Department of Mathematics and Computer Science

Dark patterns of user interface design are deceptive design strategies that seek to nudge people's behaviour in a directions that might not align with their intention. In the case of online privacy, this could mean unwillingly share their data.

The “Cookie Monster” game is an existing prototype designed to provide playful engagement with common dark patterns. Across ten levels, players are confronted with different manipulative interface designs, and their task is to identify the option that best protects their personal data. This game primarily lets its users experience the mechanisms behind dark patterns, yet it lacks an educational component that helps players gain a deeper understanding of these design patterns.

Educational approaches combining playfulness with critical reflection can help users develop awareness and resilience against the manipulative designs they encounter in their daily lives.

The aim of this bachelor's thesis is to further develop the “Cookie Monster” game into an educational tool that not only exposes players to dark patterns but also actively supports learning about them. The project combines game design, web development, and educational research to create a meaningful and accessible learning experience.

The objectives of this thesis are the following:

• Re-implement the existing game using a flexible, maintainable web programming framework (e.g., React)
• Design and integrate educational elements (e.g., explanation for encountered dark patterns) that provide players with suitable information about the specific dark patterns encountered.
• Evaluate the game in a small qualitative user study.

The current version of the game can be accessed here:
https://davidleimst.github.io/cookie_monster_educational_game/


References:

- René Schäfer, Sarah Sahabi, Annabell Brocker, and Jan Borchers. 2024. Growing Up With Dark Patterns: How Children Perceive Malicious User Interface Designs. In Proceedings of the 13th Nordic Conference on Human-Computer Interaction (NordiCHI '24). Association for Computing Machinery, New York, NY, USA, Article 25, 1–17. https://doi.org/10.1145/3679318.3685358

- Thomas Mildner, Merle Freye, Gian-Luca Savino, Philip R. Doyle, Benjamin R. Cowan, and Rainer Malaka. 2023. Defending Against the Dark Arts: Recognising Dark Patterns in Social Media. In Proceedings of the 2023 ACM Designing Interactive Systems Conference (DIS '23). Association for Computing Machinery, New York, NY, USA, 2362–2374. https://doi.org/10.1145/3563657.3595964