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One-Size-Fits-One: How AI-Driven Personalized Learning Unlocks Student Potential 

Vienna Parnell | February 5, 2023

In an instrumented classroom of the Enyedy Lab  in the Vanderbilt Sony Building, young students act out the states of matter, in this case by moving closer together to create a solid sheet of ice that is shown by their positions on the screen. (Enyedy lab)

Many college students are familiar with the experience of feeling detached from their professors during class, especially in lectures filled with upwards of hundreds of students. Even in smaller seminars with one-on-one interactions with the instructor, students with different learning styles may find the fixed pedagogies challenging. However, personalized learning can play a role in alleviating this problem.

Problems of an uniform education system

Teaching that fails to account for the inherent range in background, extracurricular and familial commitments, and academic strengths and weaknesses among the student body leaves significant room for improvement. Problematically, an education system that is uniform runs the risk of systematically under-resourcing marginalized groups, perpetuating a cycle of drop-outs, unemployment, and poverty. The traditional “one-size-fits-all” school model limits all students to the same lesson plan and fails to accommodate each students’ needs and interests. 

AI-driven approach to personalize learning experience

Enter personalized learning, an AI-driven approach that seeks to produce effective, inclusive methods for teaching and learning. By incorporating machine learning algorithms that analyze trends in student performance, the personalized learning experience produces a curriculum that is tailored to fit each student’s individual needs and interests. 

In recent years, several top contenders have emerged in the digital learning platform market, such as Knewton. Through their personalized learning product, Alta, Knewton monitors student progress and accuracy on assessments and suggests different courses of action based on their recorded performance. Another prominent company, Education Elements, developed their personalized learning platform, Highlight, to also allow teachers to review class performance trends and assign additional resources for individual students. 

Rather than entirely replacing teachers, AI-assisted personalized learning supports them by identifying and reporting consistent areas of improvement in student learning, allowing teachers to make educated changes to lesson plans without running behind schedule. Teachers can also continue to perform innately human tasks such as connecting with students on an emotional level during class and office hours. Meanwhile, technology would be carrying out the tedious task of collecting and analyzing student data. In other words, teachers will know that their efforts toward teaching certain concepts and connecting with students on an individual basis are well spent. Moreover, a computerized system provides insight into whether a class is appropriately paced or challenging, allowing a student to discover their full academic potential given an optimal learning environment. 

Raising awareness of personalized learning

In the wake of growing opposition against standardized testing and the call for educational reform in the U.S, several highly influential leaders in society have invested in personalized learning. The Bill & Melinda Gates Foundation, the Chan Zuckerberg Initiative, and the U.S. Education Department under the Obama Administration have collectively invested hundreds of millions of dollars toward the movement with the goal of furthering the academic development of students across the country. Many universities, including Vanderbilt, have also been making strides toward improving student learning in more interactive ways. 

In 2021, Vanderbilt University partnered with the National Science Foundation AI Institute for Engaged Learning. This five-year project seeks to address differences in learning preferences across student bodies through narrative-centered learning environments, interactive digital humans, and multimodal learning strategies. The team at Vanderbilt consists of several faculty members from disciplines spanning Peabody College, the College of Arts and Sciences, and the School of Engineering.

At Vanderbilt, Noel Enyedy, professor of science education, already applies immersive learning technology. In his lab, which investigates how students learn through interactions and communication, young students can move around in the classroom and watch their physical actions being rendered on-screen. When learning about the multifaceted interactions of honeybees, they adopt digital animal avatars and observe how their movements translate into the interactions between bees as they search for nectar. In another exercise, the students act out particles and learn about the different states of matters as a camera tracks their position in the classroom. 

Beyond teaching elementary students, adaptive teaching frameworks have larger, long-term implications on student achievement. Findings from research conducted by the RAND Corporation reveals early evidence that students who learned through personalized learning strategies surpassed national norms after two years in mathematics and reading, though more rigorous implementation across a wider range of schools is necessary to confirm this observation. 

Implementing personalized education at any scale is an ambitious endeavor that requires big data analytics due to the sheer amount of information that would be required from each student in order to train the machine learning systems. In a world that ensures that all data is handled ethically and produces helpful insight, the intersection of artificial intelligence and education yields a promising future for students, at Vanderbilt and elsewhere. 


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