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Argument Graph Supported Multi-Level Approach for Argumentative Writing Assistance


Aug 01, 2023


Jul 31, 2026
Funding Agency
National Science Foundation

Proficiency in argumentative writing contributes to one’s academic and professional success. However, the Nation’s Report Card shows that most adolescents are not skilled in argumentation and frequently experience difficulty when comprehending arguments and constructing well-rounded essays. Traditional pedagogical approaches for argumentative writing often require students to practice writing a whole essay before receiving feedback, missing deliberate practice opportunities on each difficulty factor that students experience. On the other hand, while formative and personalized feedback is useful in improving students’ logical writing skills, it requires substantive efforts by instructors, and causes delays in feedback.


The overarching goal for this project is to improve students’ essay writing skills by designing a novel intelligent argumentative writing assistance system, ArguAble, empowered by novel NLP and machine learning methods. Students with varying levels of writing proficiency will use ArguAble in two learning modes: (1) learning with examples, and (2) practice and get feedback, each containing practice opportunities and actionable feedback targeting different argumentation skills. 

Primary Investigator(s)

Gertrude Buck Collegiate Professor Emerita, Marsal Family School of Education; Professor Emerita of English Language and Literature, College of Literature, Science, and the Arts; Arthur F. Thurnau Professor

More Awards

Award Start Date
Sep 15, 2018
Award End Date
Aug 31, 2024
Award Start Date
Aug 01, 2018
Award End Date
Jul 31, 2024