Generative Artificial Intelligence (GenAI) is currently reshaping game development practices, production pipelines, and value networks in an unprecedentedly pervasive manner with cascading consequences remaining unclear. In the last five years since GenAI’s inception, a growing body of qualitative research has explored these early transformations from different settings and demographic angles. However, these studies often contextualise and consolidate their findings weakly with related work; for research to keep up with and support stakeholders in this development, the current moment calls for a synthesis of the findings emerged thus far. Here, we address this need through a qualitative research synthesis via meta-ethnography. We followed PRISMA-S to systematically search the relevant literature from 2020-2025, including major HCI and games research databases. We then synthesised the ten eligible studies, conducting reciprocal translation and line-of-argument synthesis guided by eMERGe, informed by CASP quality appraisal. We identified nine overarching themes, provide recommendations, and contextualise our insights in wider game production trajectories. With this work, we seek to provide practitioners, researchers and policy-makers with grounded insights to guide practice, research and governance.

This paper presents a co-creative system, designed to assist users in the ideation and generation of visual and textual humour using generative models. Our system employs a 2D-spatial interface, where the user interacts with blocks in an infinite canvas, allowing for the exploration and merge of multiple ideas at the same time. We also present some of the outputs generated by user during preliminary testing.

Despite some authors stating that data-relatedness helps interpretation, glyphs are often used unrelated to the represented data. In order to automatically produce data-related glyphs, a large visual repository is required, as well as, image structure suitable for data representation. In this paper, we propose a strategy that fulfills the two requirements and allows the production of glyphs related to the data thematic (literal and metaphorical). We compare used approach with current glyph techniques and discuss the results.