AI-driven educational technologies (AI-EdTech) process extensive data, raising concerns about commercial exploitation of children’s data and risks to their privacy, wellbeing, agency, and legal rights. The ‘fairness principle’ in data protection law requires fair data processing that meets children’s expectations and avoids unexpected, detrimental, discriminatory, or misleading practices. However, children’s own perspectives on what fairness means in AI-EdTech are underexplored in design. This study bridges the gap between law and design research to contextualize what fairness means through co-design workshops with 72 children (aged 10–12) and 4 teachers (N=76) in Scotland and Türkiye. We examine how children's perspectives can inform the operationalization of ‘fairness by design’ for AI-EdTech. Our contributions include: (1) an understanding of children’s perspectives on how fairness manifests (or does not) in AI-EdTech and (2) recommendations for both design and legal communities to align AI-EdTech design and data practices with children's values and rights.
https://dl.acm.org/doi/10.1145/3706598.3714402
The ACM CHI Conference on Human Factors in Computing Systems (https://chi2025.acm.org/)