At Risk Index is IBL’s exclusive descriptive and predictive algorithm for determining what students are at risk of failing or becoming disengaged. IBL’s AI measures engagement and performance data, regarding time invested, completion of activities, courses, and programs, along with grading, credentials, submissions, assignments, quizzes, tests, and skills acquired. Engagement and performance algorithmic data merge into the At Risk Index to generate a continuous score via descriptive, diagnostic, predictive, and prescriptive models.