From chatbots to dashboards to avatars, LEVI teams are using learning engineering to improve middle
school math outcomes for low-income students.

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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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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