This article consists of a post-analysis of twenty-four learning games, developed by master students within a dedicated course, over a period of twelve years. From this longitudinal qualitative analysis of serious game development, we are able to identify critical factors that make these games successful or not. We have grouped these factors into three axes: project organization, game mechanics and graphics. The variety of games produced in this academic context opens the way toward a more systematic analysis of this corpus, aiming at producing useful guidelines to design better serious games in an efficient manner.

The field of Interactive Digital Narrative (IDN) aims at forging a distinctive Interactive Narrative Experience (INE) by accommodating user intervention in the generation of plot events and stories. Due to the highly generative nature of IDN systems, it is challenging to validate if a narrative engine reacted according to user input providing an engaging INE. To date, a great deal of User Research methodologies employed in the field employs pre-post task questionnaires producing global accounts of the experience. Although relevant, such methods are insufficient for examining the user-system interplay that could have led users towards a certain Engagement Trajectory, or series of self-reported values of Continuation Desire sampled during runtime. In this article, we introduce a methodology for the fine-grained evaluation of the INE from the perspective of Engagement Trajectories and report the results of a study featuring user behavior pattern discovery based on Process Mining on a dataset of playtesting traces. We were able to discover and characterize 14 Engagement Trajectories encountered by 90 users of an IDN system. We consider our methodology coupled with Process Mining as a fit general-purpose technique for analyzing the process of playing in IDN.

Caring for people with Alzheimer disease can be demanding. As an attempt to help caregivers to improve their interaction with people with Alzheimer disease, we are developing a personalized simulation allowing caregivers to practice problematic situations they encounter in their daily life. The strong level of personalization and the large number of interaction choices make it challenging to design interaction mechanisms that are usable for targeted end-users. After several stages of design and evaluation, we propose an interface that supports the strong dynamic interaction during the game, it is integrated into the 3D environment and is simple to be used even by players with limited computer skills.

As interactive narratives, by definition, change according to the user’s choices (dynamic story), so do the dialogue utterances by the characters. Writing all possible utterances manually faces scaling problems. This motivates the use of natural language generation techniques. We present ExpReal, a surface realizer and templating language that allows authors of interactive narratives to write flexible and enriched templates while maintaining control over their use. Templates are automatically selected based on author-specified conditions relating to the world state (e.g. characters’ emotions) or the current task at hand. ExpReal has been developed to support at least three languages (English, French and Dutch).

In the context of assisting informal caregivers of Alzheimer Disease patients, this article presents the design and preliminary implementation of a serious game in which two agents -a user-controlled caregiver and a virtual patient- communicate via specifically-designed dialog acts, reflecting both pedagogically appropriate and inappropriate behaviors.

This paper presents a system that generates cinematic replays for dialogue-based 3D video games. The system exploits the narrative and geometric information present in these games and automatically computes camera framings and edits to build a coherent cinematic replay of the gaming session. We propose a novel importance-driven approach to cinematic replay. Rather than relying on actions performed by characters to drive the cinematography (as in idiom-based approaches), we rely on the importance of characters in the narrative. We first devise a mechanism to compute the varying importance of the characters. We then map importances of characters with different camera specifications, and propose a novel technique that (i) automatically computes camera positions satisfying given specifications, and (ii) provides smooth camera motions when transitioning between different specifications. We demonstrate the features of our system by implementing three camera behaviors (one for master shots, one for shots on the player character, and one for reverse shots). We present results obtained by interfacing our system with a full-fledged serious game (Nothing for Dinner) containing several hours of 3D animated content.

We are interested in using game technology to provide an engaging and immersive environment for experiential learning of workplace situations. Narrative intelligence will be used to provide the adventure. For authoring we provide an adaptive interface that allows the direct capture of the workplace situations and the knowledge driving the interaction. We include an initial study comparing the learning outcomes for an animated demonstration with video footage of a similar scenario.

In this paper, we describe a controlled interface for Interactive Drama, [email protected] PastMaster is used for interacting with an Interactive Drama engine. The paper discusses the test results regarding the usability of the interface.