Digital technologies, including artificial intelligence (AI) and machine learning (ML), continue to expand human capacities. In the simplest terms, (AI) refers to systems that mimic human intelligence to perform tasks and can improve themselves based on the information they collect.
There are a plethora of AI examples that we see every day, such as smartphone apps, social media feeds, advertisements, smart cars, and music streaming. But some of the newest advancements in AI have the potential to strengthen the administrative aspects of education, starting with the admissions process and predicting overall student outcomes.
Two types of AI are most useful in a school setting: rules-based or machine learning-based AI. According to experts, machine learning-based AI is a more robust approach because machines can learn and improve over time when synthesizing large amounts of information from diverse sources.
AI systems including chatbots, text editors, and learning management systems can significantly help admissions officers make more informed selection decisions.
Specifically, AI can identify traits such as analytical thinking, organizational skills, conscientiousness, and other attributes that evoke a broader picture of an applicant. AI analysis can also identify students from disadvantaged backgrounds or students who would otherwise fall short of traditional admission standards but have traits that point to success.
For example, StudentSelect.AI is an intelligence engine that delivers deep insights on applicant data that schools have already collected. This algorithm goes beyond GPA and test scores to gain deeper insights about applicants and helps schools make more informed admissions decisions.
Overall, StudentSelect.AI improves diversity and inclusion, accelerates admissions decisions, and uncovers applicants with real potential to find success as students.
While machine learning-based AI is still new and underdeveloped in academia, more schools are turning to this technology when scoring students’ written responses or analyzing complex datasets.
Natural language processing (NPL) is a subfield of AI that uses machines to understand text. Automated essay scoring uses natural language processing to grade written essays and can help educators better understand what is happening cognitively with their students.
January 2020 research showed that text features in students’ written and spoken production can predict successes in math and science domains. This information is crucial to help teachers better manage the classroom and help struggling students early on.
Researchers at Carnegie Mellon University’s Human-Computer Interaction Institute recently developed new ways to use AI through intelligent tutoring systems.
Through their methods, students and teachers can create tutors by entering problems and showing the intelligent tutoring system (ITS) how to solve them. Once learned, the computer applies the solution. If the problem is incorrect, human can fix it.
The computer then continues to build the rules, making the machine capable of applying solutions to other problems. Overall, these systems are notably more scalable than human-based tutoring, which provides students with more one-on-one support.
Lastly, AI can also help teachers intervene with at-risk students before they drop out of school.
Dropping out of school correlates with multiple factors, including poor grades, absences, low achievement, frequent transfers from school to school, and participation in extracurricular activities. Implementing AI systems is key to preventing dropouts.
In 2011, Massachusetts introduced an early warning indicator system that collected data about students starting in first grade and following them through the 12th grade. The system helps products when students would miss academic milestones without intervention.
Science-backed machines that take advantage of the latest advancements in AI and ML can help students learn faster and better. These mechanisms also help admission staff admit “best-fit” students and teachers understand their students at a deeper level.
Still, the education system needs additional investments in the research and development of new technologies to provide academia with the right tools to succeed.
