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Writing is foundational to learning in multiple disciplines. Yet, students struggle to incorporate source material and write argumentative essays, while teachers often struggle to provide content-based feedback.

One solution is to employ technologies like natural language processing (NLP), a component of artificial intelligence that can understand human language — but most of these tools have fallen short.

LRDC researchers Diane Litman, a professor in the Department of Computer Science, and �鶹��ý of Education collaborators Richard Correnti, an associate professor, and Lindsay Clare Matsumura, a professor, to address the limitations of existing educational technologies for writing.

Their project, “Development of Natural Language Processing Techniques to Improve Students' Revision of Evidence Use in Argument Writing,” will develop a system that leverages NLP to provide students with formative feedback on the quality of their revisions. Their aim is to design a system that will improve students’ implementation of the feedback on text-based argument writing, leading to more successful revision and ultimately better writing. An additional goal is to build students’ and teachers’ knowledge of effective use of source texts.