In recent years, facial recognition technologies (FRTs) have experienced enormous growth and rapid deployment. Addressing the trade-offs among the risks and benefits of complex facial recognition technologies requires the creation of a new federal office.

This primer presents background on Facial Recognition Technologies (FRTs) and is written for a non-technical audience to increase understanding of the terminology, applications, and difficulties of evaluating this complex set of technologies.

CIRCLS features relevant primers found in the literature. We welcome new primers on similar topics, but written more specifically to address the needs of the RETTL community. Have a primer to recommend? Contact CIRCLS. Download PDF Title: Artificial Intelligence in Education: Promises and Implications for Teaching and learning Authors: Holmes, Wayne; Bialik, Maya; Fadel, Charles Abstract […]

This paper describes the impact of the degrees of realism (unrealistic, moderately realistic and highly realistic) of the pedagogical agent on student’s achievement during online learning in terms of gender.

When students learn how to use evidence-based reasoning to build knowledge that is convincing to themselves and to others, their performance on both critical thinking and academic outcomes greatly increases.

Research suggests that expert understanding is characterized by coherent mental representations featuring a high level of connectedness.

Artificial intelligence (AI) is a trend that is currently leading to controversial discussions.

Data-driven predictive models are increasingly used in education to support students, instructors, and administrators. However, there are concerns about the fairness of the predictions and uses of these algorithmic systems.

The emergence of big data in educational contexts has led to new data-driven approaches to support informed decision making and efforts to improve educational effectiveness.