Sébastien George

Imran S. A. Khan, Emmanuel G. Blanchard, Sébastien George

This paper introduces the Future Atmospheric Conditions Training System (FACTS), a novel platform that advances climate resilience education through place-based, adaptive learning experiences. FACTS combines real-time atmospheric data collected by IoT sensors with curated resources from a Knowledge Base to dynamically generate localized learning challenges. Learner responses are analyzed by a Generative AI powered server, which delivers personalized feedback and adaptive support. Results from a user evaluation indicate that participants found the system both easy to use and effective for building knowledge related to climate resilience. These findings suggest that integrating IoT and Generative AI into atmospherically adaptive learning technologies holds significant promise for enhancing educational engagement and fostering climate awareness.

Aous Karoui, Iza Marfisi-Schottman, Sébastien George

Mobile Learning Games (MLGs) show great potential for increasing engagement, creativity and authentic learning. Yet, despite their great potential for education, the use of MLGs by teachers, remains limited. This is partly due to the fact that MLGs are often designed to match a specific learning context, and thus cannot be reusable for other contexts. Therefore, researchers have recently designed various types of MLG authoring tools. However, these authoring tools are not always adapted to non-computer-scientists or non-game-designers. Hence, we propose in this paper to focus on five existing MLG authoring tools, in order to assess their features and usability with the help of five teachers, who are used to organizing educational field trips. In the second part of this paper, we present an approach for designing a MLG authoring tool, based on the lacks identified through the analysis, and tailored to the teachers' different profiles and needs.

Iza Marfisi-Schottman, Sébastien George, Franck Tarpin-Bernard

Learning Games (LGs) are educational environments based on a playful approach to learning. Their use has proven to be promising in many domains, but is at present restricted by the time consuming and costly nature of the developing process. In this paper, we propose a set of quality indicators that can help the conception team to evaluate the quality of their LG during the designing process, and before it is developed. By doing so, the designers can identify and repair problems in the early phases of the conception and therefore reduce the alteration phases, that occur after testing the LG's prototype. These quality indicators have been validated by 6 LG experts that used them to assess the quality of 24 LGs in the process of being designed. They have also proven to be useful as design guidelines for novice LG designers.