The greater the proliferation of AI in educational contexts, the more important it becomes to
ensure that AI adheres to the equity and inclusion values of an educational system or …

Differential privacy (DP) data synthesizers support public release of sensitive information,
offering theoretical guarantees for privacy but limited evidence of utility in practical settings …

Algorithms deployed in education can shape the learning experience and success of a
student. It is therefore important to understand whether and how such algorithms might …

With the increasing adoption of large language models (LLMs) in education, concerns about
inherent biases in these models have gained prominence. We evaluate LLMs for bias in the …

Differential privacy (DP) data synthesizers are increasingly proposed to afford public release
of sensitive information, offering theoretical guarantees for privacy (and, in some cases …

This paper examines racial bias and discriminations in machine learning algorithms using
America's longitudinal high school students dataset. This study reveals machine learning …

Mit der zunehmenden Digitalisierung des Lernens werden immer mehr Daten von
Lernenden analysiert. Neben zahlreichen Vorteilen ist dabei nicht zu vergessen, dass …