Materializing the abstract: Understanding AI by game jamming

Abstract

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In this article, we argue that game jam formats are uniquely suited to engage participants in learning about artificial intelligence (AI) as a design material because of four factors which are characteristic of game jams: 1) Game jams provide an opportunity for hands-on, interactive prototyping, 2) Game jams encourage playful participation, 3) Game jams encourage creative combinations of AI and game development, and 4) Game jams offer understandable goals and evaluation metrics for AI. We support the argument with an interview study conducted with three AI experts who had all organized game jams with a focus on using AI in game development. Based on a thematic analysis of the expert interviews and a theoretical background of Schön's work on educating the reflective practitioner, we identified the four abovementioned factors as well as four recommendations for structuring and planning an AI-focused game jam: 1) Aligning repertoires, 2) Supporting playful participation, 3) Supporting ideation, and 4) Facilitating evaluation and reflection. Our contribution is motivated by the recent discourse on general challenges and recommendations of teaching AI identified by related literature, here under the long and intertwined history of games and AI in general. The article presents an initial discussion of the value of game jam formats for learning about AI and which factors need to be considered in regard to this specific learning goal.

Keywords

• game jams
• knowledge acquisition
• learning
• AI
• ML