can be attained. This work introduces the Olympia architecture, which is based on a previous architecture that combines VLs and intelligent tutoring systems (ITSs). In addition, Olympia enables the combination of serious games with ITSs, resulting in an educational game virtual laboratory (GVL). The GVL provides affective feedback through sound, a more engaging look-and-feel and defines student actions through the game mechanics module. Olympia was tested in a case study on teaching linear momentum in an undergraduate Physics course. For the first evaluation, a VL and a GVL were implemented. The results showed that students were motivated and learned in a similar way with both the GVL and VL environments. Later, several additions were integrated in both environments: the probabilistic student model was improved, tutorial videos were added, and the feedback was refined. For the second evaluation the results suggest that using the GVL resulted in higher learning gains than using VL.

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