Overview
Spurring the development of interventions that can double the rate of middle school math progress for low-income students.
The Learning Engineering Virtual Institute (LEVI) aims to create interventions that will double the rate of middle school math progress for low-income students.
Working in deep collaboration across teams and disciplines, LEVI will award organizations multi-year grants. Grantees will leverage advanced computational methods to develop, scale and implement AI-based tools that help low-income students succeed.
Why Middle School Math
Student proficiency in math and literacy has declined in the past decade, with outcomes in middle school math exceptionally low in the United States. Math performance, especially among historically marginalized populations, is particularly poor. Nationally, only 26% of all eighth-grade students were proficient on the NAEP math assessment in 2022. But in Cleveland, only 2% of African American 8th graders scored proficient. Yet, math proficiency is critical to economic mobility and earning a living wage. Indeed, passing 6th grade math is one of the top predictors of high school graduation.
The Covid-19 pandemic has worsened learning outcomes. Between 2019 and 2022, the NAEP Mathematics Assessment scores declined across all race & ethnic groups and math score percentile levels among 8th graders in 51 states/jurisdictions.
Why AI
The rise of ChatGPT and other large language models underscores the potential of AI to dramatically change teaching and learning. The LEVI teams are at the forefront of this change and include approaches as diverse as:
- Developing a hybrid human-AI app that gives tutors the ‘superhuman’ power to provide students with a precise dosage of tutoring at their specific learning level;
- Customizing avatars using generative AI to increase student motivation and engagement;
- Training an award-winning chatbot that delivers math tutoring with personalized feedback and prompting student reflection.
LEVI is launching its first Applied AI in Education Lab.
This Lab will generate data, insights and knowledge to power both the LEVI teams and the field more broadly. The Applied AI in Education Lab is currently seeking input from students, parents and educators to optimize the utilization of AI in improving teaching and learning, both within and outside the traditional classroom setting.
Our Teams
University of Florida
The team is creating effective and scalable AI-augmented teachable agents using large language models (LLMs) and state-of-the-art AI technologies. These innovative teachable agents are grounded within a learning-by-teaching (LT) framework, transforming students’ roles from passive learners to proactive teachers. These teachable agents are further orchestrated with multiple learning sciences strategies to engage, motivate, and facilitate students’ math learning.
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Khan Academy
Khan Academy will personalize Khanmigo, an AI-powered, chat-based tutor and set of activities, by combining the existing chat functionality with data about learners and classrooms. The student experience will balance providing grade level practice with closing prerequisite gaps, while supporting teacher and student choice. This will give students the support they need to accelerate their rate of mastery of grade-level skills.
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Carnegie Mellon University
The team is creating a hybrid human-AI tutoring system that gives each student the necessary amount of tutoring based on their individual needs. The project builds on decades of learning science research through cutting-edge tutor training and an AI-powered app that gives tutors ‘superhuman’ power, allowing them to reach all students rapidly and effectively. Using the app, tutors can access students’ data to personalize learning in real time and provide motivational support.
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Eedi
Building upon the existing Eedi platform, the team will identify patterns in students’ mistakes to make recommendations about next steps. Using this “misconception map,” the Eedi team will infer the “best” misconception for a student to address next and recommend appropriate interventions, including:
1) Digital lessons that address latent misconceptions;
2) 1:1 human tutoring based on the student’s misconceptions analytics; and
3) Instant webinars to address misconceptions shared by a group of online students. By integrating optimized digital lessons with variable-dose human tutoring, the team will maximize intervention capacity whilst retaining scalability.
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Carnegie Learning
The team will create the first adaptive, interactive video streaming program where rock-star math teachers deliver targeted instruction to students in a fun, engaging, game-based environment.
The project will use machine learning, advanced video technology, and affect detection, allowing rock-star teachers to come alive while students complete problems, improving both motivation and academic learning outcomes.
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University of Colorado Boulder
The team aims to bring together the best of what human tutoring and AI have to offer. Drawing on the profound benefits of a human tutor, the platform recommends challenging tasks, facilitates rich discussions, fosters relationships among students and tutors, provides feedback and guidance, and promotes collaborative learning.
Using learning engineering methods, the project aims to rapidly transform tutoring from a one-on-one, human or technical solution to a multimodal, multi-party, human and computer synergy, reaching over 275,000 diverse, low-income students within 5 years.
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Rising Academy Network
Rori is a math tutor chatbot built for low-literacy, low-income students in Africa to build their foundational math skills through a ubiquitous communication channel: WhatsApp. Rori’s goal is to bring cutting-edge technology, using natural language processing, machine learning, and artificial intelligence, wrapped inside a user-friendly WhatsApp bot to the students who need it most- children without access to great schools and foundational numeracy skills.
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