This book presents varied applications of artificial intelligence (AI) in test development, including research and successful examples of using AI technology in automated item generation, automated test assembly, automated scoring, and computerized adaptive testing.

This study investigated effort‐moderated (E‐M) scoring, in which item responses classified as rapid guesses are identified and excluded from scoring, and its affect on score distortion from disengaged test taking.

This guide explains the analyses, statistics, terms, and data included in the NWEA state dashboard, Exploring the Educational Impacts of COVID-19.

This paper describes a method for identifying partial engagement and provides validation evidence to support its use and interpretation. When test events indicate the presence of partial engagement, effort-moderated scores should be interpreted cautiously.

The more frequent collection of response time data is leading to an increased need for an understanding of how such data can be included in measurement models. Models for response time have been advanced, but relatively limited large-scale empirical investigations have been conducted. We take advantage of a large data set from the adaptive NWEA MAP Growth Reading Assessment to shed light on emergent features of response time behavior.

This study evaluates the effects of asking items throughout the passage (i.e., embedding items) to achieve a more precise measure of reading comprehension by removing barriers for students to demonstrate their understanding. Results showed a significant impact of embedding comprehension items within reading passages on the measurement of student achievement in comparison to answering items at the end of the passage.

This study compared the test taking disengagement of students taking a remotely administered an adaptive interim assessment in spring 2020 with their disengagement on the assessment administered in-school during fall 2019.