A glossary of AI and emerging technology terms last updated October 11, 2021.
Ben Shneiderman discusses “Responsible AI,” a Viewpoint column in the August 2021 CACM.
Recruitment is a key challenge for researchers conducting any large school-based study. Random assignment studies, however, can pose greater challenges than others because of the degree of control that researchers often require.
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 report describes the current state of the art in artificial intelligence (AI) and its potential impact for learning, teaching, and education.
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.
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.
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.