Publication
Train the Spire: An ML-Driven Single Player GWAP for Image Annotation
Keno Nanninga; Abdulrahman Mohamed Selim; Sara-Jane Bittner; Pascal Lessel; Michael Barz; Daniel Sonntag
In: Proceedings of the 2026 Symposium on Eye Tracking Research and Applications. Symposium on Eye Tracking Research & Applications (ETRA), Marrakesh, Morocco, Pages 1-5, No. 55, ISBN 979-8-4007-2519-7, Association for Computing Machinery (ACM), New York, NY, USA, 6/2026.
Abstract
Training image classification models requires large labelled datasets, which is particularly challenging in specialised domains where manual expert annotation remains the default, such as eye tracking. To address this challenge, we present Train the Spire, a single-player game with a purpose (GWAP) that embeds image annotation within turn-based card game mechanics for crowdsourcing data annotations. The system uses a few-shot deep learning classifier to validate player-generated labels, providing immediate feedback through rewards and penalties. The game incorporates elements, such as progression systems, a companion agent for system transparency, and balanced difficulty, to maintain player engagement while ensuring annotation accuracy. In this paper, we present the system design, implementation details, and evaluation study design for comparing Train the Spire against a baseline annotation tool using the VISUS mobile eye-tracking dataset in an online user study measuring effectiveness, usability, and enjoyment.