Artificial Intelligence (AI) is the science behind the development of computer systems capable of performing tasks that normally require human intelligence, i.e., visual perception, speech recognition, decision-making and translation between languages. AI is also a computer simulation of human intelligence. It’s about building machines that can imitate human behavior—and AI is getting smarter every year.
Artificial Intelligence also powers Amazon’s recommendations engine, Google Translate, and Siri, and it has a proven ability to discern human patterns and help predict, improve or influence a particular outcome. With regard to K-12 environments, AI embedded within the curriculum has been getting all the headlines, but what about the use of AI technology for the purpose of helping educators better understand their students?
One company today is helping educators identify at-risk students—before behaviors start negatively impacting their or classmates’ learning. School districts contain a wealth of student-related data that if properly mined and correlated, could quickly unlock behavioral trends or patterns that are signals to adverse conduct. And the faster educators are able to identify harmful trends, the quicker they are able to administer corrective actions.
Grades and attendance records are one of the primary sources used to spot a downward trend in a student’s behavior and performance. However, these data sources are often not grouped and checked on a weekly basis, so being late every Thursday morning or leaving early on a particular Friday of every month is not an obvious signal to unlock the reason behind classroom outbursts. Applying AI techniques to correlate all of a student’s relative data sets such as teacher-to-parent emails/texts, progress notes, visits to the principal’s office, etc., will not only identify patterns but also help to unlock causes.
Educators have many responsibilities—per student—to contend with. Applying AI techniques helps measure a student’s behavior without human intervention, to quickly determine if something is trending outside the standard deviation of the child. However, it’s important to note that AI’s determination of an at-risk student is still only one tool, and teacher and guidance counselor interventions are still needed to take a deeper look into a particular student’s actions.
In addition to pinpointing at-risk students out of the flock, AI data is also providing school districts with vital information to keep federal funding in place. There is often a discrepancy between how at-risk students are funded and how much additional money schools may receive. The 34 CFR 300.226(d) mandate requires that each Local Educational Agency (LEA) using Coordinated Early Intervening Services (CEIS) funds annually report to the state on the number of children served through CEIS. AI technology can provide a vital link between abstract data sources and trending patterns that substantiate the need for every penny of funding required for positive intervention through additional programs and service funding.
Today’s educators are held responsible for identifying more childhood patterns than their predecessors have ever had to contend with. The sheer volume of paperwork, training, and regulations sometimes obscures the early warning signs of risky behavior. Applying AI to horizontally cut through the isolated data silos can make this analytical process at a true teacher’s pet.