Rania Hodhod

Abstract. This paper introduces an adaptive narrative-based learning environment (AEINS) which supports teaching in the domain of ethics and citizenship [1]. AEINS is an inquiry-based teaching system that adopts educational theories and classroom strategies, such as the Socratic Method. AEINS targets students aged 8 to 11 years.

Abstract. Student modeling has proven its importance over the years. It allows intelligent tutoring systems to offer adaptive teaching which has proven its usefulness in the educational process. Ill-defined problems are characterized by the ambiguity in classifying a problem's answer as correct or incorrect, in addition to the lack of defined rules that helps in transferring the solution path from the initial step to the final step. These properties make it very difficult to use intelligent tutoring to teach in an ill-defined domain.

This paper describes the design of an adaptive intelligent augmented reality serious game which aims to foster problem solving skills in young learners. Studies show that our students lack computational thinking skills in high school, which raises the need to establish new methods to develop these skills in our younger learners. We believe that problem solving skills are the fundamental skills of computational thinking and are critical for STEM, in addition to a broad range of other fields. Therefore we decided to focus on those meta-cognitive skills acquired to foster problem solving, such as strategic knowledge. The game described in this paper provides a unique adaptive learning environment that aims to develop learners’ meta-cognitive skills by utilizing augmented reality technology, believable pedagogical agents and intelligent tutoring modules. It offers a great user experience and entertainment which we hope will encourage learners to invest more time in the learning process. This paper describes the architecture and design of the game from the viewpoint of educational pedagogies and frameworks for serious game design.

Procedural game generation is the automatic creation of all aspects of a playable computer game. Procedural game generation systems require specialized knowledge, virtual worlds, and art assets. In this paper, we show how 3D graphical scenes for interactive fictions can be automatically generated with only knowledge that is readily available in existing knowledge bases or can be acquired via crowdsourcing. The key to 3D scene generation is common-ly accepted spatial relationships between different types of objects in different types of scenes. We use a crowdsourcing game to automatically and rapidly acquire spatial relations. The spatial relations are used by an intelligent scene genera-tion system that selects and configures 3D assets within a virtual geometric space.

Conceptual blending (CB) is a basic mental operation that plays a fundamental role in the construction of meaning in our everyday life. The core of CB is the partial matching of two input mental spaces and the selective projection from those inputs into a novel 'blended' mental space, which then dynamically develops an emergent structure. Improvisational acting is one specialized domain in which conceptual blending is heavily used; improvisers are required to co-create stories on the stage in real time based on how they continuously perceive their environment. The Digital Improv Project has been engaged in a multi-year study of the cognitive processes involved in improvisational acting and has led to a better understanding of human cognition and creativity. In this article, we provide a computational model for the conceptual blending of cognitive scripts that can help digital improv agents to select the two input spaces required in the blending process. The blend is an emergent structure that provides new interesting events that the digital improv agents can adopt in their acting.