PFI-TT: Using Artificial Intelligence (AI) and Natural Language Processing (NLP) to Enhance Writing for Publication

The broader impact/commercial potential of this Partnerships for Innovation - Technology Translation (PFI-TT) project is greater access for novice research writers (e.g., early-career researchers and students) to high-level, responsive training in scientific writing and, as a result, a more diverse, equitable and inclusive playing field for acceptance of grant applications and publication of peer-reviewed journal articles. Research institutions often struggle to provide meaningful support for their novice writers.

Such support typically requires prohibitively expensive human resources to provide training on the technical expectations of scientific writing and, crucially, to evaluate and provide feedback on writing. However, research institutions frequently have open budgets for new educational technologies that address major pain points within their community, creating a strong product-market fit for a software that includes a linguistically-grounded instructional curriculum for scientific writing.

The proposed technology is trained to recognize and provide argument-level feedback. As such, the technology is expected to increase the capacity to advance writing, offset training burden, and enhance access to resources for underrepresented populations, all in one tool, by providing responsive, on-demand instruction and feedback on writing for publication.

The project addresses two main research opportunities: the implementation of advanced automated writing feedback and the use of neural networks for text classification and feedback generation. To date, existing writing support tools that provide automated feedback address only surface-level grammar and mechanics. From a theoretical perspective, neural networks are underexplored for text classification and feedback generation, especially for scholarly writing.

This project is expected to advance knowledge in two primary ways: (1) by demonstrating how automated feedback can be generated beyond feedback on surface-level grammar and mechanics through a focus on language specific to scientific argumentation and description and (2) by highlighting how pattern markup and a deep neural network approach to machine learning can be used to produce feedback specific to peer-reviewed journal articles and grant applications. These aims will be achieved by developing an automated writing feedback engine that will be integrated into the technology and then validated using an argument-based framework to evaluate and enhance the engine’s accuracy and usability.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.