Achiever or explorer?

Abstract

The development of artificial intelligence, e. g., for Computer Vision, through supervised learning requires the input of large amounts of annotated or labeled data objects as training data. The creation of high-quality training data is usually done manually which can be repetitive and tiring. Gamification, the use of game elements in a non-game context, is one method to make tedious tasks more interesting. This paper proposes a multi-step process for gamifying the manual creation of training data for machine learning purposes. We choose a user-adapted approach based on the results of a preceding user study with the target group (employees of an AI software development company) which helped us to identify annotation use cases and the users' player characteristics. The resulting concept includes levels of increasing difficulty, tutorials, progress indicators and a narrative built around a robot character which at the same time is a user assistant. The implemented prototype is an extension of the company's existing annotation tool and serves as a basis for further observations.