Achiever or explorer? Gamifying the creation process of training data for machine learning

Research output: Contribution to book/conference proceedings/anthology/reportConference contributionContributed



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.


Original languageEnglish
Title of host publicationMuC '20: Proceedings of Mensch und Computer 2020
EditorsBernhard Preim, Andreas Nürnberger, Christian Hansen
PublisherAssociation for Computing Machinery (ACM), New York
ISBN (print)978-1-4503-7540-5
Publication statusPublished - 6 Sept 2020

External IDs

Scopus 85091784864
Bibtex alaghbari+mitschick++2020_achiever
ORCID /0000-0001-8667-0926/work/142246666
ORCID /0000-0002-2176-876X/work/151435438