Grant Open

CoGrader 2.0: Accelerating Student Writing Proficiency through AI-Assisted Personalized Feedback at Scale

NCER

Program: From Seedlings to Scale Grants Program

Award amount: $460,190

Principal investigator: Gil Flores

Awardee:

Modern Learner Media, LLC

Year: 2025

Award period: 1 year (09/30/2025 - 09/29/2026)

Project type:

Other

Award number: R305J250071

Purpose

CoGrader 2.0 aims to dramatically improve two critical student education outcomes: writing proficiency and overall academic achievement through more frequent, high-quality feedback. Our project addresses the pressing need for more effective writing instruction and assessment in U.S. PreK-12 education, with a focus on middle and high school students. The problem we're solving is the bottleneck created by time-consuming grading, which prevents teachers from providing timely, personalized feedback to students on their writing.

CoGrader 2.0's AI-powered platform puts advanced grading technology in the hands of teachers, helping them save up to 80% of time spent grading and drafting feedback. CoGrader 2.0 enables educators to provide personalized feedback and grades to students more efficiently, dramatically shortening the overall feedback loop on written assignments. The significance of this R&D lies in its potential to transform writing instruction in U.S. schools, increase the frequency and quality of writing assignments, improve student access to high-quality feedback on writing assignments, and ultimately improve students' writing skills and academic performance at scale.

Project Activities

CoGrader 2.0 will execute a comprehensive SEER compliant research and development strategy to refine its AI-assisted grading solution. The project's Design Based Research will begin by conducting in-depth interviews with 30 teachers and 10 principals across various subjects and grade levels, while surveying 100 students to gain multifaceted insights into grading challenges and feedback needs. Concurrently, the project team will analyze data from our 1,500 monthly active users to identify critical pain points and grading patterns.

The team will iteratively develop and enhance our AI grading algorithms, focusing on improving accuracy across subjects, personalizing feedback, and expanding support for English Learners and students with special needs. They will design and test new user interfaces with 30 diverse teachers, streamlining the grading process and exploring features such as data dashboards and LMS integrations.

To assess market fit, the project team will conduct A/B testing of new features, analyze usage data for adoption rates and user satisfaction, and perform a comprehensive competitor analysis. They will engage with school administrators to understand institutional needs and develop a segmented market sizing model.

In preparation for scaling, the team will create a detailed plan addressing technical infrastructure, customer support, and strategies for rapid district-wide adoption. They will identify potential partnerships with EdTech providers and curriculum developers, and draft policy briefs on the importance of AI-assisted grading.

Key outcomes

The project will culminate in a comprehensive Proof-of-Concept Report, an advanced prototype for demonstration, and a detailed R&D Plan for Phase Two. This strategic approach will position CoGrader 2.0 to transform writing instruction and improve student outcomes at scale.