This paper traces a history of platforms targeting no-code and low-code audiences. It connects historical moments addressing accessibility challenges related to computer programming up through the more recent adoption of generative AI in game engines. Across these moments, this paper identifies communities that claimed authoring platforms, establishing their identity by rejecting other, potentially more efficient or expressive options. We argue these community dynamics have shaped the evolution of current game development platforms. By contextualizing current efforts in pedagogy as part of larger shifts in computer programming and game engine practice, we present a better understanding of the origins of platforms targeting no and low code audiences as rooted in earlier pivots in computer programming and community engagement. These “twisty” and often queer adaptations aimed at smaller communities have led to major changes in how game development has evolved for larger audiences. We close on considerations of the uncertain futures and pedagogical implications of AI-generated code and visual scripting, as game engines increasingly serve as the primary interface between creators and their work.

Many contemporary interactive digital narratives consist of cinematic content that combines performance, sound and dialogue with conventional patterns of player interactions that dictate the story path. The non-linear nature of the content makes applying methods of annotation and analysis currently used in text and film challenging, as any particular experience may differ in significant ways and a player's response may occur at different times in the experience while still applying to a particular event. As a result, most studies approach analyzing player experience using either qualitative methods or quantitative methods alone, and often these studies focus on experimental systems. We developed a coding scheme that captures opportunities and decisions and provides hooks for annotating story content and player affective response. We plan to annotate recorded player traversals of The Wolf Among Us, a choice-based adventure game, incorporating a non-verbal report method, the Sensual Evaluation Instrument [6], guided by a text-based intermediate format that models the game logic. We use David Elson's Story Intention Graph to annotate the story content [2]. The proposed study will be evaluated by whether the responses capture salient story content between different traversals effectively. We describe a proposed baseline method of manually identifying shared story segments and tagging emotional content of scenes.